News credibility labels have limited but uneven effects on news dietquality and fail to reduce misperceptions∗
Kevin Asletta , Andrew M. Guessb , Jonathan Naglera,c , Richard Bonneaua,d , and Joshua A.
Tuckera,caCenter for Social Media and Politics, New York Universityb
Department of Politics, Princeton UniversitycWilf Family Department of Politics, New York UniversitydDepartment of Biology, New York University

Revise and Resubmit at Science Advances.

Abstract
As the primary arena for viral misinformation shifts toward transnational threats such as the Covid-19 pandemic, the search continues for scalable, lasting countermeasures compatible with principles of transparency
and free expression. To advance scientific understanding and inform future interventions, we conducted arandomized field experiment evaluating the impact of source credibility labels embedded in users’ social
feeds and search results pages. By combining representative surveys (N = 3, 337) and digital trace data
(N = 946) from a subset of respondents, we provide a rare ecologically valid test of such an intervention on
both attitudes and behavior. On average across the sample, we are unable to detect changes in real-worldconsumption of news from low-quality sources after three weeks, and we can rule out even small effects onperceived accuracy of popular misinformation spread about the Black Lives Matter movement and Covid-19.
However, we present suggestive evidence of a substantively meaningful increase in news diet quality among
the heaviest consumers of misinformation in our sample. We discuss the implications of our findings forpractical questions about designing interventions to counteract online misinformation.

∗ We are extremely grateful to Craig Newmark Philanthropies for supporting this research project. The Center for Social
Media and Politics at New York University is generously supported by funding from the National Science Foundation, the John
S. and James L. Knight Foundation, the Charles Koch Foundation, the Hewlett Foundation, Craig Newmark Philanthropies,
the Siegel Family Endowment, and NYU’s Office of the Provost and Global Institute for Advanced Study. NewsGuard did
not consult with the authors on the study design or provide funding support for this research. This study has been approvedby the Princeton University Institutional Review Board (#12800) and the New York University Institutional Review Board(#FY2020-4278). Special thanks to Sam Luks at YouGov for facilitating data collection. We thank Tali Mendelberg for helpful
feedback.

The internet and social media have drastically decreased the cost of disseminating information by reducing
reliance on traditional gatekeepers. As a consequence of this openness and availability, news and information
sources have flourished from a variety of ideological and cultural perspectives. The resulting cacophony has
encouraged participation by previously underrepresented voices and enabled criticism of dominant authorities. At the same time, it has intersected with existing political divides in ways that have contributed
to pathologies in American political discourse including the spread of misinformation (Lazer et al. 2018;
Vosoughi et al. 2018; Grinberg et al. 2019; Guess et al. 2019; Osmundsen et al. ming), disagreements aboutbasic facts related to governance and policy (Flynn et al. 2017; Anspach et al. 2019; Pennycook and Rand2021), and lowered trust in established media (Guess et al. 2021). Of particular concern is the possibility
that these problems are interlinked: As political divisions widen, partisan media alienate people from authoritative sources, which could make it more difficult to counteract potentially corrosive — and in the caseof public health during a pandemic, life-threatening (Brennen et al. 2020) — misinformation.
Over the past several years, scholars, technologists and policy makers have proposed a number of solutions
intended to reduce exposure to misleading information. These range from relatively intrusive measures suchas algorithmic downranking, to subtle warnings and labels targeted at specific factual claims (Ecker et al.
2010; Clayton et al. 2019a), to general efforts to boost digital media literacy skills (Guess et al. 2020b;
Badrinathan 2020). A key challenge in these efforts is how to balance the strength of an intervention withpotential negative externalities in the form of unintended spillover effects (Pennycook et al. 2020; Nyhan
et al. 2013) or limits on individual autonomy and freedom of expression. With this tension in mind, we focus
on simple feedback in the form of informational labels designed to educate people about the quality of sourcesthat they consume and view in their search or social media feeds (Lorenz-Spreen et al. 2020). This approachbuilds on humans’ tendency to rely on cognitive shortcuts and heuristics, which depending on context canbe relatively informative (in the case of source transparency; see Gigerenzer and Selten 2002; Pennycook
and Rand 2019) or potentially distorting (in the case of popularity cues, which are a common feature ofsocial media; see Messing and Westwood 2014). In addition to being scalable relative to fact-checks, sourcelabels are relevant for a broad array of publishers across the spectrum of reliability, rather than merely those
designated as purveyors of misinformation.
In this study, we build on recent innovations for rigorously evaluating online tools (Munzert et al. 2021a,b).
In an online field experiment, we randomly encouraged participants to install a prominent web browser
extension, NewsGuard, which embeds straightforward source-level indicators of news reliability into users’
search engine results pages (SERPs), social feeds and visited URLs.1 Different “shield” symbols are placed
1 NewsGuard launched in 2018 and produces ratings based on neutral criteria evaluated by a team of journalists and editors;
more information can be found in the Materials and Methods section and at www.newsguardtech.com. Examples of how
the source ratings appear to news consumers in different settings are shown in Fig. ??. Although this study employed the1

in-feed to provide visual summaries of sources’ quality. A green shield indicates a reliable source (examples
include CNN, Fox News, and The Washington Post), a red shield indicates an unreliable source (examples
include Gateway Pundit, Epoch News, and Daily Kos), a gray shield indicates a source with user-generated
content (such as YouTube, Wikipedia, and Reddit), and a gold shield represents satire (such as The Onion,
Babylon Bee, and The Daily Mash). The user can click on the shield to see an overlay of more detailed
information about the reliability of the news domain in question.2
Figure 1: This figure displays how users are exposed to NewsGuard source labels in internet users’ search engine
results pages, social feeds and visited URLs.

Prior research investigates the ability of expert source ratings to affect the believability of claims encounNewsGuard extension, which was freely available in app stores at the time of fielding, NewsGuard did not provide any financial
support or assistance in the design of this study.
2 We show these source reliability symbols in Section G of the Supplementary Materials.

2tered online, including on health websites and social media (Kim et al. 2019; Kim and Dennis 2019). Theoretically, such ratings provide credible information to users about the “functional” dimension of a source’s
reputation, or its objective performance in a well-defined set of criteria as assessed by experts. Thoughother dimensions of reputation may not be as responsive to these judgments, experimental evidence suggeststhat such ratings, when shown alongside a mock news article, can influence the perceived truthfulness of
the article’s claim (Kim et al. 2019; Gallup 2018). NewsGuard provides a particularly comprehensive set ofsource ratings produced according to transparent criteria. Deploying these source ratings as visible labels
across a user’s search and social feeds provides an opportunity to test the effectiveness of expert ratings asa general solution to online misinformation.
Other attempts to leverage source-level information have been mixed. Though experimental demonstrations of source label effects on selective exposure are well known (Iyengar and Hahn 2009), studies have failed
to convincingly show that such cues affect susceptibility to misinformation (Clayton et al. 2019b; Dias et al.
2020). Part of the problem may be that people lack sufficient knowledge about many sources (especiallysmaller, more unreliable ones that are rarely consumed) to be able to make useful inferences based solely
on a publisher’s name or logo. Expert ratings of the kind that NewsGuard provides potentially remedy thisshortcoming by supplementing source cues with additional, independently verified information (see Gallup
2019 for survey data from a sample of NewsGuard users).
A large, related literature on source credibility examines the attributes of sources that make them morebelievable (Hovland et al. 1953; Flanagin and Metzger 2000). Researchers have sought to apply these insights
to the problem of online misinformation, though a recent meta-analysis found that source credibility-based
interventions were among the weakest of those studied for correcting misinformation (Walter and Murphy2018). The treatments considered differ in two important respects from the source reliability labels that westudy. First, they do not typically involve news publishers; “source credibility” conceptualizes messengers
broadly to encompass institutions, highly esteemed individuals, or other potential trustworthy sources of
information in society. Second, these studies specifically test the extent to which credibility can boost theefficacy of corrections to individual pieces of misinformation rather than display, as NewsGuard does, expert
ratings alongside any article (sans correction).
Existing work suggests relationships between exposure to misinformation and various well-known pathologies. Misperceptions are the most commonly studied phenomenon (Flynn et al. 2017), which can result fromdirect exposure to online misinformation (e.g., Guess et al. 2020c; Loomba et al. 2021). In this setting, however, participants are not exposed to specific misinformation in a controlled way. More generally, scholars
have suggested reactions to online “fake news” — and the epistemic chaos it engenders — in the form ofheightened cynicism toward politics overall (Lazer et al. 2018) and lower trust in traditional media (Wasser3

man and Madrid-Morales 2019; Ognyanova et al. 2020). Additionally, studies have posited that exposure to
misinformation can increase affective polarization (Lau et al. 2017; Suhay et al. 2018; Tsfati and Nir 2017)
since such hyperbolic, misleading content is often engineered to fuel disdain for the partisan outgroup. Given
these findings, we examine whether source reliability ratings can counteract these effects. This could occur
via two mechanisms. First, by reducing the likelihood that individuals click on misleading or unreliable
sources of information, these ratings would lower exposure to misinformation that can promote corrosive
attitudes toward politics and society. Second, repeated exposure to labels alongside headlines of varying
believability could induce a learning effect. Given prior evidence, it is likely that individuals will either
encounter mainly reliable sources or a mix of the two. In the former case, positive ratings could boost news
sources’ credibility in general, while the latter case creates an opportunity for improved discernment between
reliable and unreliable news sources (Kim and Dennis 2019). This also allows for a virtuous cycle in whichpeople are gradually exposed to less inflammatory content about the political outgroup.
Our main hypotheses thus test whether in-feed source reliability labels shift downstream news and information consumption from unreliable sources known for publishing misleading or false content to more reliable
sources (H1), increase trust in mainstream media3 and reliable sources (H2), and mitigate phenomena associated with democratic dysfunction (affective polarization and political cynicism)4 (H3). We also consider
three research questions for which our a priori expectations were less clear. First, past research suggeststhat certain kinds of interventions can reduce people’s beliefs in both accurate and inaccurate information(Clayton et al. 2019a; Guess et al. 2020b), so we examine whether respondents encouraged to install the
NewsGuard extension were more or less likely to believe popular false and true stories that spread during
the treatment period.5 Second, we explore whether downstream effects are observable on other outcomessuch as trust in institutions, belief that “fake news” is a problem in general, and belief that “fake news” is aproblem in the mainstream media. Third, we explore whether any of the identified effects are greater among
subgroups found in prior research to more frequently engage with online misinformation.6 Results from allof our pre-registered analyses can be found in the Supplementary Materials, Sections C, D, E, and F, G andH.
Combining panel survey data and individual-level web visit data, we find in pre-registered7 analyses
3 We also test if the effect of the treatment on trust in media is larger among individuals who have lower levels of initial trust
in media.
4 We also test if the effect of the treatment on affective polarization is larger among individuals who have higher levels ofaffective polarization.
5 We were not able to pre-register this research question because we selected the items as close to fielding as possible.
We include the results because they are more directly comparable to studies evaluating the effects of interventions designed
specifically to reduce misperceptions.
6 These groups include those who use social media sites more frequently, have low levels of digital literacy, consume morenews, and already visit more online publishers of untrustworthy news.
7 The pre-registration can be found here: REDACTED FOR BLIND REVIEW

4

that in-browser contextual source labels: (1) do not measurably shift participants’ online consumption fromunreliable sources known for publishing misleading or false content to more reliable sources; (2) fail to reduceaverage belief in widely circulated inaccurate claims; and (3) do not alter trust in the media generally. Ourestimates — especially for survey-based outcomes — are well-powered, and we can rule out even very small
Intent-to-Treat effects (Cohen’s d < 0.07). Our null findings on changes in participants’ information dietquality are somewhat noisier, though we can still rule out small effects (Cohen’s d < 0.09) according tostandard benchmarks. Our noisier estimates on behavior are themselves informative, since they reflect thewell-established reality that people with diets consisting overwhelmingly of untrustworthy news sources area relatively small subset of the population (Guess et al. 2020a). Interventions designed to improve news
quality through dynamic feedback may therefore need to focus their efforts on these individuals and tailor
the information they provide accordingly. Consistent with this — and in light of our original null findings —we undertake supplementary analyses (not preregistered) and find that the treatment does appear to improve
the average reliability score of the news consumed by participants at the lowest decile of pre-treatment online
news diet quality.

Results
To measure the effect of these source labels, we fielded a two-wave online panel survey from May 28 to June30, 2020 (Wave 1: May 28–June 9, N = 3, 862; Wave 2: June 19–June 30, N = 3, 337) that included arandomized incentive to install the NewsGuard web extension at the beginning of the first wave. Enoughrespondents were recruited that we are confident that we could detect even a small standardized effect size
(9% of a standard deviation) among respondents for whom we have behavioral data as well as the larger
survey sample. Results from our power analyses can be found in the Supplementary Materials in Section J.
Fig. 2 presents an overview of the study design. In addition to studying survey-based outcomes, we analyze
linked digital trace data to measure the quality of news consumption of a subset of our participants. We
create five distinct measures of news diet quality8 over three time periods: (i) the period before a respondent
was assigned treatment in the Wave 1 survey; (ii) the 3- to 4-week period from treatment assignment (May
28–June 9) to June 30; (iii) the nearly two-week period from July 1 to July 13. Testing the effect of thistreatment on news consumption during the third period was not a part of our original set of pre-registered
hypotheses, but rather a pre-registered research question. Using the measurement of news consumption in
the third period we leverage the exogenous disabling of NewsGuard’s free capabilities on July 19 to determine
8 We calculated the average NewsGuard reliability score for websites visited, proportion and counts of unreliable (NewsGuard
score < 60) news sites visited, and proportion and counts of reliable news sites visited.
9 On that day, the NewsGuard extension became a pay service with a monthly subscription fee of $2.99. At that point, those
who did not sign up to purchase the extension continued to encounter shield icons next to news stories, but the color no longer

5whether the behavioral effects of this intervention decay after its features are no longer available (Gerber
et al. 2011), or if the intervention has more durable effects like other novel informational nudges (Coppock
et al. 2018).
Figure 2: This figure displays a timeline of our data collection surrounding our two-wave online panel survey fromMay 28 to June 30, 2020 (Wave 1: May 28–June 9, N = 3, 862; Wave 2: June 19–June 30, N = 3, 337).

May, 2020
Wave 1Timeline

July, 2020
Wave 2

Collected digital trace data

16th

●
●
●

SurveyDataJune, 2020

●
●
●
●

28th

9th

19th

13th

30th

Pre-treatment measures

Treatment assignment

Post-treatment measures(Wave 1)

(Wave 1)

(Wave 2)Trust in media
Affective polarization
Trust in specific mainstream online news
producers
Political cynicism
Trust in fake news
Belief that fake news is a problem ingeneralBelief that fake news is a problem in themainstream media

N=
1,998

●
●
●

Trust in media
Affective polarization
Trust in specific mainstream online news
producers
Political cynicism
Trust in fake news
Belief that fake news is a problem ingeneralBelief that fake news is a problem in themainstream media
Perceived veracity on misinformation andtrue news●●●
Control(no encouragement)

N=
1,339

●
●

●

Digital
Trace
Data

Online news browsing behavior:
Proportion and count of news viewed
from unreliable and reliable newssites
○ Average NewsGuard rating for online
news viewed (0-100)●

1st treatment compliance measure○●●

2nd treatment compliance measure
Online news browsing behavior:
Proportion and count of news viewed
from unreliable and reliable news sites○ Average NewsGuard rating for online
news viewed (0-100)○

In this section we primarily report covariate-adjusted estimates of an Intent-to-Treat (ITT) effect measuring differences between the control and treatment groups on outcomes of interest. In the Supplementary
Materials (Section C) we report covariate-adjusted estimates of Complier Average Causal Effects (CACE),
the effect among those who installed the browser extension as a result of the randomized treatment. To
verify treatment compliance, we developed an automated script linked in the survey that measured whether
participants in the treatment and control groups had installed and activated the NewsGuard extension on
their web browsers twice: directly after the treatment was assigned in Wave 1 and in the last week of thetreatment period. 95% of respondents in the treatment group passed the first compliance check and 80%
passed both the first and second compliance check. For most demographic characteristics we find no statistically significant evidence that respondents who comply are very much different than those who did notcomply; moreover, for demographic characteristics where we find a statistically significant difference, thereflected credibility and contextual information was no longer accessible. Given the disabling of NewsGuard’s free capabilities
on July 1, respondents were effectively treated for 3 to 4 weeks. We pre-registered that this disabling of NewsGuard was a partof our research design. We stated in our pre-registration that if we were able to collect digital trace data after this date, wewould test whether the NewsGuard treatment caused subjects to be more likely to visit sources deemed reliable by NewsGuardor less likely to visit sources deemed unreliable by NewsGuard in the period after the treatment ended.

6

magnitudes of these differences are very small.10
Since a lack of statistically significant coefficients does not necessarily imply effects of a negligible magnitude, we use equivalence testing to rule out any meaningful effects in all of the following models (Rainey
2014; Lakens 2017; Hartman and Hidalgo 2018). Conservatively, negligible effects are defined as those smaller
than 20% of a standard deviation (Cohen 1969) of the population on a pre-treatment measure of a variable,
although others have advocated for higher thresholds (Imbens and Rubin 2015; Hartman and Hidalgo 2018).
We thus calculate standardized effects for each estimate shown in Fig. 4 and 5. The magnitudes of these standardized effects never rise above 10% of a standard deviation, thus remaining far below the most conservative
established threshold, rejecting the hypothesis of even a small effect on the outcome variable. In addition
to these standardized effect sizes, we also estimate minimum detectable effects for covariate-adjusted ITT
models reported in the main text in Section J in the Supplementary Materials. Assuming power of 0.80 andthat statistically significant effects must cross the p-value threshold of 0.05, the models can at a minimum
detect effects larger than 6.5% of a standard deviation for all of the attitudinal measures and any effectlarger than 8.9% for all of the behavioral measures. Our design is thus well-powered to detect small effectsof the intervention (though this is generally not the case for interactions).
In our sample of respondents for whom we collect behavioral news consumption data (N = 946), mostdid not visit an unreliable news site during the two- to three-week pre-treatment period (over 65%) andjust under 12% of our sample’s news diet consisted of at least 5% of visits to news sites deemed unreliable
by NewsGuard. Indeed, only 1.5% of respondents’ news diets had an average NewsGuard reliability scorebelow 60 (the threshold for reliability). This distribution appears consistent across the treatment and control
groups. Fig. 3a through 3c present the distribution of average online reliability scores of respondents in boththe control and treatment groups over the three time periods of interest.11 The long tail of low-quality online
news consumption is in line with what work has reported (Guess et al. 2020a), though the relatively rareprevalence of visits to unreliable sites among our respondents creates a challenge in terms of statistical power
when estimating treatment effects on the behavioral measures focused on the consumption of unreliable news
(the proportion of unreliable news and the count of unreliable news) among the full population. For thisreason, we also test the effect of the treatment on the subset of respondents who consume the most lowquality news. Given that the average NewsGuard reliability score of respondents’ news diets was 87.6 out
of 100 and 67.4% of news viewed was considered reliable (scores of 60 or above), there is sufficient variation
for detecting heterogeneous effects on overall news diet quality by quantiles of these pre-treatment scores.
10 More details on how compliance was estimated, compliance rates, and how compliers differed from non-compliers can befound in the Materials and Methods section. About 1% of those in the control group already had the NewsGuard extension
installed and activated.
11 In Section N of the Supplementary Materials we present the distributions of the other four behavioral measures in eachtime period.

7

Figure 3: This figure presents the distribution of the average reliability scores of online news consumption among
respondents in the treatment and control groups with vertical dashed lines indicating the mean reliability score in eachgroup. Panel A presents these distributions during the pre-treatment period. Panel B presents these distributions
during the treatment period. Panel C presents these distributions during the post-treatment period.
(a) Pre-Treatment Period(b) Treatment Period(c) Post-Treatment Period

8

Contrary to Hypothesis 1, we do not find that randomized exposure to in-browser source reliability
information shifts online consumption of news away from unreliable publishers. We present the treatment
effect estimates for each pre-registered behavioral outcome in Fig. 4. As the figure indicates, we do not findstatistically significant decreases in the proportion of news consumed from unreliable sources or in the countof unreliable online news consumed, either during the period when NewsGuard was installed by those in thetreatment group or in the two weeks after NewsGuard became a pay service. We also do not find that theintervention measurably shifts individuals toward reliable news: We do not observe statistically significant
increases in the proportion of news consumed from reliable sources, in the count of visits to these sources,
or in the average reliability score of online news consumed in either time period due to the treatment. In
addition to an absence of statistically significant effects, the estimated magnitudes are extremely small: allof the reported effect sizes constitute less than a 0.08 change in the standard deviation of the pre-treatment
measure of that variable.
Given these results we conducted a series of non-preregistered supplementary analyses to better understand the seemingly negligible impact of the news quality labels on news consumption. To determine if thetreatment affects the time spent on unreliable or reliable news websites, we weight each variable by duration,
but this does not measurably change the results (details can be found in Section C of the Supplementary
Materials). We also measured these variables strictly as referrals from social media sites and search engines
and find no statistically significant treatment effects (details can be found in Section C of the Supplementary Materials). Finally, we divide the sample according to pre-treatment deciles of respondents’ averagenews reliability scores.12 Interestingly, we find relatively strong and statistically significant treatment effects
among those in the lowest decile of news diet reliability (Fig. 5): Relative to the average pre-treatment value,
we estimate a 5.4% increase in the treatment period and a 8.6% increase beginning July 1 in the averagereliability score of news consumed (full results for each behavioral measure across each decile can be foundin Section K of the Supplementary Materials). The effect appears to be weaker when measured between
treatment assignment and July 1 than in the two weeks after the extension’s functionality ended, which isconsistent with an over-time learning effect as respondents received more feedback about the reliability of
online sources in their search results and social feeds. Though suggestive (and likely underpowered), thesesubgroup effects are consistent with the intervention being mainly effective among those who consume thegreatest amount of content from relatively unreliable sources, which can be seen in the tails of the bottom
12 We do not report as a primary specification multiplicative interaction models. As Hainmueller et al. (2019) explain, modelsof this kind assume a constant change across values of the moderator and require sufficient common support. Given thediscussion of the skew in NewsGuard scores, it is unlikely that our data provide common support between the treatment andmoderator. Additionally, as we show below, it is unlikely that any effect heterogeneity is linear. For these reasons, the visualdisplay of our exploratory results is preferable to models that impose additional, implausible assumptions. We note that thesepoints also apply to all of our pre-registered moderation analyses given the above-noted skew.

9

two panels in Fig. 3. We also tested whether treatment effects were detectable among respondents that are
more likely to view unreliable online news (older respondents, those with lower levels of digital literacy, andthose that self-identify as Republican), but do not report any statistically significant effects on the qualityof online news consumption among these groups.13
Figure 4: This figure presents estimates of the effect of the intervention (ITT, with 95% confidence intervals) on
our pre-registered online behavioral measures in the two periods after treatment assignment: before July 1 when
the NewsGuard extension was freely available and the two-week period between July 1–13 when the NewsGuard
extension was disabled. The effect is reported in standard deviations of that measure (pre-treatment).

13 Results of these exploratory analyses can be found in Section M of the Supplementary Materials.

10Figure 5: This figure presents estimates of the effect of the intervention on the average NewsGuard reliability
score of respondents’ news diets (with 95% confidence intervals). This is estimated for subsets of the sample withpre-treatment reliability scores below successive decile cutpoints.

The next hypothesis predicted that source reliability feedback would increase trust in the media andreliable sources (H2). We find that the treatment does not increase trust in media or in specific reliable news
sources. Our estimates of the treatment effect on trust in media and in selected specific sources deemed
reliable by NewsGuard are not statistically distinguishable from zero, and the reported effect sizes are lessthan a 0.05 change in the standard deviation of the pre-treatment measure of that variable. A figure depicting
the effect of the intervention on our pre-registered attitudinal measures can be found in Section K of the
Supplementary Materials.
We also find no support for our final hypothesis, which predicted that exposure to the treatment could help
to alleviate pathologies such as affective polarization14 and political cynicism associated with consuming,
believing, and sharing news from unreliable sources (H3), which in retrospect is perhaps not surprisingbecause we did not find an effect for the treatment on consuming of believing news from unreliable sources.
14 We also do not find that any effect of the treatment is concentrated among those with higher levels of affective polarization.
These results can be found in Sections E and F in the Supplementary Materials.

11

RQ1 asked whether source reliability information affects belief in misinformation as well as accurate claims.
To answer this question, all respondents were asked to judge the veracity of five widely circulated statements
about the Black Lives Matter (BLM) movement and five similarly well-circulated statements about Covid-19
using a four-point scale in Wave 2. Of the five statements about each topic, three were false and two were
true. By taking the mean of the perceived veracity measure for the three false statements about each topic
we created a measure of belief in misinformation in the BLM movement and Covid-19, and we likewise
created a measure of belief in true information using the other two items. The intervention had no effect on
belief in misinformation about the Black Lives Matter movement and Covid-19, and it did not measurably
affect belief in the true statements.15
Our second research question asked whether exposure to the intervention leads to effects on other outcomes such as trust in institutions, belief that fake news is a problem in general, and belief that fake news
is a problem in the mainstream media. We do not find that the intervention measurably affected theseoutcomes.16 Finally, RQ3 asked if any effects are moderated by specific characteristics (proportion of news
consumed that is unreliable, partisanship of news diet, online news consumption, social media use, digital
literacy, and belief in scientific misinformation), but we find no other consistent evidence of effect heterogeneity.

Discussion
Despite the promise of browser-based tools designed to reduce users’ reliance on misinformation,17 evidence
from a pre-registered randomized field experiment among a large representative sample of Americans reveals
that the particular intervention studied here — providing dynamic, in-feed source reliability labels — does
not measurably improve news diet quality or reduce misperceptions on average among the general population.
Our estimates, based on both survey and behavioral data collected over an extended period, are precise andrule out even modest effect sizes by conventional standards.
Though we are able to measure pre- and post-treatment news consumption “in the wild” as well as
treatment compliance at the point of installation, we do not capture the contents of participants’ browser
experience — what they see but don’t click on. This means that we did not directly observe how often
users encountered NewsGuard’s labels in the course of browsing their feeds and perusing search results
pages during the study period. We can estimate how often they were exposed to these labels by counting thenumber of visits to online news sites rated by NewsGuard (which trigger a source quality label in the browser
15 Reported effect sizes are all below a 0.03 change in the standard deviation.
16 Reported effect sizes are all below a 0.03 change in the standard deviation.
17 Survey data on users’ experience with the tool indicates generally positive assessments (Gallup 2019);//www.politico.com/news/magazine/2021/04/27/america-social-media-problem-newsguard-484757.

12

see also https:

bar when the extension is installed), Google search results pages, and time spent on Facebook or Twitter
during the pre-treatment period; we present distributions of these measures in the Supplementary Materials
in Section J. We are most interested in respondents’ time spent on search engine results pages (SERPs) andsocial media feeds because users scrolling through these pages are likelier to be exposed to the credibility
ratings for multiple news sites as opposed to one at a time. This should theoretically translate to greater
potential for the labels’ effectiveness than discrete visits to online news sites rated by NewsGuard, though
we find that the majority of our respondents rarely visit Google SERPs for long periods of time. Specifically,
the median time spent on Google SERPs per day in the pre-treatment period is only a few seconds, andthe mean is 1.6 minutes. This is consistent with evidence that users typically gravitate toward top-ranked
search results (Guan and Cutrell 2007).The relative rarity of exposure to credibility ratings in these contexts
may partly explain the negligible effect of this intervention on the quality of online news consumption anddownstream attitudes.
Our results speak to a large body of work on heuristics, cognitive processing, and reputation theory (e.g.,
Chaiken 1987; Flanagin and Metzger 2000; Kim et al. 2019). The NewsGuard shield icons provide expertisecues, which have been shown since the work of Hovland to be associated with increased source credibility(Hovland et al. 1953). However, in a more partisan age in which attitudes toward news sources are strongly
correlated with partisanship (Jurkowitz et al. 2020), relatively subtle contextual information may not be a
sufficiently powerful prod to shift perceptions of source credibility. Our findings also relate to an emerging
body of research on accuracy motives among social media users and attempts to encourage discernment innews sharing behavior via “accuracy nudges” (Pennycook et al. 2021). Though real-world experimentation
points to the efficacy of such interventions, our null findings raise the possibility that accuracy mechanisms
may operate differently for publicly observable sharing behavior than for private consumption. For example,
sharing false information may come with a perceived reputational cost that is not associated with low-quality
news consumption (Altay et al. 2019).
Although we do not uncover statistically significant average treatment effects, we present suggestive
evidence of a substantively meaningful boost in news quality among the heaviest consumers of misinformationin our sample (who comprise a small proportion of respondents, approximately 10%, consistent with priorresearch on fake news exposure). In both the treatment period (before July 1) and in the post-treatment
period (beginning July 1) we observed the quality of news diets among the lowest 10% of our sample increaseby 5.4% and 8.6% from their pre-treatment levels, respectively. For the period beginning July 1, we evenobserve a statistically significant increase in news reliability among those in the bottom 20% of pre-treatmentnews consumption quality.
NewsGuard is not the only web extension that offers expert ratings, but it is likely the most comprehensive
13

and transparent. Other web extensions, such as NoBias and Media Bias/Factcheck, do provide similar levelsof coverage online but report ideological bias rather than reliability. Other extensions such as Newstrition
and The Factual provide article and source-level ratings, but these only appear in social media feeds and not
in search engine results. Given these considerations, it seems likely that other extensions would not produce
larger treatment effects than those we estimate for NewsGuard.
Our findings illustrate the challenge of studying interventions arguably aimed at a specific (and often
difficult-to-sample) population, namely the heaviest consumers of misinformation. Though we chose to
optimize our sampling for maximal treatment compliance, this came with a tradeoff in the form of lower
prevalence of the target behavior. Future research should consider the costs and benefits of other strategies,
such as oversampling from the desired population to increase statistical precision. Our results are bestinterpreted as population-level estimates of the treatment effect, which remains a parameter of great interest
to policymakers, platforms, and designers of digital tools alike.

Materials and Methods
NewsGuard Extension and Ratings
To produce credibility ratings, NewsGuard employs a team of trained journalists and editors to review andrate news and information websites based on nine journalistic criteria. The criteria assess basic practices ofreliability and transparency. Based on a site’s performance on these nine criteria, it is assigned a reliability
rating from 0–100.18 Online domains with score of 60 or higher are considered reliable (green shield), whilescores below 60 are considered unreliable.19 A histogram of NewsGuard scores for the majority of online
news domains can be found in Section B of the Supplementary Materials.20 NewsGuard can be installed on
all major web browsers (Safari, Microsoft Edge, Mozilla Firefox, Internet Explorer, and Google Chrome) as
well as Android and iOS mobile phones. Normally, the NewsGuard extension costs $2.99 per month, but it isavailable for free (and bundled) with Microsoft Edge as well as to over 200 million potential users worldwide
through assorted partnerships.21
18 The criteria are: “Does not repeatedly publish false content”, “Gathers and presents information responsibly”, “Regularly
corrects or clarifies errors”, “Handles the difference between news and opinion responsibly”, “Avoids deceptive headlines”,“Website discloses ownership and financing”, “Clearly labels advertising”, “Reveals who’s in charge, including any possible
conflicts of interest”, and “The site provides names of content creators, along with either contact or biographical information”.
19 Over 41% of the more than 5,000 news domains rated received a red, suspect rating. Reassuringly, the NewsGuard list
contains most of the “fake news” publishers identified by Allcott et al. (2019); 88% of the online news domains in the Allcott
et al. (2019) list are rated as unreliable by NewsGuard. In addition, 99% of mainstream online news domains identified byMicrosoft Project Ratio are rated as reliable by NewsGuard.
20 Over the course of 2020, NewsGuard rated 2,144 additional news domains and partnered with the World Health Organization
to report misinformation and flag 371 websites that spread misinformation about Covid-19 in the first months of the pandemic.
21 Currently, NewsGuard is offered for free to 30 million BT internet and mobile customers, students through TurnItIn, andpatrons at over 750 libraries (including the Chicago Public Library).

14

Data, Sample and Measures
We conducted a two-wave online panel survey of U.S. adults through the survey company YouGov in thesummer of 2020 that included an encouragement to install NewsGuard in the first wave. Respondents
were selected by YouGov’s matching and weighting algorithm to approximate the demographic and politicalattributes of the U.S. population (32% college graduates, 45% male, median age 50 years old; 46% identify
as Democrats and 36% as Republicans). We also oversampled members of the YouGov Pulse panel, who
voluntarily provide behavioral data on their online information consumption (N = 946) (see Section B
in the Supplementary Materials for demographic details). Pulse panelists confidentially share visit-level
data on domains and URLs of web activity, including estimated duration and time stamps, on registereddesktop/laptop and mobile devices. Data from Pulse panelists in our sample comprise N = 11, 903, 134
observations collected from laptop and desktop computers via the Reality Mine app. In the main text we
present measures using desktop data only, given that respondents could only install NewsGuard on theirdesktops at the time the study ran.22 Secure transactions and passwords are not collected or shared withresearchers, and YouGov performs a scrub of personally identifying information before delivering the data.
For thorough validation of Pulse data, see Guess et al. (2020a); Guess (2020).
The main outcome of interest is news consumed by our study participants from publishers of low-quality
news sources. We are also interested in the effect of this intervention on the dependent variables specified in
our other hypotheses and research questions, including the perceived accuracy of true and false news stories,
trust in media, and other possible downstream effects. We are also interested if effects on these variables arehigher within certain subgroups, such as those those who use social media more or that have lower levels ofdigital literacy. Details on all of these variables are available in Section A of the Supplementary Materials.

Treatment and Compliance
At the beginning of the Wave 1 survey, respondents were asked if they would be willing to install an extension
to their web browser, which was intended to minimize differences between the treatment and control groupand in compliance. We then randomly assigned respondents in Wave 1 to be encouraged to install the
NewsGuard web extension. We do not find that those in the treatment and control groups were statisticallydifferent across income, race, partisanship, education, and gender (sample demographic details are presented
in Section B of the Supplementary Materials). Those in the treatment group were slightly younger (by 2years) and had slightly higher levels of digital literacy than the control group, but the magnitudes of thesedifferences are small. We also found recontact rates in the treatment group and the control group to be
22 In Section O of the Supplementary Materials we replicate Fig. 4 in the main text but use only mobile data that we collected.

15

similar (14.1% in the control group compared to 13.2% in the treatment group).23 Based on Wave 2 survey
data, 94% of participants in the treatment group who installed NewsGuard felt neutral or positive toward
the extension, and 41% liked the extension “a little” or “a lot.”
We define “compliance” as successfully installing and activating the NewsGuard extension (as a result of
the encouragement), which we validate via a script linked at the beginning of the Wave 1 survey and during
the last week of the treatment period.24 This gives us two separate compliance measures that we can use
for over 92% of our respondents.25 Notably, we find little difference in the characteristics of respondents
who would successfully take the treatment if encouraged (“compliers”)26 and those who would not take thetreatment if encouraged (“never-takers”). We find no statistically significant evidence that respondents whocomply are very much different than those who did not comply along the dimensions of age, partisanship,gender, or race. Compliers are more likely to hold a postsecondary degree, report a higher income level,and score higher on our digital literacy scale than never-takers, but the magnitudes of these differences are
small.27 Among the respondents for whom we have web data, compliers score higher on our digital literacy
scale and consumed a higher proportion of unreliable news domains than never-takers in the pre-treatment
period, but again, the magnitudes of these differences are small.

Analysis
Our pre-registered primary analyses are an Intent-to-Treat (ITT) model and two Complier Average Causal
Effect (CACE) models using two different compliance measures, one that measures compliance solely at the
beginning of the treatment period and another which measures compliance both at the beginning and endof the treatment period. We report both unadjusted (differences in means) and covariate-adjusted estimatesof treatment effects for each dependent variable of interest.28 For covariate-adjusted models, we selectedcovariates for inclusion using lasso regressions run separately for each dependent variable. The list of pretreatment variables for possible inclusion as covariates can be found in Section A of the Supplementary
23 We do not find significant differences in demographic characteristics between those in the control and treatment groups
who did not complete Wave 2. We also do not find that treatment compliance rates differ between those who completed Wave
2 and those who did not (53% of respondents in the treatment group who attrited installed the NewsGuard extension, while
57% of respondents in the treatment group who did not attrite installed the extension). Details are presented in Section B of
the Supplementary Materials.
24 Respondents click the verification link in both compliance checks and they are redirected to a separate page in which we
can verify whether the NewsGuard extension has been installed and is active. We record their unique ID and the result of thecompliance check, so we can match it to their survey responses. We did not ask early respondents of our Wave 2 survey tocomplete the compliance check during the survey. Rather, we waited until the last week of the treatment period and sent theman e-mail asking them to click on the verification link. Respondents who filled out the Wave 2 survey in the last week of thetreatment period were asked to click on the verification link at the end of their Wave 2 survey.
25 This compliance check failed about 4% of the time due to random browser or survey issues that do not appear biased in
any identifiable direction. Given this, we only collected first and second compliance check data for 92% of our respondents.
26 In this analysis we define compliance using our second, stronger measure that uses the compliance checks during Wave 1
and during the last week of the treatment period.
27 Details comparing these two groups can be found in Section B of the Supplementary Materials.
28 We use robust standard errors (HC2) in all analyses and report p-values from two-tailed t-tests.

16Materials.
One concern with using an ITT model is that it will understate the true effect of an intervention whensome respondents do not comply with the encouragement, but we report relatively high levels of compliance(discussed at length in the previous section). Given high levels of compliance we report the covariate-adjusted
Intent-To-Treat effect in the main text of the paper and the Complier Average Causal Effect (CACE) using
both measures of compliance in the Supplementary Materials.

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Supplementary Methods and Materials: News credibility labels
have limited but uneven effects on news diet quality and fail toreduce misperceptions

Contents
A Study design

3

A.1 Dependent Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

A.2 Moderators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

A.3 List of pre-treatment covariates for possible inclusion . . . . . . . . . . . . . . . . . . . . . . .

9

B Descriptive Statistics

10

B.1 Descriptive statistics for sample by treatment and control groups . . . . . . . . . . . . . . . .

10

B.2 Descriptive statistics for sample by treatment and control groups by attrition . . . . . . . . .

11

B.3 Descriptive statistics for sample by compliance status. . . . . . . . . . . . . . . . . . . . . .

12

B.4 Descriptive Statistics for NewsGuard Ratings . . . . . . . . . . . . . . . . . . . . . . . . . . .

14

C Results From All Covariate-Adjusted Models

15

C.1 Behavioral Measures (Before July 1st) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15

C.2 Behavioral Measures (After July 1st) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

17

C.3 Behavioral Measures - Weighted by Duration (Before July 1st) . . . . . . . . . . . . . . . . .

19

C.4 Behavioral Measures - Weighted by Duration (After July 1st) . . . . . . . . . . . . . . . . . .

21

C.5 Behavioral Measures - Referrals from Social Media Sites and Search Engines (Before July 1st) 23
C.6 Behavioral Measures - Referrals from Social Media Sites and Search Engines (After July 1st)25

C.7 Attitudinal Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27

D Results From All Covariate-Unadjusted Models
D.1 Behavioral Measures (Before July 1st) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1

41
41

D.2 Behavioral Measures (After July 1st) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

43

D.3 Behavioral Measures - Weighted by Duration (Before July 1st) . . . . . . . . . . . . . . . . .

45

D.4 Behavioral Measures - Weighted by Duration (After July 1st) . . . . . . . . . . . . . . . . . .

47

D.5 Behavioral Measures - Referrals from Social Media Sites and Search Engines (Before July 1st) 49
D.6 Behavioral Measures - Referrals from Social Media Sites and Search Engines (After July 1st)51

D.7 Attitudinal Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

53

E Results From Covariate-Adjusted Model Testing Hypotheses About Moderators

57

F Results From Covariate-Unadjusted Model Testing Hypotheses Regarding Moderators 59
G NewsGuard Indicators of Source Reliability

61

H Minimum Detectable Effects for Covariate-adjusted ITT and CACE Models

62

I

Effect of Intervention on Behavioral Measures Using Different Samples of Respondents 63

J Distribution of Pre-Treatment Behavioral Measures

68

K Figure: Effect of Intervention on Attitudinal Measures

74

L Figure: Proportion of the average daily proportion of unreliable news viewed of thetreatment group75
M Non-Pre-Registered Exploratory Results

76

N Balance Table and Density Plots133

O Results From Covariate-Adjusted Models using Mobile Behavioral Data

138

2

A

Study design

A.1

Dependent Variables(1) Belief in misinformation and true information: We test whether those in the treatment groupare less likely to believe three popular false statements about the Black Lives Matter movement and three
popular false statements about Covid-19. We also test if the respondents were more likely to believe two
popular true statements the Black Lives Matter movement and true popular statements about Covid-19.
To construct this measure, we asked respondents to evaluate the accuracy of a number of headlines on thefollowing 4-point scale:Possible answers: (A) Not at all accurate (1) (B) Not very accurate (2) (C) Somewhat accurate (3) (D) Very
accurate (4) (E) Don’t know (NA)We used two sets of items. One set (Covid-19 misinformation/information) consisted of older, popular claims
about Covid-19 circulated before (within 3 months of the beginning of the treatment period) and during
the treatment period. The misinformation about Covid-19 was collected from NewsGuard’s own newsletter,
which documented these false claims as among the most popular at the time. The accurate information waschosen by the researchers from widely shared facts about Covid-19.
The second set consisted of popular claims about the Black Lives Matter movement that started to be
spread during the treatment period. The misinformation about the Black Lives Matter was selected by the
researchers looking through popular claims about Black Lives Matter fact-checked by one of the following
fact-checking organizations: Snopes, Politifact, Reuters, FactCheck.org, and the Associated Press. True
statements were chosen by the researchers from popular news during that period.
It is possible that participants were more likely exposed to the Black Lives Matter information/misinformation
relative to the Covid-19 information/misinformation, but we have no reason to believe that the claims about
Covid-19 weren’t circulating during the treatment period.
All of the pieces of misinformation were rated as false by at least one third-party fact-checking organization.
The order of the headlines was randomized within wave for each respondent. All of the headlines are listed
below:(1.1) Information about the Black Lives Matter movement (True and False news)
We asked our respondents in the wave 2 survey: Below are statements people have been making about
the Black Lives Matter movement and related protests. Please indicate whether you believe the following
statements are accurate or not.
(1) Many of these protesters were paid to attend the protest. (False)
A number of fact-checks debunked this popular false claim (click on the hyperlinks to be directed to the
URL):
Snopes ; Politifact ; Reuters(2) The 75-year old man shown on video being pushed to the ground by police in Buffalo, New York was amember of Antifa. (False)
A number of fact-checks debunked this popular false claim (click on the hyperlinks to be directed to the
URL):
Politifact ; Factcheck ; AP News
(3) No active police officer deaths have been connected to violence during protests. (True)
The true information can be found here (click on the hyperlinks to be directed to the URL):
Politifact3

(4) Donald Trump mocked George Floyd by saying “I can’t breathe” at a recent rally. (False)
A number of fact-checks debunked this popular false claim (click on the hyperlinks to be directed to the
URL):
Politifact ; Reuters(5) In response to the Black Lives Matter movement, NASCAR has banned Confederate flags at all races.
(True)The true information can be found here (click on the hyperlinks to be directed to the URL):
CNN ; NPR
(1.2) Information about Covid-19 (True and False news)
Below are statements people have been making about Covid-19 (the novel coronavirus). Please indicate
whether you believe the following statements are accurate or not.
(1) Covid-19 is being spread by 5G cell phone technology. (False)
A number of fact-checks debunked this popular false claim (click on the hyperlinks to be directed to the
URL):
Reuters ; Fullfact ; BBC(2) The Bill and Melinda Gates Foundation is planning to implement a mandatory Covid-19 vaccine programthat utilizes microchips under a patient’s skin. (False) A number of fact-checks debunked this popular false
claim (click on the hyperlinks to be directed to the URL):
Factcheck ; Factcheck ; Nature(3) A new loss of taste or smell is a symptom of Covid-19. (True)
The true information can be found here (click on the hyperlinks to be directed to the URL):
Mayo Clinic ; Healthline(4) The Chinese government created Covid-19 as a bioweapon. (False)
A number of fact-checks debunked this popular false claim (click on the hyperlinks to be directed to the
URL):
Factcheck ; Buzzfeed News

(5) Covid-19 can be spread by people who do not show symptoms. (True)
The true information can be found here (click on the hyperlinks to be directed to the URL):
Nebraskamed ; CDC(2) Trust in media:
Media Trust Index (Wave 1: M edia T rust Indexi ; Wave 2: M edia T rust Index 2i ): Measures
the respondent’s trust in media using the summed value assigned to the respondent’s answer to the next
three questions. To create an index for media trust we sum the values assigned to each respondent’s answer
for each question. The sum is assigned to the following variables: M edia T rust Indexi (wave 1) andM edia T rust Index 2i (wave 2):
Some people think that by criticizing leaders, news organizations keep political leaders fromdoing their job. Others think that such criticism is worth it because it keeps political leadersfrom doing things that should not be done. Which position is closer to your opinion?
(A) Stops political leaders from doing their job (0) (B) Keep political leaders from doing things that shouldn’t
be done (1)4In presenting the news dealing with political and social issues, do you think that news organizations deal fairly with all sides, or do they tend to favor one side?
(A) Deal fairly with all sides (1) (B) Tend to favor one side (0)Based on what you know, how often do you believe the nation’s major news organizations
fabricate news stories?
(A) All the time (0)(B) Most of the time (0.25)(C) About half the time (0.5)(D) Once in a while (0.75)(E) Never (1)The first question is pulled from the Pew American Trends Panel survey (Pew, 2017). The second twoquestions are taken from Guess, Nyhan and Reifler (2019).
(3) Trust in specific mainstream online news producers
This question measures the level of trust in the following media sources. The value assigned to each respondent is in parentheses next to the answer. Higher values denotes higher levels of trust.
How much, if at all, do you trust the information you get from:(7) Fox News
(8) CNN(9) MSNBC(10) CBS(11) ABC(12) NBC

Possible Answers:(A) A lot (4)(B) Some (3)(C) Not too much (2)(D) Not at all (1)(4) Affective polarization
Measures affective polarization by taking the absolute value of the difference between the feeling thermometers of each political party taken during each wave. Equation: dem f eeling thermometeri - rep f eeling thermometeri .
Below is the question asked to create this measure.

Feeling Towards Democratic and Republican Party (Wave 1: dem f eeling thermometeri , rep f eeling thermometer
Wave 2: dem f eeling thermometer 2i , rep f eeling thermometer 2i ): Measure the respondent’s the positive feeling towards each party on a thermometer scale of 0 (negative) to 100 (positive). This is one measureof affective polarization. The value of the thermometer is assigned to each respective variable.
(1) How would you rate the Democratic Party?
(2) How would you rate the Republican Party?
This method is the same method used to calculate affective polarization in the ANES survey.

5(5) Political cynicism
Measures the respondent’s level of political cynicism. The value assigned to each respondent is in parentheses
next to the answer. Higher values denotes higher levels of political cynicism.

Do you think that quite a few of the people running the government are crooked, not very
many are, or do you think hardly any of them are crooked?
(A) Quite a few (3)(B) Not very many (2)(C) Hardly any (1)(D) Don’t Know (NA)This questions is the same used by and Dancey (2012) and ANES.
(6) Trust in institutions
Trust: Trust in institutions is measured using four questions from the ANES survey that measures trust.
For each question a respondent is assigned a value between 0 and 1 dependent on their answer. The values
they are assigned for each answer are in parentheses.

How much of the time do you think you can trust each of the following groups to do what isright?
Groups: “The federal government in Washington D.C.” ; “Law enforcement” ; “The media” ; “People ingeneral”
Possible Answers: “Almost never” (0) ; “Some of the time” (0.33) ; “Most of the time” (0.67) ; “Almost
Always” (1.0)

How much do public officials care what people like you think?
(A) None at all (0)(B) A little (0.25)(C) A moderate amount (0.50)(D) A lot (0.75)(E) A great deal (1.0)

Do you think the government today has:(A) Too little power (0)(B) About the right amount of power (0.5)(C) Too much power. (1.0)

How much can people like you affect what the government does?
(A) None at all (0)(B) A little (0.25)(C) A moderate amount (0.50)(D) A lot (0.75)(E) A great deal (1.0)

T rust Scorei is the average value each respondent received.
This method of measurement is the same as that used in the ANES and Miller, Saunders and Farhart (2016).
6

(7) Belief that fake news is a problem in general
We assign a value to the variables, F ake news problemi (wave 1) and F ake news problem 2i (wave 2),
depending on the value to the answer they choose for the question below.
How much of a problem do you think made-up news and information is in the country today?
(A) 4: A very big problem (4)(B) 3 (3)(C) 2 (2)(D) 1, Not a problem at all (1)(8) Belief that fake news is a problem in the mainstream media.

We assign a value to the variables, F ake news M Mi (wave 1) and F ake news M M 2i (wave 2), dependingon the value to the answer they choose for the question below.
President Trump often accuses several major news outlets of being “fake news.” Do you think“fake news” from the mainstream media is a real problem, or do you think the mainstream
media are generally reliable and report the facts as best they can?

(A) 5: “Fake news” is a serious problem affecting the mainstream media. (5)(B) 4 (4)(C) 3 (3)(D) 2 (2)(E) 1: Mistakes by the mainstream media are rare, and I generally find what they report to be credible. (1)A.2

Moderators(1) Social media use
We measure the respondent’s activity on Twitter and Facebook. We ask the following question:
How frequently do you: (1) Look at Twitter(2) Post things on Twitter(3) Look at Facebook(4) Post things on Facebook

Possible responses: (A) At least 10 times a day (7)(B) Several times a day (6)(C) About once a day (5)(D) 3 to 6 days a week (4)(E) 1 to 2 days a week (3)(F) Every few weeks (2)(G) 1 to 2 days a week (1)(H) Never (0)(I) Don’t Know (NA)7

We create an index for social media frequency by taking the average of the values assigned to the variables
f acebook post f requencyi , twitter post f requencyi , twitter view f requencyi , and f acebook view f requencyi .
The average value of these variables are assigned to the variable Social M edia F reqi .
(2) Digital literacy
Digital literacy is measured using the following two grid questions.
The first grid question asks for respondent’s familiarity with the following terms on a five
point scale (1 representing no understanding and 5 representing full understanding):(1) Phishing(2) JPG(3) Cache(4) Malware(5) RSS(6) Hashtag

The second grid question asks respondents’ agreement with the following statements on a scaleof -4 = Strongly Disagree to 4 = Strongly Agree:(1) I prefer to ask friends how to use any new technological gadget instead of trying to figure it out myself.
(2) I feel like information technology is a part of my daily life.
(3) Using information technology makes it easier to do my work.
(4) I often have trouble finding things that I’ve saved on my computer.
Note: The value for one and four are reverse coded.
By summing all of the values we can create a digital literacy score that is assigned to the variable: DL Scorei
for each respondent. We take the inverse to determine if the effect size increased with lower levels of digital
literacy.
(4) Online news consumption
The number of non-successive domains with NewsGuard scores before the respondent took the wave 1 survey(from May 15th to the first day of wave 1). The sum of non-successive domains with NewsGuard scores after
the treatment.
(5) Partisan news diet
Using web tracking data from before individuals are treated we analyze web tracking data. We average thepartisanship scores from domains visited to create a measure of news diet partisanship. The partisanshipscores are derived from Eady et al. (N.d.) and each domain visited if in the Eady et al. (N.d.) list is given
a partisanship score. If it is not in the list it is not used. The average partisanship score is assigned to thevariable partisanship new dieti .
(6) Prior visits to online publishers of fake news
The sum of the domain unreliable dummyj scoresassigned to domains visited by the respondent divided by number of non-successive domains with NewsGuard
scores before the respondent took the wave 1 survey.
(7) Those who use the Safari web browser
A dummy variable that is assigned “1” if the browser the respondent is using the Safari browser to take thesurvey and possibly download the extension (Yes: 1 ; No: 0).

8

A.3

List of pre-treatment covariates for possible inclusion

Our list of pre-treatment covariates for possible inclusion are: gender (F emalei dummy), education level(Educationi ), age (Agei ), age squared (Age2i ), 7-point party id (P arty IDi ), race/ethnicity (Ethnicityi ), 7point ideology (Con Ideologyi ), media trust (M edia T rust Indexi ), social media use (Social M edia F reqi ),trust in information from newspapers (trust newspapersi ), trust in information from social media (trust socmedi ),news consumption (trad news networki , trad news cablei , trad news printi , trad news publici , trad news talki ,trad news deski , trad news mobilei ), browser used, pre-treatment version of the DV (if available and notalready listed), logged number of visits to domains with NewsGuard scores (if DV is a Pulse count).

9

B

Descriptive Statistics

B.1

Descriptive statistics for sample by treatment and control groups

Below, we present the summary statistic for those who completed both waves and those who only completed
the first wave.
Group

Observ. Age

Dig. Lit.

Race(Prop.
White)
0.76

Education

Ideology

48.21

Income Gender(Prop.
Female)6.23
0.61

Control and did not takethe Wave 2 survey
Control and took bothsurveys
Treated and did not takethe Wave 2 survey
Treated and took bothsurveys

220

48.99

3.86

-0.39

1339

56.73

47.11

6.64

0.54

0.80

4.02

-0.19

304

48.39

47.11

6.55

0.58

0.79

4.00

-0.46

1998

54.55

47.92

6.44

0.56

0.80

4.04

-0.28

10

B.2

Descriptive statistics for sample by treatment and control groups by attrition

Below, we present the summary statistic for groups that completed both waves and those that only completed
one wave. 14.1% of respondents in the control group did not take the second wave survey. 13.2% of
respondents in the treatment group did not take the second wave survey.
Group

Observ. Age

Dig. Lit.

Race(Prop.
White)
0.78

Education

Ideology

46.92

Income Gender(Prop.
Female)6.76
0.55

Control and we do nothave digital trace data
Control and we have digital trace data
Treated and we do nothave digital trace data
Treated and we have digital trace data

1035

55.78

4.00

3.77

304

59.94

47.77

6.23

0.51

0.85

4.07

3.94

1461

52.96

48.27

6.51

0.55

0.79

4.01

3.67

537

58.87

46.97

6.24

0.59

0.86

4.09

3.85

11

B.3

Descriptive statistics for sample by compliance status

In the following figures, we show estimated means along various dimensions for respondents who would install
the NewsGuard web browser extension if and only if they are assigned to receive it (“compliers”) and those
who would not under any circumstances (“never-takers”) computed following the procedure in Marbach andHangartner (2020). Points show estimated means for the complete sample, respondents who would take thetreatment only if encouraged to install it (“compliers”), and respondents who would not take the treatment
even if encouraged to install it (“never-taker”) computed following Marbach and Hangartner (2020). Allvariables are rescaled to the 0 to 1 interval. Lines display 95% confidence intervals based on bootstrapped
standard errors. Asterisks on variable names indicates that the mean values for compliers and never-takers
are significantly different (p < .05).
Figure 1: Profile of all compliers and never-takers

12Figure 2: Profile of compliers and never-takers with whom we collect web-browsing data

13

B.4

Descriptive Statistics for NewsGuard Ratings

A histogram of NewsGuard scores is listed below:
Figure 3: Histogram of NewsGuard Scores

14

C

Results From All Covariate-Adjusted Models

C.1

Behavioral Measures (Before July 1st)

Table 1: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0019(0.0028)0.8631∗∗∗(0.0508)
0.82120.8208821

CACE (Model 1)−0.0022(0.0030)
0.8642∗∗∗(0.0511)0.82110.8207806

CACE (Model 2)−0.0025(0.0035)
0.8571∗∗∗(0.0533)
0.81370.8132786

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 2: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0075(0.0125)
0.7992∗∗∗(0.0226)
0.66140.6605821

CACE (Model 1)−0.0065(0.0132)
0.7958∗∗∗(0.0231)0.65520.6544806

CACE (Model 2)−0.0060(0.0150)
0.8034∗∗∗(0.0229)
0.66620.6653786

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 3: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Age-Squared
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0409(0.0216)0.0000∗∗∗(0.0000)
0.8420∗∗∗(0.0261)
0.0386∗∗∗(0.0061)0.7246
0.7233854

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

15

CACE (Model 1)
0.0443(0.0227)0.0000∗∗∗(0.0000)
0.8422∗∗∗(0.0266)
0.0386∗∗∗(0.0062)
0.72140.7201
838

CACE (Model 2)
0.0500(0.0263)0.0000∗∗∗(0.0000)
0.8346∗∗∗(0.0271)
0.0399∗∗∗(0.0064)0.71560.7142814

Table 4: Testing the Effect of the Intervention on Count of Reliable News Consumed with Covariate-Adjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Age
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0100(0.0194)
0.0048∗∗∗(0.0008)0.5013∗∗∗(0.0367)0.2079∗∗∗(0.0170)0.86680.8661
854

CACE (Model 1)−0.0100(0.0205)
0.0049∗∗∗(0.0008)0.5019∗∗∗(0.0376)0.2075∗∗∗(0.0175)
0.86430.8636
838

CACE (Model 2)−0.0091(0.0234)
0.0046∗∗∗(0.0008)
0.4968∗∗∗(0.0382)0.2109∗∗∗(0.0179)
0.86500.8643814

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 5: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.4702(0.3221)0.8184∗∗∗(0.0357)
0.6676
0.6668821

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

16

CACE (Model 1)
0.5048(0.3346)
0.8227∗∗∗(0.0351)
0.67380.6730806

CACE (Model 2)0.5649(0.3853)
0.8213∗∗∗(0.0367)
0.66570.6649786

C.2

Behavioral Measures (After July 1st)

Table 6: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0043(0.0035)
0.7979∗∗∗(0.0619)0.74650.7459763

CACE (Model 1)−0.0043(0.0037)
0.7984∗∗∗(0.0622)0.74620.7455750

CACE (Model 2)−0.0031(0.0042)0.8222∗∗∗(0.0596)
0.7621
0.7615729

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 7: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0013(0.0142)0.7733∗∗∗(0.0247)
0.60840.6074
763

CACE (Model 1)−0.0005(0.0149)
0.7727∗∗∗(0.0253)
0.60320.6021
750

CACE (Model 2)−0.0011(0.0168)
0.7869∗∗∗(0.0247)0.62010.6191729

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 8: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0062(0.0192)
0.7034∗∗∗(0.0291)0.65460.6538846

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

17

CACE (Model 1)
0.0062(0.0202)0.7080∗∗∗(0.0298)
0.65250.6517
829

CACE (Model 2)
0.0141(0.0233)
0.7047∗∗∗(0.0306)
0.64980.6489805

Table 9: Testing the Effect of the Intervention on Count of Reliable News Consumed with Covariate-Adjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0240(0.0314)0.5667∗∗∗(0.0489)
0.1426∗∗∗(0.0224)0.71800.7169811

CACE (Model 1)−0.0267(0.0330)
0.5662∗∗∗(0.0499)
0.1427∗∗∗(0.0229)0.7146
0.7136797

CACE (Model 2)−0.0372(0.0373)
0.5724∗∗∗(0.0512)0.1447∗∗∗(0.0236)0.72280.7217774

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 10: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.5667(0.3577)0.7583∗∗∗(0.0428)0.60950.6085763

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

18

CACE (Model 1)
0.5644(0.3722)0.7705∗∗∗(0.0423)0.6206
0.6196750

CACE (Model 2)0.5934(0.4201)
0.7845∗∗∗(0.0402)0.62880.6278729

C.3

Behavioral Measures - Weighted by Duration (Before July 1st)

Table 11: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st) – Duration Weighted

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0048(0.0036)0.8476∗∗∗(0.0521)0.75410.7535821

CACE (Model 1)−0.0049(0.0038)
0.8481∗∗∗(0.0522)0.75370.7531
806

CACE (Model 2)−0.0056(0.0045)0.8406∗∗∗(0.0545)0.74330.7427786

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 12: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st) – Duration Weighted

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0013(0.0151)0.7521∗∗∗(0.0233)
0.5892
0.5882
821

CACE (Model 1)
0.0027(0.0158)
0.7475∗∗∗(0.0237)
0.5824
0.5814806

CACE (Model 2)0.0007(0.0182)0.7483∗∗∗(0.0244)0.58050.5795786

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 13: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st) – Duration Weighted

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−263.1468(159.9564)
0.9670∗∗∗(0.2059)0.74080.7402891

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

19

CACE (Model 1)−286.1545(171.2872)
0.9680∗∗∗(0.2069)
0.74110.7405872

CACE (Model 2)−330.9852(199.9503)
0.9683∗∗∗(0.2076)0.74100.7404848

Table 14: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st) – Duration Weighted

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)971.1443(2127.7790)
1.0578∗∗∗(0.0814)0.70710.7065891

CACE (Model 1)
923.8793(2257.1809)
1.0576∗∗∗(0.0814)0.70700.7064872

CACE (Model 2)1275.1953(2644.3343)
1.0567∗∗∗(0.0815)0.70640.7057848

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 15: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (Before July 1st) – Duration Weighted

Treatment
Party ID
Ideology
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.5953(0.4026)−0.2751∗(0.1245)−0.2606(0.1488)0.7466∗∗∗(0.0464)0.58430.5823821

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

20

CACE (Model 1)0.6506(0.4217)−0.2491∗(0.1240)−0.2860(0.1466)
0.7497∗∗∗(0.0466)
0.58670.5846806

CACE (Model 2)
0.6352(0.4853)−0.2762∗(0.1260)−0.2650(0.1494)
0.7469∗∗∗(0.0481)0.57780.5756786

C.4

Behavioral Measures - Weighted by Duration (After July 1st)

Table 16: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st) – Duration Weighted

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0060(0.0046)
0.8418∗∗∗(0.0577)
0.70640.7056
761

CACE (Model 1)−0.0065(0.0049)
0.8438∗∗∗(0.0577)
0.70760.7068748

CACE (Model 2)−0.0055(0.0056)0.8648∗∗∗(0.0563)
0.71580.7150727

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 17: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st) – Duration Weighted

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0011(0.0176)
0.6997∗∗∗(0.0283)0.50110.4998761

CACE (Model 1)
0.0042(0.0186)0.6945∗∗∗(0.0288)
0.49390.4925
748

CACE (Model 2)0.0059(0.0210)0.7142∗∗∗(0.0286)0.51420.5128727

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 18: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st) – Duration Weighted

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−123.5642(162.4097)
0.0010−0.0002
884

CACE (Model 1)−124.8044(171.1623)
0.0005−0.0006867

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

21

CACE (Model 2)−140.8097(201.6918)−0.0004−0.0016839

Table 19: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st) – Duration Weighted

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)2073.2925(1475.2341)
0.4019∗∗∗(0.1035)
0.57240.5714
846

CACE (Model 1)
2200.9569(1566.0089)
0.4015∗∗∗(0.1037)
0.57220.5712
829

CACE (Model 2)2575.8879(1842.3836)
0.4009∗∗∗(0.1039)0.57210.5711805

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 20: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (After July 1st) – Duration Weighted

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.6736(0.4559)0.7395∗∗∗(0.0509)0.53090.5297761

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

22

CACE (Model 1)0.7206(0.4726)0.7531∗∗∗(0.0507)0.54400.5427
748

CACE (Model 2)0.7293(0.5387)
0.7561∗∗∗(0.0522)0.54110.5399727

C.5

Behavioral Measures - Referrals from Social Media Sites and Search Engines (Before July 1st)

Table 21: Testing the Effect of the Intervention on Proportion of News Diet of Referrals From Search Engines
and Social Media (Google, Twitter, and Facebook) That is Unreliable with Covariate-Adjusted Models (HC2
Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0035(0.0068)
0.6561∗∗∗(0.0933)
0.40710.4053649

CACE (Model 1)−0.0046(0.0072)
0.6589∗∗∗(0.0938)
0.40890.4070638

CACE (Model 2)−0.0055(0.0083)
0.6272∗∗∗(0.0959)
0.37170.3696620

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 22: Testing the Effect of the Intervention on Proportion of News Diet of Referrals From Search Engines
and Social Media (Google, Twitter, and Facebook) That is Reliable with Covariate-Adjusted Models (HC2
Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0295(0.0166)
0.6616∗∗∗(0.0337)0.44710.4454649

CACE (Model 1)−0.0283(0.0174)
0.6568∗∗∗(0.0345)0.43840.4367
638

CACE (Model 2)−0.0351(0.0201)0.6504∗∗∗(0.0360)
0.42750.4257620

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 23: Testing the Effect of the Intervention on Count of Unreliable News Referred From Search Engines
and Social Media (Google, Twitter, and Facebook) with Covariate-Adjusted Models (HC2 Robust standard
errors) (Before July 1st)

Treatment
Log of news viewed
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0050(0.0167)
0.0237∗∗∗(0.0046)
0.8034∗∗∗(0.0525)0.60740.6057700

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

23

CACE (Model 1)−0.0078(0.0174)
0.0233∗∗∗(0.0047)
0.7996∗∗∗(0.0530)
0.60410.6024
689

CACE (Model 2)−0.0086(0.0202)0.0228∗∗∗(0.0047)0.7902∗∗∗(0.0537)
0.59630.5945668

Table 24: Testing the Effect of the Intervention on Count of Reliable News Referred From Search Engines
and Social Media (Google, Twitter, and Facebook) with Covariate-Adjusted Models (HC2 Robust standard
errors) (Before July 1st)

Treatment
Log of news viewed
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0105(0.0241)0.0848∗∗∗(0.0112)0.7563∗∗∗(0.0255)
0.77940.7785700

CACE (Model 1)−0.0130(0.0251)
0.0837∗∗∗(0.0113)0.7564∗∗∗(0.0258)
0.77810.7772
689

CACE (Model 2)−0.0140(0.0290)0.0846∗∗∗(0.0115)0.7626∗∗∗(0.0264)0.78280.7818668

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 25: Testing the Effect of the Intervention on Reliability Score of News Referred From Search Engines
and Social Media (Google, Twitter, and Facebook) with Covariate-Adjusted Models (HC2 Robust standard
errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.8349(0.6286)
0.5965∗∗∗(0.0711)
0.39990.3980649

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

24

CACE (Model 1)
0.8852(0.6595)
0.5869∗∗∗(0.0714)0.39330.3914
638

CACE (Model 2)0.9550(0.7581)0.5808∗∗∗(0.0742)0.37920.3772620

C.6

Behavioral Measures - Referrals from Social Media Sites and Search Engines (After July 1st)

Table 26: Testing the Effect of the Intervention on Proportion of News Diet of Referrals From Search Engines
and Social Media (Google, Twitter, and Facebook) That is Unreliable with Covariate-Adjusted Models (HC2
Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0037(0.0067)
0.6389∗∗∗(0.0889)
0.45390.4519548

CACE (Model 1)
0.0037(0.0072)
0.6386∗∗∗(0.0891)
0.45350.4514
538

CACE (Model 2)0.0048(0.0083)
0.6342∗∗∗(0.0971)
0.43100.4288522

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 27: Testing the Effect of the Intervention on Proportion News Diet of Referrals From Search Engines
and Social Media (Google, Twitter, and Facebook) That is Reliable with Covariate-Adjusted Models (HC2
Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0126(0.0195)
0.6370∗∗∗(0.0382)
0.3934
0.3912
548

CACE (Model 1)
0.0166(0.0206)
0.6310∗∗∗(0.0393)
0.38320.3809538

CACE (Model 2)0.0102(0.0228)0.6546∗∗∗(0.0396)0.40260.4003522

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 28: Testing the Effect of the Intervention on Count of Unreliable News Referred From Search Engines
and Social Media (Google, Twitter, and Facebook) with Covariate-Adjusted Models (HC2 Robust standard
errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0128(0.0159)
0.5838∗∗∗(0.0490)0.50150.4999612

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

25

CACE (Model 1)
0.0097(0.0166)
0.5868∗∗∗(0.0499)
0.50280.5011
601

CACE (Model 2)
0.0156(0.0193)
0.5773∗∗∗(0.0514)0.48850.4868582

Table 29: Testing the Effect of the Intervention on Count of Reliable News Referred From Search Engines
and Social Media (Google, Twitter, and Facebook) with Covariate-Adjusted Models (HC2 Robust standard
errors) (After July 1st)

Treatment
Age
Log of news viewed
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0135(0.0306)
0.0042∗∗∗(0.0012)0.0504∗∗∗(0.0123)0.7207∗∗∗(0.0279)
0.69320.6912
608

CACE (Model 1)
0.0164(0.0323)0.0043∗∗∗(0.0012)0.0495∗∗∗(0.0126)0.7204∗∗∗(0.0283)0.69080.6887
597

CACE (Model 2)0.0057(0.0370)0.0043∗∗∗(0.0012)0.0531∗∗∗(0.0129)0.7231∗∗∗(0.0291)0.69280.6907578

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 30: Testing the Effect of the Intervention on Reliability Score of News Referred From Search Engines
and Social Media (Google, Twitter, and Facebook) with Covariate-Adjusted Models (HC2 Robust standard
errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.1765(0.6905)
0.5535∗∗∗(0.1019)0.3256
0.3231548

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

26

CACE (Model 1)0.2247(0.7352)
0.5430∗∗∗(0.1026)0.31590.3133
538

CACE (Model 2)0.2338(0.8291)0.5196∗∗∗(0.1051)
0.29250.2898522

C.7

Attitudinal Measures

Table 31: Testing Effect of Intervention on Belief in Misinformation about the Black Lives Matter Movementwith Covariate-Adjusted Models (HC2 Robust standard errors)

Treatment
Web Browser (Safari)Party ID
Education
GenderAge
Trust in Media
IdeologyNews consumption (network news)
News consumption (print news)
News consumption (talk radio)
News consumption (news on desktop)
News consumption (news on mobile)
Trust of news in newspapers sm
Trust of news in newspapers
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0206(0.0297)−0.1440∗∗∗(0.0409)
0.0135(0.0118)−0.0291∗∗(0.0110)0.0756∗(0.0301)
0.0018(0.0011)−0.1892∗∗∗(0.0229)0.1027∗∗∗(0.0122)0.0218∗(0.0107)0.0543∗∗∗(0.0130)
0.0601∗∗∗(0.0128)−0.0535∗∗∗(0.0128)−0.0282∗∗(0.0105)
0.0035∗∗∗(0.0007)−0.1067∗∗∗(0.0184)
0.29160.28752662

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

27

CACE (Model 1)−0.0219(0.0317)−0.1481∗∗∗(0.0411)0.0142(0.0120)−0.0337∗∗(0.0111)0.0731∗(0.0304)
0.0021(0.0011)−0.1786∗∗∗(0.0231)0.1039∗∗∗(0.0125)
0.0212∗(0.0108)0.0507∗∗∗(0.0133)
0.0580∗∗∗(0.0129)−0.0535∗∗∗(0.0128)−0.0278∗∗(0.0106)
0.0034∗∗∗(0.0007)−0.1091∗∗∗(0.0185)
0.2926
0.2885
2588

CACE (Model 2)−0.0225(0.0380)−0.1391∗∗∗(0.0421)0.0133(0.0124)−0.0325∗∗(0.0113)0.0624∗(0.0310)0.0015(0.0012)−0.1695∗∗∗(0.0236)0.1054∗∗∗(0.0129)0.0253∗(0.0110)0.0472∗∗∗(0.0135)
0.0616∗∗∗(0.0133)−0.0587∗∗∗(0.0133)−0.0278∗(0.0108)
0.0032∗∗∗(0.0007)−0.1099∗∗∗(0.0189)
0.29280.28852491

Table 32: Testing Effect of Intervention on Belief in True Information about the Black Lives Matter Movementwith Covariate-Adjusted Models (HC2 Robust standard errors)

Treatment
Party ID
Trust in Media
Ideology
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0115(0.0265)−0.0159(0.0098)
0.1577∗∗∗(0.0197)−0.0786∗∗∗(0.0101)0.13900.13793193

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

28

CACE (Model 1)−0.0016(0.0284)−0.0164(0.0100)
0.1600∗∗∗(0.0200)−0.0759∗∗∗(0.0103)0.13700.13593104

CACE (Model 2)−0.0041(0.0340)−0.0180(0.0101)
0.1629∗∗∗(0.0202)−0.0745∗∗∗(0.0105)
0.14210.14102982

Table 33: Testing Effect of Intervention on Belief in Misinformation about Covid-19 with Covariate-Adjusted
Models (HC2 Robust standard errors)

Treatment
Party ID
Race/Ethnicity
Education
Trust in Media
IdeologyNews consumption (print news)
News consumption (talk radio)
Trust of news in newspapers sm
Trust of news in newspapers
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0012(0.0267)
0.0381∗∗∗(0.0114)−0.1640∗∗∗(0.0351)−0.0436∗∗∗(0.0096)−0.2358∗∗∗(0.0199)0.0695∗∗∗(0.0117)0.0606∗∗∗(0.0111)0.0500∗∗∗(0.0116)0.0055∗∗∗(0.0006)−0.0882∗∗∗(0.0161)
0.31820.31572729

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

29

CACE (Model 1)
0.0034(0.0281)0.0413∗∗∗(0.0115)−0.1844∗∗∗(0.0354)−0.0472∗∗∗(0.0096)−0.2272∗∗∗(0.0198)
0.0728∗∗∗(0.0118)
0.0562∗∗∗(0.0110)0.0455∗∗∗(0.0116)0.0051∗∗∗(0.0006)−0.0826∗∗∗(0.0161)0.32540.32292652

CACE (Model 2)
0.0187(0.0334)
0.0385∗∗(0.0117)−0.1947∗∗∗(0.0369)−0.0477∗∗∗(0.0097)−0.2158∗∗∗(0.0203)
0.0754∗∗∗(0.0121)0.0529∗∗∗(0.0112)0.0456∗∗∗(0.0119)0.0052∗∗∗(0.0006)−0.0844∗∗∗(0.0161)
0.32140.31882549

Table 34: Testing Effect of Intervention on Belief in True Information about Covid-19 with CovariateAdjusted Models (HC2 Robust standard errors)

Treatment
Trust in Media
IdeologyNews consumption (talk radio)
Trust of news in newspapers
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0004(0.0183)0.0896∗∗∗(0.0144)−0.0259∗∗∗(0.0056)−0.0428∗∗∗(0.0082)
0.0719∗∗∗(0.0113)0.11220.11083267

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

30

CACE (Model 1)
0.0134(0.0195)
0.0855∗∗∗(0.0143)−0.0291∗∗∗(0.0055)−0.0403∗∗∗(0.0083)
0.0689∗∗∗(0.0113)0.11290.1115
3172

CACE (Model 2)
0.0119(0.0233)
0.0781∗∗∗(0.0145)−0.0293∗∗∗(0.0056)−0.0439∗∗∗(0.0084)
0.0696∗∗∗(0.0115)
0.11350.1121
3048

Table 35: Testing Effect of Intervention on Trust in Media with Covariate-Adjusted Models (HC2 Robust
standard errors)

Treatment
Party ID
Trust in Media
Ideology
Trust of news in newspapers
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0303(0.0200)−0.0550∗∗∗(0.0076)
0.5555∗∗∗(0.0154)−0.0564∗∗∗(0.0075)
0.0771∗∗∗(0.0115)0.60030.59973335

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

31

CACE (Model 1)
0.0263(0.0214)−0.0569∗∗∗(0.0078)
0.5529∗∗∗(0.0156)−0.0537∗∗∗(0.0076)
0.0813∗∗∗(0.0116)0.6006
0.59993237

CACE (Model 2)
0.0373(0.0258)−0.0590∗∗∗(0.0079)
0.5635∗∗∗(0.0160)−0.0503∗∗∗(0.0077)0.0750∗∗∗(0.0118)
0.60610.60543108

Table 36: Testing Effect of Intervention on Affective Polarization with Covariate-Adjusted Models (HC2
Robust standard errors)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0276(0.5763)0.8702∗∗∗(0.0095)
0.71590.71573193

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

32

CACE (Model 1)
0.0602(0.6108)
0.8675∗∗∗(0.0097)
0.71730.71723103

CACE (Model 2)
0.0811(0.7286)0.8678∗∗∗(0.0098)
0.71980.71962987

Table 37: Testing Effect of Intervention on Whether They Believe “Fake News is a Problem” with CovariateAdjusted Models (HC2 Robust standard errors)

Pre-Treatment Value
Treatment
Party ID
Trust in Media
Ideology
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.5486∗∗∗(0.0175)−0.0093(0.0224)0.0159(0.0084)−0.1056∗∗∗(0.0172)
0.0317∗∗∗(0.0088)
0.39840.39753335

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

33

CACE (Model 1)
0.5483∗∗∗(0.0177)−0.0122(0.0239)0.0198∗(0.0084)−0.1039∗∗∗(0.0175)
0.0292∗∗∗(0.0088)0.40130.4004
3237

CACE (Model 2)0.5582∗∗∗(0.0181)−0.0118(0.0289)0.0201∗(0.0086)−0.1055∗∗∗(0.0180)0.0271∗∗(0.0091)0.41090.40993108

Table 38: Testing Effect of Intervention on Whether They Believe “Fake News is a Problem in the Mainstream
Media” with Covariate-Adjusted Models (HC2 Robust standard errors)

Pre-Treatment Value
Treatment
Party ID
Trust in Media
Ideology
Trust of news in newspapers
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.5867∗∗∗(0.0205)−0.0426(0.0293)0.0635∗∗∗(0.0127)−0.2554∗∗∗(0.0275)
0.0869∗∗∗(0.0129)−0.1062∗∗∗(0.0192)
0.73550.73503334

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

34

CACE (Model 1)
0.5917∗∗∗(0.0209)−0.0416(0.0313)0.0608∗∗∗(0.0130)−0.2570∗∗∗(0.0278)0.0844∗∗∗(0.0131)−0.1059∗∗∗(0.0195)
0.73620.73573236

CACE (Model 2)0.5935∗∗∗(0.0213)−0.0361(0.0376)
0.0618∗∗∗(0.0130)−0.2579∗∗∗(0.0283)0.0801∗∗∗(0.0134)−0.1089∗∗∗(0.0199)0.73970.73923107

Table 39: Testing Effect of Intervention on Trust in Institutions with Covariate-Adjusted Models (HC2
Robust standard errors)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.2431(0.3756)
0.7389∗∗∗(0.0133)
0.58700.58673007

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

35

CACE (Model 1)
0.3362(0.3984)0.7419∗∗∗(0.0131)0.59370.59342922

CACE (Model 2)
0.2293(0.4781)0.7438∗∗∗(0.0134)
0.59220.59192815

Table 40: Testing Effect of Intervention on Trust in CBS with Covariate-Adjusted Models (HC2 Robust
standard errors)

Treatment
Pre-Treatment Value
Party ID
Trust in Media
IdeologyNews consumption (network news)
Trust of news in newspapers sm
Trust of news in newspapers
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0440(0.0281)0.4610∗∗∗(0.0214)−0.0417∗∗∗(0.0105)
0.1899∗∗∗(0.0215)−0.0485∗∗∗(0.0111)0.0711∗∗∗(0.0101)
0.0031∗∗∗(0.0006)
0.0485∗∗(0.0187)
0.57390.57262472

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

36

CACE (Model 1)
0.0411(0.0299)
0.4626∗∗∗(0.0216)−0.0415∗∗∗(0.0105)
0.1903∗∗∗(0.0217)−0.0471∗∗∗(0.0113)0.0733∗∗∗(0.0102)0.0032∗∗∗(0.0006)
0.0497∗∗(0.0189)
0.57670.57532401

CACE (Model 2)
0.0440(0.0358)
0.4567∗∗∗(0.0220)−0.0448∗∗∗(0.0107)0.1818∗∗∗(0.0222)−0.0460∗∗∗(0.0115)
0.0719∗∗∗(0.0104)
0.0031∗∗∗(0.0007)0.0603∗∗(0.0193)
0.57700.57552304

Table 41: Testing Effect of Intervention on Trust in ABC with Covariate-Adjusted Models (HC2 Robust
standard errors)

Treatment
Pre-Treatment Value
Party ID
Trust in Media
IdeologyNews consumption (network news)
Trust of news in newspapers sm
Trust of news in newspapers
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0303(0.0284)
0.4644∗∗∗(0.0222)−0.0554∗∗∗(0.0108)
0.1521∗∗∗(0.0229)−0.0526∗∗∗(0.0114)0.0636∗∗∗(0.0103)
0.0033∗∗∗(0.0006)
0.0347(0.0194)
0.56480.56342451

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

37

CACE (Model 1)
0.0301(0.0300)
0.4719∗∗∗(0.0223)−0.0539∗∗∗(0.0108)
0.1522∗∗∗(0.0228)−0.0520∗∗∗(0.0116)0.0653∗∗∗(0.0104)
0.0033∗∗∗(0.0006)
0.0334(0.0196)0.57090.56942382

CACE (Model 2)
0.0349(0.0361)0.4748∗∗∗(0.0228)−0.0585∗∗∗(0.0111)0.1329∗∗∗(0.0230)−0.0513∗∗∗(0.0119)0.0646∗∗∗(0.0106)
0.0033∗∗∗(0.0006)
0.0408∗(0.0199)0.57550.57402289

Table 42: Testing Effect of Intervention on Trust in NBC with Covariate-Adjusted Models (HC2 Robust
standard errors)

Treatment
Pre-Treatment Value
Party ID
Trust in Media
IdeologyNews consumption (network news)
Trust of news in newspapers sm
Trust of news in newspapers
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0238(0.0272)0.4703∗∗∗(0.0220)−0.0536∗∗∗(0.0105)0.1856∗∗∗(0.0226)−0.0515∗∗∗(0.0112)0.0614∗∗∗(0.0099)
0.0031∗∗∗(0.0006)
0.0440∗(0.0184)
0.60640.60512447

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

38

CACE (Model 1)
0.0283(0.0287)
0.4756∗∗∗(0.0220)−0.0514∗∗∗(0.0106)
0.1971∗∗∗(0.0230)−0.0499∗∗∗(0.0114)0.0613∗∗∗(0.0099)
0.0031∗∗∗(0.0006)
0.0422∗(0.0184)
0.61370.6124
2379

CACE (Model 2)
0.0296(0.0346)
0.4762∗∗∗(0.0223)−0.0537∗∗∗(0.0106)
0.1806∗∗∗(0.0233)−0.0503∗∗∗(0.0116)
0.0611∗∗∗(0.0101)
0.0030∗∗∗(0.0006)
0.0507∗∗(0.0188)0.61490.61362287

Table 43: Testing Effect of Intervention on Trust in CNN with Covariate-Adjusted Models (HC2 Robust
standard errors)

Treatment
Pre-Treatment Value
Party ID
Trust in Media
Ideology
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0149(0.0249)
0.5751∗∗∗(0.0182)−0.0753∗∗∗(0.0104)
0.1922∗∗∗(0.0200)−0.0375∗∗∗(0.0108)
0.66010.65952887

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

39

CACE (Model 1)
0.0196(0.0266)
0.5744∗∗∗(0.0183)−0.0757∗∗∗(0.0106)
0.1935∗∗∗(0.0203)−0.0376∗∗∗(0.0111)
0.66180.66122806

CACE (Model 2)
0.0248(0.0320)0.5639∗∗∗(0.0188)−0.0782∗∗∗(0.0109)0.2006∗∗∗(0.0209)−0.0373∗∗(0.0113)0.6636
0.66302691

Table 44: Testing Effect of Intervention on Trust in Fox News with Covariate-Adjusted Models (HC2 Robust
standard errors)

Treatment
Pre-Treatment Value
Party ID
Ideology
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0026(0.0231)0.6468∗∗∗(0.0176)
0.0263∗∗(0.0086)
0.0734∗∗∗(0.0095)
0.60460.60402883

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

40

CACE (Model 1)−0.0012(0.0243)0.6427∗∗∗(0.0179)
0.0277∗∗(0.0086)
0.0752∗∗∗(0.0097)
0.60780.60732805

CACE (Model 2)−0.0100(0.0294)
0.6449∗∗∗(0.0182)
0.0254∗∗(0.0088)
0.0758∗∗∗(0.0099)
0.60840.60792692

DResults From All Covariate-Unadjusted Models

D.1

Behavioral Measures (Before July 1st)

Table 45: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0005(0.0071)0.0000−0.0012857

CACE (Model 1)0.0006(0.0075)0.0000−0.0011841

CACE (Model 2)−0.0006(0.0086)−0.0000−0.0013818

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 46: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0110(0.0215)0.0003−0.0009
857

CACE (Model 1)−0.0105(0.0225)0.0005−0.0007
841

CACE (Model 2)−0.0185(0.0257)
0.0005−0.0007818

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 47: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0290(0.0395)
0.0006−0.0005
904

CACE (Model 1)
0.0338(0.0414)0.00120.0001885

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

41

CACE (Model 2)
0.0373(0.0475)0.00150.0004860

Table 48: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0161(0.0597)0.0001−0.0010904

CACE (Model 1)−0.0029(0.0623)−0.0001−0.0012885

CACE (Model 2)−0.0034(0.0717)−0.0001−0.0012860

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 49: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0549(0.5771)0.0000−0.0012857

CACE (Model 1)0.1503(0.6023)0.0000−0.0012841

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

42

CACE (Model 2)0.1080(0.6867)−0.0001−0.0013818

D.2

Behavioral Measures (After July 1st)

Table 50: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0016(0.0074)0.0001−0.0012813

CACE (Model 1)−0.0013(0.0078)−0.0000−0.0013800

CACE (Model 2)−0.0010(0.0091)
0.0002−0.0010774

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 51: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0111(0.0223)0.0003−0.0009813

CACE (Model 1)
0.0129(0.0233)−0.0001−0.0013800

CACE (Model 2)0.0094(0.0269)0.0002−0.0011774

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 52: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0090(0.0326)0.0001−0.0010884

CACE (Model 1)
0.0114(0.0342)0.0003−0.0008
867

CACE (Model 2)
0.0200(0.0395)
0.0000−0.0012839

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 53: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0403(0.0578)0.0005−0.0006
884

CACE (Model 1)−0.0270(0.0602)−0.0002−0.0013867

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

43

CACE (Model 2)−0.0368(0.0701)−0.0011−0.0023
839

Table 54: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.1774(0.5990)0.0001−0.0011813

CACE (Model 1)
0.2296(0.6268)0.0000−0.0012800

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

44

CACE (Model 2)0.1439(0.7222)0.0004−0.0009774

D.3

Behavioral Measures - Weighted by Duration (Before July 1st)

Table 55: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st) – Duration Weighted

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0036(0.0075)0.0003−0.0009856

CACE (Model 1)−0.0034(0.0079)−0.0001−0.0013840

CACE (Model 2)−0.0053(0.0090)−0.0003−0.0015817

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 56: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st) – Duration Weighted

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0035(0.0232)
0.0000−0.0011856

CACE (Model 1)−0.0023(0.0242)0.0000−0.0012840

CACE (Model 2)−0.0126(0.0276)−0.0000−0.0012817

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 57: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st) – Duration Weighted

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−457.2273(350.1165)
0.00280.0017
904

CACE (Model 1)−475.0275(370.3970)
0.00200.0008885

CACE (Model 2)−554.2217(433.1678)−0.0004−0.0016860

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 58: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st) – Duration Weighted

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)
1578.2085(4285.7158)
0.0001−0.0010904

CACE (Model 1)
1734.1659(4539.8499)
0.0003−0.0008885

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

45

CACE (Model 2)2373.7616(5303.0171)
0.0008−0.0004860

Table 59: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (Before July 1st) – Duration Weighted

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.4935(0.6256)0.0007−0.0004
856

CACE (Model 1)
0.5961(0.6540)0.0011−0.0001840

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

46

CACE (Model 2)
0.5397(0.7463)0.0006−0.0006
817

D.4

Behavioral Measures - Weighted by Duration (After July 1st)

Table 60: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st) – Duration Weighted

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0049(0.0083)0.0005−0.0008811

CACE (Model 1)−0.0050(0.0087)
0.0000−0.0012798

CACE (Model 2)−0.0055(0.0102)0.0012−0.0001772

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 61: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st) – Duration Weighted

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0138(0.0240)0.0004−0.0008811

CACE (Model 1)
0.0176(0.0250)0.0003−0.0010798

CACE (Model 2)
0.0137(0.0288)
0.0006−0.0007772

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 62: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st) – Duration Weighted

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−123.5642(162.4097)
0.0010−0.0002
884

CACE (Model 1)−124.8044(171.1623)
0.0005−0.0006
867

CACE (Model 2)−140.8097(201.6918)−0.0004−0.0016839

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 63: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st) – Duration Weighted

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)1827.2807(1762.6270)
0.0010−0.0001
884

CACE (Model 1)
2006.9349(1860.7421)
0.00150.0004867

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

47

CACE (Model 2)2438.3461(2188.5690)
0.00310.0019839

Table 64: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (After July 1st) – Duration Weighted

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.5698(0.6663)0.0009−0.0003811

CACE (Model 1)
0.6748(0.6985)0.0009−0.0004798

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

48

CACE (Model 2)0.6405(0.8020)0.00240.0011772

D.5

Behavioral Measures - Referrals from Social Media Sites and Search Engines (Before July 1st)

Table 65: Testing the Effect of the Intervention on Proportion of News Diet of Referrals From Search Engines
and Social Media (Google, Twitter, and Facebook) That is Unreliable with Covariate-Adjusted Models (HC2
Robust standard errors) (Before July 1st)

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0012(0.0082)0.0000−0.0013733

CACE (Model 1)−0.0018(0.0087)
0.0000−0.0014721

CACE (Model 2)−0.0032(0.0098)−0.0002−0.0017700

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 66: Testing the Effect of the Intervention on Proportion of News Diet of Referrals From Search Engines
and Social Media (Google, Twitter, and Facebook) That is Reliable with Covariate-Adjusted Models (HC2
Robust standard errors) (Before July 1st)

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0284(0.0216)0.00230.0010733

CACE (Model 1)−0.0273(0.0225)0.00190.0005
721

CACE (Model 2)−0.0364(0.0257)0.00180.0003700

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 67: Testing the Effect of the Intervention on Count of Unreliable News Referred From Search Engines
and Social Media (Google, Twitter, and Facebook) with Covariate-Adjusted Models (HC2 Robust standard
errors) (Before July 1st)

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0117(0.0252)0.0003−0.0010770

CACE (Model 1)−0.0139(0.0262)0.0001−0.0012757

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

49

CACE (Model 2)−0.0147(0.0298)−0.0003−0.0017735

Table 68: Testing the Effect of the Intervention on Count of Reliable News Referred From Search Engines
and Social Media (Google, Twitter, and Facebook) with Covariate-Adjusted Models (HC2 Robust standard
errors) (Before July 1st)

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0481(0.0497)0.0012−0.0001770

CACE (Model 1)−0.0447(0.0516)0.0010−0.0003
757

CACE (Model 2)−0.0604(0.0595)
0.00290.0015735

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 69: Testing the Effect of the Intervention on Reliability Score of News Referred From Search Engines
and Social Media (Google, Twitter, and Facebook) with Covariate-Adjusted Models (HC2 Robust standard
errors) (Before July 1st)

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.3393(0.7678)0.0003−0.0011
733

CACE (Model 1)
0.4237(0.8001)
0.0009−0.0005721

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

50

CACE (Model 2)0.2932(0.9142)0.0000−0.0014700

D.6

Behavioral Measures - Referrals from Social Media Sites and Search Engines (After July 1st)

Table 70: Testing the Effect of the Intervention on Proportion of News Diet of Referrals From Search Engines
and Social Media (Google, Twitter, and Facebook) That is Unreliable with Covariate-Adjusted Models (HC2
Robust standard errors) (After July 1st)

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0088(0.0081)0.00160.0000630

CACE (Model 1)
0.0087(0.0085)
0.00230.0007
620

CACE (Model 2)0.0093(0.0098)
0.0006−0.0011598

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 71: Testing the Effect of the Intervention on Proportion News Diet of Referrals From Search Engines
and Social Media (Google, Twitter, and Facebook) That is Reliable with Covariate-Adjusted Models (HC2
Robust standard errors) (After July 1st)

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0117(0.0234)0.0004−0.0012630

CACE (Model 1)
0.0156(0.0243)0.0002−0.0014620

CACE (Model 2)0.0108(0.0279)0.0007−0.0009598

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 72: Testing the Effect of the Intervention on Count of Unreliable News Referred From Search Engines
and Social Media (Google, Twitter, and Facebook) with Covariate-Adjusted Models (HC2 Robust standard
errors) (After July 1st)

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0147(0.0210)0.0008−0.0007
680

CACE (Model 1)
0.0118(0.0220)0.0010−0.0005669

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

51

CACE (Model 2)
0.0175(0.0251)0.0006−0.0009647

Table 73: Testing the Effect of the Intervention on Count of Reliable News Referred From Search Engines
and Social Media (Google, Twitter, and Facebook) with Covariate-Adjusted Models (HC2 Robust standard
errors) (After July 1st)

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0462(0.0501)
0.0013−0.0002
680

CACE (Model 1)−0.0410(0.0520)0.00160.0001
669

CACE (Model 2)−0.0679(0.0601)
0.00270.0011647

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 74: Testing the Effect of the Intervention on Reliability Score of News Referred From Search Engines
and Social Media (Google, Twitter, and Facebook) with Covariate-Adjusted Models (HC2 Robust standard
errors) (After July 1st)

TreatedR2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.5662(0.8032)
0.0007−0.0008
630

CACE (Model 1)−0.4744(0.8397)0.0011−0.0005620

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

52

CACE (Model 2)−0.6503(0.9523)−0.0002−0.0018
598

D.7

Attitudinal Measures

Table 75: Testing Effect of Intervention on Belief in Misinformation about the Black Lives Matter Movementwith Covariate-Unadjusted Models (HC2 Robust standard errors)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0728∗(0.0323)0.00160.0013
3170

CACE (Model 1)−0.0764∗(0.0344)0.00320.00293079

CACE (Model 2)−0.0939∗(0.0414)0.00630.00602962

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 76: Testing Effect of Intervention on Belief in True Information about the Black Lives Matter Movementwith Covariate-Unadjusted Models (HC2 Robust standard errors)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0004(0.0285)0.0000−0.0003
3195

CACE (Model 1)
0.0110(0.0305)−0.0000−0.0003
3105

CACE (Model 2)
0.0162(0.0366)
0.0002−0.00012983

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 77: Testing Effect of Intervention on Belief in Misinformation about Covid-19 with CovariateUnadjusted Models (HC2 Robust standard errors)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0170(0.0288)
0.0001−0.0002
3263

CACE (Model 1)−0.0125(0.0304)
0.00050.0002
3169

CACE (Model 2)−0.0059(0.0363)0.00050.00013044

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 78: Testing Effect of Intervention on Belief in True Information about Covid-19 with CovariateUnadjusted Models (HC2 Robust standard errors)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0076(0.0196)0.0000−0.0003
3270

CACE (Model 1)
0.0202(0.0209)
0.00070.00043174

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

53

CACE (Model 2)
0.0218(0.0249)0.00160.00133050

Table 79: Testing Effect of Intervention on Trust in Media with Covariate-Unadjusted Models (HC2 Robust
standard errors)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0478(0.0316)0.00070.00043337

CACE (Model 1)
0.0413(0.0338)0.00110.00083238

CACE (Model 2)
0.0627(0.0410)0.00220.00193109

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 80: Testing Effect of Intervention on Affective Polarization with Covariate-Unadjusted Models (HC2
Robust standard errors)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.3371(1.0770)0.0000−0.0003
3237

CACE (Model 1)−0.3549(1.1467)0.0000−0.0003
3143

CACE (Model 2)−0.8156(1.3733)
0.0002−0.00013021

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 81: Testing Effect of Intervention on Whether They Believe “Fake News is a Problem” with CovariateUnadjusted Models (HC2 Robust standard errors)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0107(0.0288)
0.0000−0.0003
3337

CACE (Model 1)−0.0143(0.0309)
0.0002−0.0001
3238

CACE (Model 2)−0.0143(0.0375)0.0002−0.00023109

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 82: Testing Effect of Intervention on Whether They Believe “Fake News is a Problem in the Mainstream
Media” with Covariate-Unadjusted Models (HC2 Robust standard errors)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0455(0.0580)0.0002−0.0001
3336

CACE (Model 1)−0.0449(0.0623)0.00050.00023237

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

54

CACE (Model 2)−0.0564(0.0752)0.00110.00083108

Table 83: Testing Effect of Intervention on Trust in Institutions with Covariate-Unadjusted Models (HC2
Robust standard errors)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.6346(0.5630)0.00040.0001
3134

CACE (Model 1)−0.7537(0.6021)0.00100.00073042

CACE (Model 2)−1.0348(0.7237)0.00120.00092928

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 84: Testing Effect of Intervention on Trust in CBS with Covariate-Unadjusted Models (HC2 Robust
standard errors)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0634(0.0371)0.00090.00063337

CACE (Model 1)
0.0663(0.0398)
0.00110.0007
3238

CACE (Model 2)
0.0761(0.0480)0.00170.00143109

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 85: Testing Effect of Intervention on Trust in ABC with Covariate-Unadjusted Models (HC2 Robust
standard errors)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0569(0.0371)0.00070.00043337

CACE (Model 1)
0.0652(0.0397)0.00070.0004
3238

CACE (Model 2)
0.0825(0.0480)0.00150.00123109

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 86: Testing Effect of Intervention on Trust in NBC with Covariate-Unadjusted Models (HC2 Robust
standard errors)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0542(0.0378)0.00060.00033337

CACE (Model 1)
0.0588(0.0405)
0.00090.00063238

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

55

CACE (Model 2)
0.0738(0.0489)0.00190.00153109

Table 87: Testing Effect of Intervention on Trust in CNN with Covariate-Unadjusted Models (HC2 Robust
standard errors)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0338(0.0410)0.0002−0.0001
3336

CACE (Model 1)
0.0357(0.0440)0.00030.00003237

CACE (Model 2)
0.0462(0.0532)0.00100.00073108

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 88: Testing Effect of Intervention on Trust in Fox News with Covariate-Unadjusted Models (HC2
Robust standard errors)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0108(0.0388)
0.0000−0.00033336

CACE (Model 1)−0.0162(0.0415)0.00050.00013237

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

56

CACE (Model 2)−0.0285(0.0501)
0.00110.00083108

E

Results From Covariate-Adjusted Model Testing Hypotheses
About Moderators

Table 89: Testing Effect of Intervention Initial level of Affective Polarization Moderator on Effect on Affective
Polarization (Covariate-Adjusted)

Treatment
Moderator
Party ID
Race/Ethnicity
GenderAge
Trust in MediaNews consumption (cable news)
News consumption (print news)
News consumption (talk radio)
News consumption (news on desktop)
Treatment*Moderator
R2
Adj. R2
Num. obs.
∗∗∗

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

57

Affective Polarization−0.6305(1.4245)0.8388∗∗∗(0.0159)−0.2634(0.1764)
1.1935(0.7890)0.2779(0.5622)0.1347∗∗∗(0.0213)−0.4657(0.4451)
0.3519(0.1871)−0.6449∗∗(0.2290)0.0024(0.2337)0.5342∗(0.2249)
0.0130(0.0193)0.72240.72143192

Table 90: Testing Effect of Intervention Initial level of Trust In Media (Inverse) Moderator on Effect on
Trust In Media (Covariate-Adjusted)

Treatment
Moderator
Party ID
Ideology
Trust of news in newspapers
Treatment*Moderator
R2
Adj. R2
Num. obs.
∗∗∗

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

58

Trust in Media
0.0104(0.0356)−0.5641∗∗∗(0.0206)−0.0550∗∗∗(0.0076)−0.0564∗∗∗(0.0075)
0.0768∗∗∗(0.0115)0.0145(0.0223)0.60030.59963335

F

Results From Covariate-Unadjusted Model Testing Hypotheses
Regarding Moderators

Table 91: Testing Effect of Intervention Initial level of Affective Polarization Moderator on Effect on Affective
Polarization (Covariate-Adjusted)
Affective Polarization−0.7565(1.4484)0.8626∗∗∗(0.0153)
0.0130(0.0196)
0.71590.7156
3193

Treatment
Moderator
Treatment*Moderator
R2
Adj. R2
Num. obs.
∗∗∗

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

59

Table 92: Testing Effect of Intervention Initial level of Trust In Media (Inverse) Moderator on Effect on
Trust In Media (Covariate-Adjusted)

Treatment
Moderator
Treatment*Moderator
R2
Adj. R2
Num. obs.
∗∗∗

Trust in Media
0.0100(0.0366)−0.7614∗∗∗(0.0175)
0.0237(0.0228)0.54810.54773337

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

60

G

NewsGuard Indicators of Source Reliability
Figure 4: Different NewsGuard Indicators of Source Quality

61

H

Minimum Detectable Effects for Covariate-adjusted ITT and
CACE Models

Table 93: Minimum Detectable Effect of Attitudinal Measures assuming power=0.80 and 95 percent Statistical Significance inboth the covariate-adjusted Intent-To-Treat and CACE model using the strongest measure
Variable
Affective Polarization
Whether They Believe “Fake News is a Problem”
Whether They Believe “Fake News is a Problem in the Mainstream Media”
Trust in Institutions
Trust in CBS
Trust in ABC
Trust in NBC
Trust in CNN
Trust in Fox News
Belief in Misinformation about the Black Lives Matter Movement
Belief in True Information about the Black Lives Matter Movement
Belief in Misinformation about Covid-19
Belief in True Information about Covid-19
Trust in Media

MDE
(ITT)
0.037
0.053
0.035
0.046
0.052
0.052
0.050
0.043
0.046
0.065
0.065
0.062
0.065
0.043

MDE
(CACE)
0.003
0.140
0.052
0.007
0.115
0.115
0.109
0.091
0.111
0.150
0.160
0.163
0.233
0.113

Table 94: Minimum Detectable Effect of Behavioral Measures assuming power=0.80 and 95 percent Statistical Significance inboth the covariate-adjusted Intent-To-Treat and CACE model using the strongest measure
Variable

MDE
(ITT)
0.060
0.080
0.072
0.050
0.080
0.070
0.089
0.081
0.073
0.088

Proportion of News Diet That is Unreliable
Proportion of News Diet That is Reliable
Count of Unreliable News Consumed
Count of Reliable News Consumed
Avg. Reliability Score of News Diet
Proportion of News Diet That is Unreliable
Proportion of News Diet That is Reliable
Count of Unreliable News Consumed
Count of Reliable News Consumed
Avg. Reliability Score of News Diet

62

MDE
(CACE)
1.349
0.576
0.272
0.145
0.021
1.534
0.630
0.379
0.204
0.024

I

Effect of Intervention on Behavioral Measures Using Different
Samples of Respondents

Table 95: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with Covariate-Adjusted Models(HC2 Robust standard errors) (Before July 1st) on Different Samples of Respondents by Decile of their Quality of News
Consumption Pre-Treatment (Average Reliability Score of News)
DecileThreshold

ITT Model

Bottom 10 Percent
Bottom 20 Percent
Bottom 30 Percent
Bottom 40 Percent
Bottom 50 Percent
Bottom 60 Percent
Bottom 70 Percent
Bottom 80 Percent
Bottom 90 Percent

78.25585.34787.48288.38389.1679090.77392.46295-0.021 (0.023)-0.011 (0.013)-0.007 (0.009)-0.006 (0.007)-0.004 (0.005)-0.003 (0.004)-0.003 (0.004)-0.003 (0.003)-0.003 (0.003)∗∗∗

CACE Model (WeakCompliance Measure)-0.025 (0.025)-0.013 (0.014)-0.008 (0.009)-0.006 (0.007)-0.005 (0.005)-0.004 (0.004)-0.004 (0.004)-0.003 (0.003)-0.003 (0.003)

CACE Model (Strong
Compliance Measure)-0.03 (0.029)-0.015 (0.016)-0.009 (0.01)-0.007 (0.008)-0.005 (0.006)-0.004 (0.005)-0.004 (0.005)-0.004 (0.004)-0.004 (0.004)

Sample Size
78
157
237
315
396
493
549
629
712

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 96: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with Covariate-Adjusted Models(HC2 Robust standard errors) (Before July 1st) on Different Samples of Respondents by Decile of their Quality of News
Consumption Pre-Treatment (Average Reliability Score of News)
DecileThreshold

ITT Model

Bottom 10 Percent
Bottom 20 Percent
Bottom 30 Percent
Bottom 40 Percent
Bottom 50 Percent
Bottom 60 Percent
Bottom 70 Percent
Bottom 80 Percent
Bottom 90 Percent

78.25585.34787.48288.38389.1679090.77392.46295-0.007 (0.03)
0 (0.029)
0.01 (0.022)
0.029 (0.019)
0.034 (0.017)*
0.013 (0.016)
0.003 (0.015)
0.001 (0.013)-0.008 (0.013)∗∗∗

CACE Model (WeakCompliance Measure)-0.004 (0.032)
0.004 (0.031)
0.011 (0.023)
0.032 (0.02)
0.036 (0.018)*
0.015 (0.016)
0.005 (0.016)
0.003 (0.014)-0.007 (0.013)

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

63

CACE Model (Strong
Compliance Measure)-0.007 (0.037)
0.003 (0.035)
0.009 (0.026)
0.029 (0.023)
0.035 (0.02)
0.015 (0.018)
0.004 (0.018)
0.001 (0.016)-0.008 (0.015)

Sample Size
78
157
237
315
396
493
549
629
712

Table 97: Testing the Effect of the Intervention on Count of Unreliable News Consumed with Covariate-Adjusted Models (HC2
Robust standard errors) (Before July 1st) on Different Samples of Respondents by Decile of their Quality of News Consumption
Pre-Treatment (Average Reliability Score of News)
DecileThreshold

ITT Model

Bottom 10 Percent
Bottom 20 Percent
Bottom 30 Percent
Bottom 40 Percent
Bottom 50 Percent
Bottom 60 Percent
Bottom 70 Percent
Bottom 80 Percent
Bottom 90 Percent

78.25585.34787.48288.38389.1679090.77392.46295

0.029 (0.086)
0.083 (0.068)
0.076 (0.048)
0.07 (0.041)
0.04 (0.035)
0.044 (0.03)
0.037 (0.028)
0.033 (0.026)
0.035 (0.024)∗∗∗

CACE Model (WeakCompliance Measure)0.02 (0.092)
0.082 (0.072)
0.076 (0.051)
0.073 (0.043)
0.041 (0.036)
0.049 (0.032)
0.041 (0.03)
0.037 (0.027)
0.039 (0.025)

CACE Model (Strong
Compliance Measure)0.029 (0.107)
0.1 (0.081)
0.088 (0.056)
0.08 (0.049)
0.044 (0.041)
0.054 (0.036)
0.045 (0.034)
0.041 (0.031)
0.044 (0.029)

Sample Size
79
159
239
317
398
495
551
631
714

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 98: Testing the Effect of the Intervention on Count of Reliable News Consumed with Covariate-Adjusted Models (HC2
Robust standard errors) (Before July 1st) on Different Samples of Respondents by Decile of their Quality of News Consumption
Pre-Treatment (Average Reliability Score of News)
DecileThreshold

ITT Model

Bottom 10 Percent
Bottom 20 Percent
Bottom 30 Percent
Bottom 40 Percent
Bottom 50 Percent
Bottom 60 Percent
Bottom 70 Percent
Bottom 80 Percent
Bottom 90 Percent

78.25585.34787.48288.38389.1679090.77392.46295

0.038 (0.051)
0.02 (0.038)
0.001 (0.034)
0.022 (0.029)
0.029 (0.025)
0.017 (0.023)
0.009 (0.021)
0.009 (0.02)-0.002 (0.019)∗∗∗

CACE Model (WeakCompliance Measure)0.044 (0.056)
0.023 (0.041)
0 (0.036)
0.022 (0.03)
0.03 (0.026)
0.019 (0.024)
0.01 (0.022)
0.01 (0.021)-0.002 (0.02)

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

64

CACE Model (Strong
Compliance Measure)0.049 (0.065)
0.024 (0.046)-0.009 (0.039)
0.016 (0.034)
0.025 (0.029)
0.022 (0.027)
0.011 (0.025)
0.01 (0.023)-0.002 (0.022)

Sample Size
79
159
239
317
398
495
551
631
714

Table 99: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted Models (HC2
Robust standard errors) (Before July 1st) on Different Samples of Respondents by Decile of their Quality of News Consumption
Pre-Treatment (Average Reliability Score of News)
DecileThreshold

ITT Model

Bottom 10 Percent
Bottom 20 Percent
Bottom 30 Percent
Bottom 40 Percent
Bottom 50 Percent
Bottom 60 Percent
Bottom 70 Percent
Bottom 80 Percent
Bottom 90 Percent

78.25585.34787.48288.38389.1679090.77392.46295

3.707 (1.824)*
1.87 (1.201)
1.19 (0.799)
1.004 (0.614)
0.599 (0.516)
0.558 (0.423)
0.44 (0.382)
0.444 (0.343)
0.619 (0.336)∗∗∗

CACE Model (WeakCompliance Measure)4.129 (1.891)*
2.045 (1.238)
1.277 (0.811)
1.083 (0.63)
0.644 (0.527)
0.602 (0.436)
0.476 (0.396)
0.477 (0.354)
0.655 (0.349)

CACE Model (Strong
Compliance Measure)4.415 (2.139)*
2.15 (1.386)
1.351 (0.904)
1.106 (0.714)
0.63 (0.593)
0.653 (0.497)
0.515 (0.453)
0.518 (0.404)
0.714 (0.4)

Sample Size
78
157
237
315
396
493
549
629
712

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 100: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with Covariate-Adjusted
Models (HC2 Robust standard errors) (After July 1st) on Different Samples of Respondents by Decile of their Quality of News
Consumption Pre-Treatment (Average Reliability Score of News)
DecileThreshold

ITT Model

Bottom 10 Percent
Bottom 20 Percent
Bottom 30 Percent
Bottom 40 Percent
Bottom 50 Percent
Bottom 60 Percent
Bottom 70 Percent
Bottom 80 Percent
Bottom 90 Percent

78.25585.34787.48288.38389.1679090.77392.46295-0.039 (0.027)-0.025 (0.016)-0.013 (0.011)-0.008 (0.008)-0.007 (0.006)-0.008 (0.005)-0.007 (0.005)-0.006 (0.004)-0.005 (0.004)∗∗∗

CACE Model (WeakCompliance Measure)-0.039 (0.029)-0.026 (0.017)-0.012 (0.011)-0.007 (0.008)-0.007 (0.007)-0.008 (0.006)-0.007 (0.005)-0.006 (0.005)-0.005 (0.004)

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

65

CACE Model (Strong
Compliance Measure)-0.034 (0.033)-0.023 (0.019)-0.011 (0.012)-0.006 (0.01)-0.006 (0.008)-0.006 (0.006)-0.005 (0.006)-0.005 (0.005)-0.003 (0.005)

Sample Size
70
141
215
289
369
459
511
585
663

Table 101: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with Covariate-Adjusted Models (HC2 Robust standard errors) (After July 1st) on Different Samples of Respondents by Decile of their Quality of News
Consumption Pre-Treatment (Average Reliability Score of News)
DecileThreshold

ITT Model

Bottom 10 Percent
Bottom 20 Percent
Bottom 30 Percent
Bottom 40 Percent
Bottom 50 Percent
Bottom 60 Percent
Bottom 70 Percent
Bottom 80 Percent
Bottom 90 Percent

78.25585.34787.48288.38389.1679090.77392.46295

0.036 (0.034)
0.023 (0.033)
0.036 (0.026)
0.039 (0.021)
0.06 (0.019)**0.034 (0.018)
0.024 (0.017)
0.01 (0.016)
0.003 (0.015)∗∗∗

CACE Model (WeakCompliance Measure)0.038 (0.036)
0.025 (0.035)
0.037 (0.027)
0.041 (0.022)
0.063 (0.02)**
0.036 (0.019)
0.026 (0.018)
0.011 (0.017)
0.005 (0.015)

CACE Model (Strong
Compliance Measure)0.023 (0.04)
0.021 (0.039)
0.033 (0.03)
0.036 (0.025)
0.063 (0.022)**0.035 (0.021)
0.023 (0.02)
0.007 (0.019)
0 (0.018)

Sample Size
70
141
215
289
369
459
511
585
663

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 102: Testing the Effect of the Intervention on Count of Unreliable News Consumed with Covariate-Adjusted Models (HC2
Robust standard errors) (After July 1st) on Different Samples of Respondents by Decile of their Quality of News Consumption
Pre-Treatment (Average Reliability Score of News)
DecileThreshold

ITT Model

Bottom 10 Percent
Bottom 20 Percent
Bottom 30 Percent
Bottom 40 Percent
Bottom 50 Percent
Bottom 60 Percent
Bottom 70 Percent
Bottom 80 Percent
Bottom 90 Percent

78.25585.34787.48288.38389.1679090.77392.46295-0.163 (0.104)-0.042 (0.072)
0.001 (0.05)
0.014 (0.04)-0.005 (0.033)-0.004 (0.028)-0.007 (0.027)-0.001 (0.024)
0.007 (0.022)∗∗∗

CACE Model (WeakCompliance Measure)-0.174 (0.112)-0.044 (0.077)
0.001 (0.053)
0.016 (0.043)-0.006 (0.035)-0.005 (0.03)-0.008 (0.028)-0.001 (0.025)
0.007 (0.023)

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

66

CACE Model (Strong
Compliance Measure)-0.189 (0.127)-0.045 (0.085)
0.004 (0.059)
0.021 (0.049)-0.004 (0.04)
0.005 (0.034)-0.001 (0.032)
0.006 (0.029)
0.016 (0.026)

Sample Size
74
147
226
302
383
479
531
608
687

Table 103: Testing the Effect of the Intervention on Count of Reliable News Consumed with Covariate-Adjusted Models (HC2
Robust standard errors) (After July 1st) on Different Samples of Respondents by Decile of their Quality of News Consumption
Pre-Treatment (Average Reliability Score of News)
DecileThreshold

ITT Model

Bottom 10 Percent
Bottom 20 Percent
Bottom 30 Percent
Bottom 40 Percent
Bottom 50 Percent
Bottom 60 Percent
Bottom 70 Percent
Bottom 80 Percent
Bottom 90 Percent

78.25585.34787.48288.38389.1679090.77392.46295-0.034 (0.094)-0.045 (0.055)
0.012 (0.05)
0.02 (0.043)
0.009 (0.039)
0.004 (0.037)
0.01 (0.035)
0.007 (0.033)-0.014 (0.031)∗∗∗

CACE Model (WeakCompliance Measure)-0.044 (0.101)-0.052 (0.059)
0.004 (0.053)
0.014 (0.046)
0.003 (0.041)
0.002 (0.039)
0.007 (0.037)
0.004 (0.035)-0.016 (0.032)

CACE Model (Strong
Compliance Measure)-0.078 (0.114)-0.07 (0.065)-0.019 (0.056)-0.004 (0.051)-0.014 (0.046)-0.01 (0.042)-0.002 (0.04)-0.004 (0.038)-0.026 (0.036)

Sample Size
74
147
225
300
379
472
524
599
678

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 104: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted Models (HC2
Robust standard errors) (After July 1st) on Different Samples of Respondents by Decile of their Quality of News Consumption
Pre-Treatment (Average Reliability Score of News)
DecileThreshold

ITT Model

Bottom 10 Percent
Bottom 20 Percent
Bottom 30 Percent
Bottom 40 Percent
Bottom 50 Percent
Bottom 60 Percent
Bottom 70 Percent
Bottom 80 Percent
Bottom 90 Percent

78.25585.34787.48288.38389.1679090.77392.46295

5.863 (1.858)**
3.187 (1.255)*
1.694 (0.855)*
1.277 (0.677)
1.194 (0.555)*
0.933 (0.475)
0.849 (0.435)
0.711 (0.4)
0.648 (0.376)∗∗∗

CACE Model (WeakCompliance Measure)
5.988 (1.892)**
3.215 (1.284)*
1.655 (0.863)
1.261 (0.694)
1.192 (0.567)*
0.941 (0.493)
0.856 (0.452)
0.712 (0.414)
0.631 (0.391)

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

67

CACE Model (Strong
Compliance Measure)5.813 (2.066)**
3.22 (1.397)*
1.665 (0.948)
1.281 (0.782)
1.221 (0.63)
1.006 (0.55)
0.921 (0.507)
0.757 (0.464)
0.662 (0.44)

Sample Size
70
141
215
289
369
459
511
585
663

J

Distribution of Pre-Treatment Behavioral Measures
Figure 5: Distribution of Average NewsGuard Reliability Score of Online News Diet (Pre-Treatment)

68

Figure 6: Distribution of Proportion of Unreliable News in Online News Diet (Pre-Treatment)69

Figure 7: Distribution of Count of Visits to Online News Sites Rated by NewsGuard (Max=2,000)(PreTreatment)

70

Figure 8: Distribution of Count of Google Search Referrals to an Online News Sites Rated by NewsGuard(Pre-Treatment)71

Figure 9: Distribution of Time Spent on Facebook (Pre-Treatment)72

Figure 10: Distribution of Time Spent on Twitter (Pre-Treatment)

73

K

Figure: Effect of Intervention on Attitudinal Measures

Figure 11: This figure presents estimates of the effect of the intervention (with 95% confidence intervals)
on our pre-registered attitudinal measures. The effect is reported in standard deviations of that measure(pre-treatment).

74

L

Figure: Proportion of the average daily proportion of unreliable
news viewed of the treatment group

Figure 12: This figure presents the average daily proportion of unreliable news viewed by the treatmentgroup across this study (with 95% confidence intervals)

75

M

Non-Pre-Registered Exploratory Results

Older Respondents (55 Plus) - Behavioral Measures (Before July 1st) - CovariateAdjusted
Table 105: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Adjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0019(0.0036)0.8639∗∗∗(0.0563)0.82410.8235
577

CACE (Model 1)−0.0023(0.0038)
0.8652∗∗∗(0.0566)
0.82410.8235566

CACE (Model 2)−0.0024(0.0045)0.8570∗∗∗(0.0592)
0.81540.8147554

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 106: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0010(0.0139)0.8096∗∗∗(0.0292)
0.66840.6673
577

CACE (Model 1)0.0012(0.0147)
0.8066∗∗∗(0.0297)
0.6642
0.6630566

CACE (Model 2)−0.0054(0.0169)
0.8138∗∗∗(0.0286)
0.66910.6679554

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 107: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0335(0.0277)
0.8662∗∗∗(0.0284)0.0449∗∗∗(0.0088)0.73100.7297589

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

76

CACE (Model 1)
0.0336(0.0294)
0.8661∗∗∗(0.0291)0.0458∗∗∗(0.0090)0.72670.7252
578

CACE (Model 2)
0.0379(0.0341)0.8567∗∗∗(0.0298)
0.0469∗∗∗(0.0092)0.72040.7189565

Table 108: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0045(0.0200)
0.4333∗∗∗(0.0484)0.2520∗∗∗(0.0214)0.90030.8998589

CACE (Model 1)
0.0046(0.0212)0.4309∗∗∗(0.0497)0.2526∗∗∗(0.0222)0.89900.8985
578

CACE (Model 2)−0.0023(0.0242)0.4287∗∗∗(0.0504)
0.2566∗∗∗(0.0225)
0.89980.8993565

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 109: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.5477(0.3718)0.8673∗∗∗(0.0362)
0.72640.7254577

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

77

CACE (Model 1)
0.6496(0.3911)0.8639∗∗∗(0.0367)
0.72400.7231
566

CACE (Model 2)0.6802(0.4560)
0.8625∗∗∗(0.0384)
0.71540.7144554

Older Respondents (55 Plus) - Behavioral Measures (After July 1st) - CovariateAdjusted
Table 110: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Adjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0015(0.0045)
0.8135∗∗∗(0.0698)
0.75260.7517
542

CACE (Model 1)−0.0012(0.0047)
0.8140∗∗∗(0.0701)
0.75240.7515
532

CACE (Model 2)0.0001(0.0055)
0.8420∗∗∗(0.0672)0.76770.7668519

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 111: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0176(0.0149)0.8010∗∗∗(0.0309)
0.63120.6298542

CACE (Model 1)
0.0195(0.0158)
0.8036∗∗∗(0.0313)0.6296
0.6282
532

CACE (Model 2)
0.0126(0.0182)
0.8130∗∗∗(0.0306)
0.6351
0.6337519

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 112: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0105(0.0242)0.7430∗∗∗(0.0255)
0.69500.6939581

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

78

CACE (Model 1)
0.0105(0.0255)0.7502∗∗∗(0.0260)
0.69530.6942570

CACE (Model 2)
0.0156(0.0301)
0.7461∗∗∗(0.0271)0.68700.6859556

Table 113: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0084(0.0313)
0.4810∗∗∗(0.0662)0.2028∗∗∗(0.0289)0.77700.7758561

CACE (Model 1)−0.0108(0.0330)
0.4817∗∗∗(0.0671)
0.2024∗∗∗(0.0293)
0.77800.7767
551

CACE (Model 2)−0.0260(0.0377)0.4779∗∗∗(0.0679)
0.2099∗∗∗(0.0297)
0.78130.7801538

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 114: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.4328(0.3862)
0.7913∗∗∗(0.0448)0.68560.6844542

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

79

CACE (Model 1)
0.4748(0.4071)
0.7950∗∗∗(0.0457)
0.68360.6824
532

CACE (Model 2)0.4481(0.4677)
0.8129∗∗∗(0.0420)0.69370.6925519

Older Respondents (55 Plus) - Behavioral Measures (Before July 1st) - CovariateUnadjusted
Table 115: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Unadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0042(0.0091)
0.0003−0.0014592

CACE (Model 1)
0.0045(0.0096)0.0007−0.0010581

CACE (Model 2)0.0038(0.0110)0.0006−0.0011567

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 116: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0177(0.0234)0.0010−0.0007592

CACE (Model 1)
0.0225(0.0247)0.0008−0.0009
581

CACE (Model 2)
0.0155(0.0285)0.0004−0.0014567

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 117: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0546(0.0516)0.00180.0002
616

CACE (Model 1)
0.0574(0.0543)0.00240.0007
604

CACE (Model 2)
0.0702(0.0624)0.00370.0020590

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 118: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0955(0.0691)
0.00310.0015
616

CACE (Model 1)
0.0995(0.0726)
0.00460.0029604

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

80

CACE (Model 2)0.1162(0.0846)0.0008−0.0009590

Table 119: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.1312(0.7196)0.0001−0.0016592

CACE (Model 1)−0.0283(0.7553)0.0000−0.0017581

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

81

CACE (Model 2)−0.1880(0.8655)
0.0006−0.0012567

Older Respondents (55 Plus) - Behavioral Measures (After July 1st) - CovariateUnadjusted
Table 120: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Unadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0038(0.0094)0.0003−0.0015574

CACE (Model 1)
0.0049(0.0099)0.0007−0.0011564

CACE (Model 2)0.0055(0.0117)−0.0010−0.0029548

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 121: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0419(0.0245)0.00530.0035574

CACE (Model 1)
0.0463(0.0257)
0.00460.0028564

CACE (Model 2)
0.0406(0.0299)
0.00340.0016548

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 122: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0235(0.0435)0.0005−0.0012607

CACE (Model 1)
0.0277(0.0457)0.0010−0.0006
596

CACE (Model 2)
0.0384(0.0531)0.0004−0.0014579

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 123: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0473(0.0672)
0.0008−0.0008
607

CACE (Model 1)
0.0535(0.0704)
0.0015−0.0002596

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

82

CACE (Model 2)
0.0647(0.0826)
0.00230.0006579

Table 124: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0437(0.7154)0.0000−0.0017574

CACE (Model 1)−0.0098(0.7522)0.0000−0.0018564

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

83

CACE (Model 2)−0.1907(0.8742)−0.0002−0.0020548

Older Respondents (35 and Younger) - Behavioral Measures (Before July 1st) Covariate-Adjusted
Table 125: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Adjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0105(0.0112)0.04670.0284
54

CACE (Model 1)−0.0108(0.0115)0.04020.0213
53

CACE (Model 2)−0.0121(0.0130)
0.0121−0.007652

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 126: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0559(0.0598)
0.8455∗∗∗(0.0668)
0.6534
0.6383
49

CACE (Model 1)
0.0584(0.0625)0.8477∗∗∗(0.0664)0.65270.637348

CACE (Model 2)
0.0672(0.0696)
0.8382∗∗∗(0.0692)
0.6336
0.616947

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 127: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Firefox Browser1
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0369(0.0850)
0.2748∗(0.1328)0.5761∗∗(0.1874)
0.4586
0.428558

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

84

CACE (Model 1)−0.0369(0.0878)
0.2748∗(0.1325)
0.5760∗∗(0.1883)
0.45390.4224
56

CACE (Model 2)−0.0459(0.1076)
0.2766∗(0.1306)
0.5775∗∗(0.1901)
0.44110.406953

Table 128: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Education
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0888(0.1013)0.0739(0.0478)0.6801∗∗∗(0.0717)0.1342∗∗∗(0.0347)
0.84200.8291
54

CACE (Model 1)−0.0938(0.1048)0.0759(0.0484)0.6801∗∗∗(0.0715)0.1357∗∗∗(0.0353)
0.83780.8243
53

CACE (Model 2)−0.1088(0.1208)
0.0653(0.0522)0.6729∗∗∗(0.0783)
0.1364∗∗(0.0397)
0.82160.806151

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 129: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−2.0415(1.5662)0.03210.0135
54

CACE (Model 1)−2.2301(1.6138)
0.03110.012153

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

85

CACE (Model 2)−2.5858(1.8464)−0.0405−0.061352

Older Respondents (35 and Younger) - Behavioral Measures (After July 1st) Covariate-Adjusted
Table 130: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Adjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Chrome Browser1
Firefox Browser1
Social Media use
Race/Ethnicity
Education
Gender
IdeologyNews consumption (cable news)
News consumption (print news)
News consumption (public radio)
News consumption (talk radio)
News consumption (news on desktop)
News consumption (news on mobile)
Trust of news in newspapers sm
Trust of news in newspapers
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0003(0.0006)
0.0007(0.0006)−0.0028(0.0015)0.0001(0.0003)0.0007(0.0006)−0.0005(0.0003)0.0002(0.0008)
0.0005∗(0.0002)−0.0003(0.0003)−0.0005(0.0004)
0.0003(0.0003)−0.0001(0.0004)
0.0010(0.0006)−0.0006∗(0.0003)0.0000(0.0000)
0.0002(0.0004)
0.4806∗∗∗(0.0341)0.0006∗(0.0002)0.98710.975439

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

86

CACE (Model 1)0.0003(0.0007)0.0006(0.0007)−0.0029(0.0015)0.0001(0.0003)0.0007(0.0007)−0.0005(0.0004)
0.0001(0.0009)
0.0005∗(0.0002)−0.0003(0.0004)−0.0005(0.0004)
0.0004(0.0004)−0.0001(0.0004)
0.0010(0.0006)−0.0006(0.0003)0.0000(0.0000)
0.0002(0.0004)
0.4801∗∗∗(0.0345)0.0006∗(0.0002)
0.98690.9744
38

CACE (Model 2)0.0003(0.0008)
0.0005(0.0009)−0.0030(0.0015)0.0002(0.0003)0.0006(0.0007)−0.0005(0.0004)
0.0002(0.0010)0.0005∗(0.0002)−0.0003(0.0004)−0.0005(0.0004)
0.0004(0.0004)−0.0000(0.0005)
0.0010(0.0006)−0.0006(0.0003)0.0000(0.0000)
0.0002(0.0004)
0.4803∗∗∗(0.0362)0.0006∗(0.0003)
0.9864
0.972737

Table 131: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.1391(0.0724)0.8644∗∗∗(0.0632)
0.75520.7427
42

CACE (Model 1)−0.1449(0.0760)
0.8574∗∗∗(0.0681)0.73350.7195
41

CACE (Model 2)−0.1621(0.0873)0.8643∗∗∗(0.0720)0.69330.676740

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 132: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0106(0.0338)0.5205∗∗(0.1875)
0.5422
0.5242
54

CACE (Model 1)−0.0104(0.0357)
0.5202∗∗(0.1883)
0.53950.5207
52

CACE (Model 2)−0.0133(0.0448)0.5205∗∗(0.1893)
0.53630.516249

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 133: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Age-Squared
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.2221(0.1794)0.0007∗(0.0003)0.7345∗∗∗(0.0986)
0.6996
0.681554

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

87

CACE (Model 1)−0.2256(0.1851)0.0007∗(0.0003)0.7249∗∗∗(0.1041)0.68950.670052

CACE (Model 2)−0.2931(0.2351)0.0008∗(0.0004)
0.7434∗∗∗(0.1153)
0.6616
0.639049

Table 134: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.8297(1.5103)
0.0056−0.0175
45

CACE (Model 1)−1.2021(1.5408)0.0127−0.010844

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

88

CACE (Model 2)−1.5125(1.7585)−0.0375−0.062843

Older Respondents (35 and Younger) - Behavioral Measures (Before July 1st) Covariate-Unadjusted
Table 135: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Unadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0105(0.0112)0.04670.0284
54

CACE (Model 1)−0.0108(0.0115)0.04020.0213
53

CACE (Model 2)−0.0121(0.0130)
0.0121−0.007652

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 136: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0071(0.1057)
0.0001−0.0192
54

CACE (Model 1)
0.0138(0.1091)−0.0001−0.0197
53

CACE (Model 2)
0.0281(0.1228)0.0011−0.018952

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 137: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0646(0.0985)0.0081−0.0088
61

CACE (Model 1)−0.0603(0.1017)0.0033−0.0142
59

CACE (Model 2)−0.0773(0.1231)−0.0196−0.0384
56

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 138: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.3748(0.3017)0.02700.0105
61

CACE (Model 1)−0.3280(0.3091)
0.02570.008659

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

89

CACE (Model 2)−0.4479(0.3446)−0.0484−0.0679
56

Table 139: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−2.0415(1.5662)0.03210.0135
54

CACE (Model 1)−2.2301(1.6138)
0.03110.012153

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

90

CACE (Model 2)−2.5858(1.8464)−0.0405−0.061352

Older Respondents (35 and Younger) - Behavioral Measures (After July 1st) Covariate-Unadjusted
Table 140: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Unadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0039(0.0047)
0.03850.016145

CACE (Model 1)−0.0040(0.0049)
0.03170.0086
44

CACE (Model 2)−0.0045(0.0054)0.02980.006243

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 141: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.1053(0.1247)0.0178−0.0051
45

CACE (Model 1)−0.1005(0.1297)
0.0132−0.010344

CACE (Model 2)−0.0974(0.1459)−0.0124−0.037143

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 142: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0026(0.0689)0.0000−0.018157

CACE (Model 1)
0.0060(0.0710)0.0007−0.0182
55

CACE (Model 2)0.0053(0.0878)0.0009−0.019152

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 143: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.3407(0.2464)0.02700.009457

CACE (Model 1)−0.3022(0.2542)0.02660.008355

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

91

CACE (Model 2)−0.4309(0.2843)−0.0574−0.078652

Table 144: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.8297(1.5103)
0.0056−0.0175
45

CACE (Model 1)−1.2021(1.5408)0.0127−0.010844

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

92

CACE (Model 2)−1.5125(1.7585)−0.0375−0.062843

Respondents That Self-Identify as a Republican - Behavioral Measures (Before
July 1st) - Covariate-Adjusted
Table 145: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Adjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0005(0.0028)0.8631∗∗∗(0.0443)0.88690.8863
419

CACE (Model 1)−0.0004(0.0030)
0.8632∗∗∗(0.0442)0.88700.8864
413

CACE (Model 2)−0.0005(0.0035)0.8643∗∗∗(0.0441)0.88780.8872402

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 146: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0134(0.0170)0.7778∗∗∗(0.0328)0.65370.6520419

CACE (Model 1)−0.0133(0.0177)0.7753∗∗∗(0.0334)
0.64890.6472
413

CACE (Model 2)−0.0134(0.0205)
0.7745∗∗∗(0.0345)0.64540.6436402

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 147: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0155(0.0286)
0.8255∗∗∗(0.0365)0.0397∗∗∗(0.0074)
0.6906
0.6885438

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

93

CACE (Model 1)
0.0206(0.0295)
0.8310∗∗∗(0.0366)
0.0398∗∗∗(0.0076)
0.69240.6902
431

CACE (Model 2)
0.0148(0.0345)0.8308∗∗∗(0.0369)
0.0418∗∗∗(0.0079)
0.6941
0.6918416

Table 148: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0036(0.0262)
0.5536∗∗∗(0.0524)0.1856∗∗∗(0.0239)0.88160.8807
438

CACE (Model 1)
0.0024(0.0271)
0.5610∗∗∗(0.0546)
0.1821∗∗∗(0.0250)
0.87880.8779431

CACE (Model 2)−0.0002(0.0312)0.5283∗∗∗(0.0552)
0.1951∗∗∗(0.0258)
0.87830.8774416

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 149: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.3595(0.3478)
0.7535∗∗∗(0.0736)0.57590.5738419

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

94

CACE (Model 1)
0.3687(0.3630)
0.7519∗∗∗(0.0739)0.57400.5719413

CACE (Model 2)
0.4633(0.4230)
0.7591∗∗∗(0.0741)0.57570.5736402

Respondents That Self-Identify as a Republican - Behavioral Measures (After
July 1st) - Covariate-Adjusted
Table 150: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Adjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0004(0.0042)0.9188∗∗∗(0.0580)
0.82570.8248392

CACE (Model 1)0.0005(0.0043)0.9189∗∗∗(0.0580)
0.82580.8249387

CACE (Model 2)
0.0024(0.0048)0.9205∗∗∗(0.0579)
0.83160.8307375

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 151: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0006(0.0187)
0.7994∗∗∗(0.0307)0.63810.6362
392

CACE (Model 1)−0.0009(0.0195)
0.7986∗∗∗(0.0313)
0.6334
0.6315
387

CACE (Model 2)0.0059(0.0217)0.8123∗∗∗(0.0299)
0.65140.6495375

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 152: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0151(0.0255)
0.6651∗∗∗(0.0394)
0.61510.6133436

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

95

CACE (Model 1)
0.0164(0.0264)
0.6710∗∗∗(0.0394)
0.61750.6157429

CACE (Model 2)
0.0281(0.0299)
0.6720∗∗∗(0.0398)
0.62780.6260414

Table 153: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0030(0.0425)0.5982∗∗∗(0.0688)0.1183∗∗∗(0.0324)0.71930.7173
418

CACE (Model 1)
0.0050(0.0441)0.5983∗∗∗(0.0715)0.1180∗∗∗(0.0336)0.71430.7122
412

CACE (Model 2)0.0048(0.0507)0.5917∗∗∗(0.0761)
0.1230∗∗∗(0.0357)
0.70910.7069398

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 154: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.1751(0.3923)0.7973∗∗∗(0.0731)0.58470.5825392

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

96

CACE (Model 1)
0.1952(0.4081)0.7946∗∗∗(0.0737)
0.58300.5809387

CACE (Model 2)0.1146(0.4652)
0.7997∗∗∗(0.0742)0.58740.5852375

Respondents That Self-Identify as a Republican - Behavioral Measures (Before
July 1st) - Covariate-Unadjusted
Table 155: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Unadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0045(0.0087)
0.0005−0.0017444

CACE (Model 1)
0.0045(0.0091)
0.0006−0.0017
437

CACE (Model 2)0.0043(0.0107)0.0007−0.0017423

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 156: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0123(0.0290)0.0004−0.0019444

CACE (Model 1)
0.0095(0.0299)0.0002−0.0021437

CACE (Model 2)0.0003(0.0341)−0.0000−0.0024423

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 157: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0248(0.0481)0.0005−0.0016
467

CACE (Model 1)
0.0257(0.0498)
0.0004−0.0017459

CACE (Model 2)
0.0131(0.0588)
0.0009−0.0014443

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 158: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0334(0.0859)0.0003−0.0018
467

CACE (Model 1)−0.0344(0.0881)−0.0001−0.0023459

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

97

CACE (Model 2)−0.0583(0.1021)−0.0009−0.0032
443

Table 159: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.1266(0.5549)0.0001−0.0022444

CACE (Model 1)−0.0991(0.5758)
0.0002−0.0021437

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

98

CACE (Model 2)−0.0664(0.6739)0.0002−0.0021423

Respondents That Self-Identify as a Republican - Behavioral Measures (After
July 1st) - Covariate-Unadjusted
Table 160: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Unadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0047(0.0102)0.0005−0.0019422

CACE (Model 1)
0.0047(0.0106)
0.0005−0.0019
417

CACE (Model 2)0.0060(0.0124)−0.0002−0.0027401

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 161: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0284(0.0311)0.0021−0.0003
422

CACE (Model 1)
0.0251(0.0320)0.0019−0.0005
417

CACE (Model 2)
0.0274(0.0368)0.0012−0.0013401

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 162: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0170(0.0384)
0.0004−0.0018
460

CACE (Model 1)
0.0158(0.0398)
0.0002−0.0020453

CACE (Model 2)
0.0207(0.0462)0.0017−0.0006435

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 163: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0640(0.0808)0.0013−0.0008460

CACE (Model 1)−0.0577(0.0831)0.0006−0.0016453

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

99

CACE (Model 2)−0.0775(0.0961)−0.0030−0.0053435

Table 164: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.3306(0.6083)0.0006−0.0017
422

CACE (Model 1)−0.3108(0.6286)0.0005−0.0019417

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

100

CACE (Model 2)−0.4806(0.7333)−0.0012−0.0038401

Respondents That Self-Identify as a Democrat - Behavioral Measures (Before
July 1st) - Covariate-Adjusted
Table 165: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Adjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0005(0.0028)0.8631∗∗∗(0.0443)0.88690.8863
419

CACE (Model 1)−0.0004(0.0030)
0.8632∗∗∗(0.0442)0.88700.8864
413

CACE (Model 2)−0.0005(0.0035)0.8643∗∗∗(0.0441)0.88780.8872402

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 166: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0134(0.0170)0.7778∗∗∗(0.0328)0.65370.6520419

CACE (Model 1)−0.0133(0.0177)0.7753∗∗∗(0.0334)
0.64890.6472
413

CACE (Model 2)−0.0134(0.0205)
0.7745∗∗∗(0.0345)0.64540.6436402

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 167: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0155(0.0286)
0.8255∗∗∗(0.0365)0.0397∗∗∗(0.0074)
0.6906
0.6885438

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

101

CACE (Model 1)
0.0206(0.0295)
0.8310∗∗∗(0.0366)
0.0398∗∗∗(0.0076)
0.69240.6902
431

CACE (Model 2)
0.0148(0.0345)0.8308∗∗∗(0.0369)
0.0418∗∗∗(0.0079)
0.6941
0.6918416

Table 168: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0036(0.0262)
0.5536∗∗∗(0.0524)0.1856∗∗∗(0.0239)0.88160.8807
438

CACE (Model 1)
0.0024(0.0271)
0.5610∗∗∗(0.0546)
0.1821∗∗∗(0.0250)
0.87880.8779431

CACE (Model 2)−0.0002(0.0312)0.5283∗∗∗(0.0552)
0.1951∗∗∗(0.0258)
0.87830.8774416

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 169: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.3595(0.3478)
0.7535∗∗∗(0.0736)0.57590.5738419

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

102

CACE (Model 1)
0.3687(0.3630)
0.7519∗∗∗(0.0739)0.57400.5719413

CACE (Model 2)
0.4633(0.4230)
0.7591∗∗∗(0.0741)0.57570.5736402

Respondents That Self-Identify as a Democrat - Behavioral Measures (After July
1st) - Covariate-Adjusted
Table 170: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Adjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0004(0.0042)0.9188∗∗∗(0.0580)
0.82570.8248392

CACE (Model 1)0.0005(0.0043)0.9189∗∗∗(0.0580)
0.82580.8249387

CACE (Model 2)
0.0024(0.0048)0.9205∗∗∗(0.0579)
0.83160.8307375

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 171: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0006(0.0187)
0.7994∗∗∗(0.0307)0.63810.6362
392

CACE (Model 1)−0.0009(0.0195)
0.7986∗∗∗(0.0313)
0.6334
0.6315
387

CACE (Model 2)0.0059(0.0217)0.8123∗∗∗(0.0299)
0.65140.6495375

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 172: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0151(0.0255)
0.6651∗∗∗(0.0394)
0.61510.6133436

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

103

CACE (Model 1)
0.0164(0.0264)
0.6710∗∗∗(0.0394)
0.61750.6157429

CACE (Model 2)
0.0281(0.0299)
0.6720∗∗∗(0.0398)
0.62780.6260414

Table 173: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0030(0.0425)0.5982∗∗∗(0.0688)0.1183∗∗∗(0.0324)0.71930.7173
418

CACE (Model 1)
0.0050(0.0441)0.5983∗∗∗(0.0715)0.1180∗∗∗(0.0336)0.71430.7122
412

CACE (Model 2)0.0048(0.0507)0.5917∗∗∗(0.0761)
0.1230∗∗∗(0.0357)
0.70910.7069398

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 174: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.1751(0.3923)0.7973∗∗∗(0.0731)0.58470.5825392

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

104

CACE (Model 1)
0.1952(0.4081)0.7946∗∗∗(0.0737)
0.58300.5809387

CACE (Model 2)0.1146(0.4652)
0.7997∗∗∗(0.0742)0.58740.5852375

Respondents That Self-Identify as a Democrat - Behavioral Measures (Before
July 1st) - Covariate-Unadjusted
Table 175: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Unadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0045(0.0087)
0.0005−0.0017444

CACE (Model 1)
0.0045(0.0091)
0.0006−0.0017
437

CACE (Model 2)0.0043(0.0107)0.0007−0.0017423

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 176: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0123(0.0290)0.0004−0.0019444

CACE (Model 1)
0.0095(0.0299)0.0002−0.0021437

CACE (Model 2)0.0003(0.0341)−0.0000−0.0024423

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 177: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0248(0.0481)0.0005−0.0016
467

CACE (Model 1)
0.0257(0.0498)
0.0004−0.0017459

CACE (Model 2)
0.0131(0.0588)
0.0009−0.0014443

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 178: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0334(0.0859)0.0003−0.0018
467

CACE (Model 1)−0.0344(0.0881)−0.0001−0.0023459

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

105

CACE (Model 2)−0.0583(0.1021)−0.0009−0.0032
443

Table 179: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.1266(0.5549)0.0001−0.0022444

CACE (Model 1)−0.0991(0.5758)
0.0002−0.0021437

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

106

CACE (Model 2)−0.0664(0.6739)0.0002−0.0021423

Respondents That Self-Identify as a Democrat - Behavioral Measures (After July
1st) - Covariate-Unadjusted
Table 180: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Unadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0047(0.0102)0.0005−0.0019422

CACE (Model 1)
0.0047(0.0106)
0.0005−0.0019
417

CACE (Model 2)0.0060(0.0124)−0.0002−0.0027401

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 181: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0284(0.0311)0.0021−0.0003
422

CACE (Model 1)
0.0251(0.0320)0.0019−0.0005
417

CACE (Model 2)
0.0274(0.0368)0.0012−0.0013401

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 182: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0170(0.0384)
0.0004−0.0018
460

CACE (Model 1)
0.0158(0.0398)
0.0002−0.0020453

CACE (Model 2)
0.0207(0.0462)0.0017−0.0006435

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 183: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0640(0.0808)0.0013−0.0008460

CACE (Model 1)−0.0577(0.0831)0.0006−0.0016453

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

107

CACE (Model 2)−0.0775(0.0961)−0.0030−0.0053435

Table 184: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.3306(0.6083)0.0006−0.0017
422

CACE (Model 1)−0.3108(0.6286)0.0005−0.0019417

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

108

CACE (Model 2)−0.4806(0.7333)−0.0012−0.0038401

Respondents That Self-Identify as Neither a Democrat or Republican - Behavioral Measures (Before July 1st) - Covariate-Adjusted
Table 185: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Adjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0110(0.0236)0.0025−0.0063
116

CACE (Model 1)−0.0109(0.0243)0.0011−0.0078
115

CACE (Model 2)−0.0101(0.0289)
0.0022−0.0069111

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 186: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0429(0.0322)0.8211∗∗∗(0.0613)0.65660.6505
115

CACE (Model 1)
0.0434(0.0333)
0.8211∗∗∗(0.0612)0.65410.6478114

CACE (Model 2)
0.0533(0.0381)0.8558∗∗∗(0.0530)
0.69670.6911110

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 187: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0584(0.0669)
0.8480∗∗∗(0.0931)0.64680.6407118

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

109

CACE (Model 1)
0.0618(0.0690)0.8452∗∗∗(0.0933)
0.64620.6400117

CACE (Model 2)
0.0920(0.0812)0.8163∗∗∗(0.0970)
0.61270.6057113

Table 188: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0026(0.0516)0.7268∗∗∗(0.0986)
0.0974∗(0.0446)0.82730.8227
117

CACE (Model 1)−0.0039(0.0530)
0.7262∗∗∗(0.0990)0.0981∗(0.0448)0.82750.8228116

CACE (Model 2)
0.0026(0.0621)0.7297∗∗∗(0.0998)
0.0998∗(0.0445)0.82520.8203112

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 189: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)1.2078(0.8507)0.8370∗∗∗(0.0730)
0.71560.7105115

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

110

CACE (Model 1)
1.2361(0.8778)0.8379∗∗∗(0.0729)0.71500.7099114

CACE (Model 2)
1.2949(1.0111)0.8256∗∗∗(0.0754)0.71770.7124110

Respondents That Self-Identify as Neither a Democrat or Republican - Behavioral Measures (After July 1st) - Covariate-Adjusted
Table 190: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Adjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0060(0.0201)
0.0009−0.0081114

CACE (Model 1)−0.0058(0.0207)0.0000−0.0090
113

CACE (Model 2)−0.0036(0.0252)0.0017−0.0077108

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 191: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0471(0.0383)0.8207∗∗∗(0.0568)0.65080.6442
109

CACE (Model 1)−0.0495(0.0399)
0.8167∗∗∗(0.0578)0.64570.6390108

CACE (Model 2)−0.0600(0.0479)
0.8451∗∗∗(0.0506)
0.6694
0.6629104

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 192: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0315(0.0570)0.6976∗∗∗(0.0719)0.62900.6224115

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

111

CACE (Model 1)−0.0319(0.0590)0.6980∗∗∗(0.0724)0.62760.6208114

CACE (Model 2)−0.0230(0.0712)0.6808∗∗∗(0.0755)
0.61000.6027110

Table 193: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0180(0.0801)
0.8194∗∗∗(0.0507)0.75520.7509115

CACE (Model 1)−0.0241(0.0824)0.8212∗∗∗(0.0506)
0.75810.7537
114

CACE (Model 2)−0.0884(0.0872)
0.8481∗∗∗(0.0487)
0.78950.7856110

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 194: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.6040(1.7568)0.0013−0.0076
114

CACE (Model 1)
0.6152(1.8100)
0.0001−0.0089113

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

112

CACE (Model 2)0.4907(2.0759)
0.0022−0.0073108

Respondents That Self-Identify as Neither a Democrat or Republican - Behavioral Measures (Before July 1st) - Covariate-Unadjusted
Table 195: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Unadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0110(0.0236)0.0025−0.0063
116

CACE (Model 1)−0.0109(0.0243)0.0011−0.0078
115

CACE (Model 2)−0.0101(0.0289)
0.0022−0.0069111

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 196: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0537(0.0660)
0.0058−0.0029
116

CACE (Model 1)−0.0603(0.0679)
0.00880.0001
115

CACE (Model 2)−0.0673(0.0802)0.00970.0006111

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 197: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0514(0.1200)
0.0017−0.0068
119

CACE (Model 1)
0.0587(0.1233)
0.0047−0.0039
118

CACE (Model 2)0.1199(0.1363)−0.0038−0.0128114

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 198: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0001(0.1508)
0.0000−0.0085
119

CACE (Model 1)0.0017(0.1553)0.0000−0.0086118

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

113

CACE (Model 2)
0.0720(0.1801)
0.0004−0.0085
114

Table 199: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.5911(1.7251)0.0013−0.0075
116

CACE (Model 1)0.6007(1.7762)0.0003−0.0085115

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

114

CACE (Model 2)0.2114(2.0372)−0.0002−0.0094111

Respondents That Self-Identify as Neither a Democrat or Republican - Behavioral Measures (After July 1st) - Covariate-Unadjusted
Table 200: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Unadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0060(0.0201)
0.0009−0.0081114

CACE (Model 1)−0.0058(0.0207)0.0000−0.0090
113

CACE (Model 2)−0.0036(0.0252)0.0017−0.0077108

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 201: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0843(0.0660)
0.01440.0056
114

CACE (Model 1)−0.0914(0.0679)
0.01740.0086
113

CACE (Model 2)−0.1073(0.0828)0.0012−0.0082108

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 202: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0098(0.1046)0.0001−0.0085
118

CACE (Model 1)−0.0061(0.1076)−0.0002−0.0089
117

CACE (Model 2)
0.0404(0.1223)−0.0045−0.0136112

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 203: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0215(0.1476)0.0002−0.0084
118

CACE (Model 1)
0.0184(0.1519)0.0007−0.0080117

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

115

CACE (Model 2)
0.0309(0.1798)
0.0011−0.0079
112

Table 204: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.6040(1.7568)0.0013−0.0076
114

CACE (Model 1)
0.6152(1.8100)
0.0001−0.0089113

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

116

CACE (Model 2)0.4907(2.0759)
0.0022−0.0073108

Respondents With Higher than The Median Level of Digital Literacy - Behavioral Measures (Before July 1st) - Covariate-Adjusted
Table 205: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Adjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0049(0.0036)0.8064∗∗∗(0.0713)0.82250.8216
386

CACE (Model 1)−0.0050(0.0038)
0.8063∗∗∗(0.0714)0.82200.8210379

CACE (Model 2)−0.0054(0.0043)0.7902∗∗∗(0.0761)0.81030.8093370

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 206: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0012(0.0185)
0.7928∗∗∗(0.0339)
0.66630.6645
386

CACE (Model 1)
0.0020(0.0191)
0.7895∗∗∗(0.0345)0.66050.6587
379

CACE (Model 2)0.0038(0.0215)
0.7857∗∗∗(0.0354)0.66180.6600370

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 207: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Age-Squared
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0568(0.0304)
0.0000∗∗∗(0.0000)
0.8223∗∗∗(0.0366)
0.0505∗∗∗(0.0092)
0.75880.7563403

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

117

CACE (Model 1)
0.0591(0.0316)0.0000∗∗∗(0.0000)
0.8252∗∗∗(0.0372)
0.0523∗∗∗(0.0095)
0.75750.7550395

CACE (Model 2)
0.0669(0.0362)0.0000∗∗∗(0.0000)
0.8142∗∗∗(0.0378)
0.0538∗∗∗(0.0099)0.74920.7465384

Table 208: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0089(0.0294)
0.4465∗∗∗(0.0570)0.2292∗∗∗(0.0252)
0.84760.8464
403

CACE (Model 1)−0.0124(0.0305)
0.4464∗∗∗(0.0579)
0.2291∗∗∗(0.0258)
0.84150.8403395

CACE (Model 2)−0.0188(0.0340)0.4525∗∗∗(0.0584)0.2291∗∗∗(0.0265)
0.84390.8427384

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 209: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.3890(0.4195)
0.7852∗∗∗(0.0512)0.69130.6897386

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

118

CACE (Model 1)
0.2946(0.4260)0.8015∗∗∗(0.0483)
0.71200.7104
379

CACE (Model 2)0.3310(0.4812)0.7947∗∗∗(0.0518)
0.69680.6951370

Respondents With Higher than The Median Level of Digital Literacy - Behavioral Measures (After July 1st) - Covariate-Adjusted
Table 210: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Adjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0072(0.0050)
0.7564∗∗∗(0.0910)0.75300.7516
353

CACE (Model 1)−0.0074(0.0051)0.7566∗∗∗(0.0910)0.75310.7516
348

CACE (Model 2)−0.0067(0.0057)
0.7900∗∗∗(0.0884)
0.77120.7699338

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 211: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0052(0.0212)0.7660∗∗∗(0.0355)
0.60740.6051
353

CACE (Model 1)
0.0064(0.0217)0.7633∗∗∗(0.0361)0.60190.5996
348

CACE (Model 2)0.0033(0.0238)0.7705∗∗∗(0.0354)0.61190.6096338

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 212: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Age-Squared
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0055(0.0263)0.0000∗∗∗(0.0000)
0.6812∗∗∗(0.0454)0.70000.6977397

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

119

CACE (Model 1)−0.0068(0.0270)0.0000∗∗∗(0.0000)
0.6889∗∗∗(0.0462)
0.70300.7007
390

CACE (Model 2)−0.0011(0.0314)0.0000∗∗∗(0.0000)
0.6832∗∗∗(0.0472)
0.69370.6912377

Table 213: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Age
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0126(0.0484)
0.0073∗∗∗(0.0019)0.4028∗∗∗(0.0735)
0.1983∗∗∗(0.0317)0.68370.6803
378

CACE (Model 1)−0.0133(0.0497)0.0073∗∗∗(0.0020)
0.3981∗∗∗(0.0736)0.2003∗∗∗(0.0319)0.67520.6717
372

CACE (Model 2)−0.0422(0.0547)
0.0064∗∗(0.0020)0.4128∗∗∗(0.0741)0.2038∗∗∗(0.0328)0.68930.6858361

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 214: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.9398(0.5177)0.7544∗∗∗(0.0657)
0.60760.6053353

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

120

CACE (Model 1)
0.8315(0.5203)
0.7765∗∗∗(0.0625)
0.6372
0.6351
348

CACE (Model 2)
0.9245(0.5676)
0.8068∗∗∗(0.0549)
0.66030.6583338

Respondents With Higher than The Median Level of Digital Literacy - Behavioral Measures (Before July 1st) - Covariate-Unadjusted
Table 215: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Unadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0094(0.0103)0.0018−0.0007
407

CACE (Model 1)
0.0101(0.0106)
0.0024−0.0001
399

CACE (Model 2)0.0092(0.0117)0.0025−0.0001389

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 216: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0260(0.0313)0.0017−0.0008
407

CACE (Model 1)−0.0274(0.0321)0.0021−0.0004
399

CACE (Model 2)−0.0398(0.0360)
0.0018−0.0008389

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 217: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0812(0.0581)0.00430.0020432

CACE (Model 1)
0.0933(0.0600)
0.00710.0048423

CACE (Model 2)
0.0979(0.0678)
0.00740.0050411

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 218: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0221(0.0869)0.0001−0.0022432

CACE (Model 1)−0.0138(0.0888)−0.0003−0.0027423

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

121

CACE (Model 2)−0.0128(0.1009)−0.0008−0.0033
411

Table 219: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.4786(0.7629)0.0009−0.0016
407

CACE (Model 1)−0.5288(0.7871)0.0014−0.0011399

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

122

CACE (Model 2)−0.5786(0.8751)0.0015−0.0010389

Respondents With Higher than The Median Level of Digital Literacy - Behavioral Measures (After July 1st) - Covariate-Unadjusted
Table 220: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Unadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0074(0.0113)0.0010−0.0016
376

CACE (Model 1)
0.0079(0.0115)0.0013−0.0014371

CACE (Model 2)0.0086(0.0132)
0.0008−0.0020360

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 221: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0037(0.0333)
0.0000−0.0026
376

CACE (Model 1)−0.0027(0.0339)0.0000−0.0027
371

CACE (Model 2)−0.0159(0.0383)−0.0007−0.0035360

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 222: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0378(0.0490)0.0014−0.0011
413

CACE (Model 1)
0.0458(0.0504)
0.0021−0.0003
406

CACE (Model 2)
0.0491(0.0571)0.0006−0.0019392

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 223: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0246(0.0846)0.0002−0.0022413

CACE (Model 1)−0.0059(0.0860)−0.0001−0.0026406

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

123

CACE (Model 2)−0.0184(0.0981)−0.0012−0.0038
392

Table 224: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.2860(0.8398)
0.0003−0.0024
376

CACE (Model 1)−0.3695(0.8577)0.0007−0.0020371

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

124

CACE (Model 2)−0.4384(0.9625)−0.0008−0.0036360

Respondents With Lower than The Median Level of Digital Literacy - Behavioral
Measures (Before July 1st) - Covariate-Adjusted
Table 225: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Adjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0032(0.0040)0.9737∗∗∗(0.0614)0.84080.8400395

CACE (Model 1)
0.0029(0.0042)0.9788∗∗∗(0.0627)0.84190.8411
388

CACE (Model 2)0.0035(0.0050)0.9796∗∗∗(0.0629)0.84150.8406378

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 226: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0119(0.0180)0.8044∗∗∗(0.0330)
0.65350.6517395

CACE (Model 1)−0.0101(0.0192)0.8020∗∗∗(0.0336)0.64910.6473
388

CACE (Model 2)−0.0143(0.0223)0.8176∗∗∗(0.0323)
0.66200.6601378

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 227: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0311(0.0314)0.9151∗∗∗(0.0325)
0.67940.6779425

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

125

CACE (Model 1)
0.0344(0.0332)
0.9127∗∗∗(0.0336)0.67320.6716
417

CACE (Model 2)
0.0369(0.0391)0.9099∗∗∗(0.0345)0.66920.6676405

Table 228: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Age
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0039(0.0271)
0.0039∗∗(0.0013)0.5804∗∗∗(0.0491)
0.1757∗∗∗(0.0243)0.87870.8775410

CACE (Model 1)−0.0020(0.0290)0.0040∗∗(0.0013)0.5773∗∗∗(0.0498)
0.1766∗∗∗(0.0246)0.87780.8765403

CACE (Model 2)0.0041(0.0340)0.0036∗∗(0.0013)0.5620∗∗∗(0.0515)0.1845∗∗∗(0.0254)0.87660.8754391

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 229: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.5443(0.4889)
0.8416∗∗∗(0.0493)0.68070.6791395

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

126

CACE (Model 1)
0.7158(0.5156)
0.8354∗∗∗(0.0506)
0.6766
0.6749388

CACE (Model 2)
0.8194(0.6069)
0.8383∗∗∗(0.0514)0.67580.6741378

Respondents With Lower than The Median Level of Digital Literacy - Behavioral
Measures (After July 1st) - Covariate-Adjusted
Table 230: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Adjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0018(0.0051)
0.8840∗∗∗(0.0767)
0.75410.7528375

CACE (Model 1)−0.0015(0.0055)
0.8870∗∗∗(0.0784)
0.75360.7522
368

CACE (Model 2)−0.0002(0.0063)0.8883∗∗∗(0.0784)0.76080.7595358

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 231: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0070(0.0202)0.7733∗∗∗(0.0385)0.5896
0.5874
375

CACE (Model 1)−0.0066(0.0218)
0.7757∗∗∗(0.0392)
0.5876
0.5853368

CACE (Model 2)−0.0070(0.0251)0.7944∗∗∗(0.0387)
0.60920.6070358

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 232: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0134(0.0281)0.7159∗∗∗(0.0368)
0.62250.6207409

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

127

CACE (Model 1)
0.0146(0.0301)
0.7174∗∗∗(0.0385)0.61410.6122
401

CACE (Model 2)
0.0271(0.0345)0.7196∗∗∗(0.0394)0.62010.6181390

Table 233: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateAdjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
Log of news viewed
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0087(0.0418)0.6500∗∗∗(0.0663)
0.1077∗∗(0.0326)
0.75660.7548394

CACE (Model 1)−0.0118(0.0445)0.6557∗∗∗(0.0668)
0.1052∗∗(0.0329)0.75750.7556
387

CACE (Model 2)−0.0022(0.0523)0.6582∗∗∗(0.0701)
0.1085∗∗(0.0340)0.75740.7554376

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 234: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Adjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
Pre-Treatment Value
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.1882(0.5113)0.7774∗∗∗(0.0584)0.62870.6267375

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

128

CACE (Model 1)0.2505(0.5494)
0.7829∗∗∗(0.0603)0.62440.6224
368

CACE (Model 2)0.2323(0.6436)0.7842∗∗∗(0.0619)0.62090.6188358

Respondents With Lower than The Median Level of Digital Literacy - Behavioral
Measures (Before July 1st) - Covariate-Unadjusted
Table 235: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Unadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0071(0.0107)0.0012−0.0012409

CACE (Model 1)−0.0078(0.0114)0.0007−0.0018402

CACE (Model 2)−0.0096(0.0136)0.0017−0.0009390

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 236: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0100(0.0309)
0.0002−0.0022409

CACE (Model 1)
0.0176(0.0328)0.0006−0.0019402

CACE (Model 2)
0.0146(0.0384)
0.0003−0.0023390

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 237: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0109(0.0563)0.0001−0.0022430

CACE (Model 1)−0.0130(0.0595)
0.0000−0.0023422

CACE (Model 2)−0.0092(0.0694)−0.0001−0.0025409

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 238: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0068(0.0845)0.0000−0.0023430

CACE (Model 1)
0.0071(0.0895)
0.0002−0.0022422

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

129

CACE (Model 2)0.0095(0.1042)−0.0003−0.0027
409

Table 239: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (Before July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.6825(0.8920)0.0015−0.0010409

CACE (Model 1)
0.9061(0.9395)
0.00280.0003402

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

130

CACE (Model 2)0.9529(1.0996)0.0009−0.0017390

Respondents With Lower than The Median Level of Digital Literacy - Behavioral
Measures (After July 1st) - Covariate-Unadjusted
Table 240: Testing the Effect of the Intervention on Proportion of News Diet That is Unreliable with
Covariate-Unadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0107(0.0105)
0.00290.0004
401

CACE (Model 1)−0.0108(0.0112)0.0015−0.0011394

CACE (Model 2)−0.0116(0.0133)
0.00770.0051380

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 241: Testing the Effect of the Intervention on Proportion of News Diet That is Reliable with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.0207(0.0309)
0.0011−0.0014401

CACE (Model 1)
0.0277(0.0331)0.0007−0.0019394

CACE (Model 2)
0.0341(0.0391)
0.0005−0.0021380

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 242: Testing the Effect of the Intervention on Count of Unreliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0175(0.0458)
0.0004−0.0020430

CACE (Model 1)−0.0205(0.0486)−0.0002−0.0026
422

CACE (Model 2)−0.0066(0.0572)
0.0002−0.0023408

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

Table 243: Testing the Effect of the Intervention on Count of Reliable News Consumed with CovariateUnadjusted Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)−0.0383(0.0806)
0.0005−0.0018430

CACE (Model 1)−0.0304(0.0855)−0.0001−0.0025422

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

131

CACE (Model 2)−0.0327(0.1013)0.0001−0.0024408

Table 244: Testing the Effect of the Intervention on Reliability Score of News Diet with Covariate-Unadjusted
Models (HC2 Robust standard errors) (After July 1st)

Treatment
R2
Adj. R2
Num. obs.
∗∗∗

Intent-To-Treat (ITT)0.6270(0.8945)0.0013−0.0012401

CACE (Model 1)
0.7670(0.9551)0.0011−0.0015394

p < 0.001; ∗∗ p < 0.01; ∗ p < 0.05

132

CACE (Model 2)0.7094(1.1335)0.0017−0.0009380

N

Balance Table and Density Plots

Table 245: Average Statistics for Behavioral Measures of Online News Quality by Control and Treatment
Group in Each Time Period of Interest
Control

Number of respondents
Average Proportion of News Viewed that was ”Unreliable”(Pre-Treatment period)
Average Proportion of News Viewed that was ”Unreliable”(Treatment period)
Average Proportion of News Viewed that was ”Unreliable”(Post-Treatment period)
Average Proportion of News Viewed that was ”Reliable”(Pre-Treatment period)
Average Proportion of News Viewed that was ”Reliable”(Treatment period)
Average Proportion of News Viewed that was ”Reliable”(Post-Treatment period)
Average Count (log) of News Viewed that was ”Unreliable”(Pre-Treatment period)
Average Count (log) of News Viewed that was ”Unreliable”(Treatment period)
Average Count (log) of News Viewed that was ”Unreliable”(Post-Treatment period)
Average Count (log) of News Viewed that was ”Reliable”(Pre-Treatment period)
Average Count (log) of News Viewed that was ”Reliable”(Treatment period)
Average Count (log) of News Viewed that was ”Reliable”(Post-Treatment period)
Average Reliability Score of News Viewed(Pre-Treatment period)
Average Reliability Score of News Viewed(Treatment period)
Average Reliability Score of News Viewed(Post-Treatment period)

133

356
0.03143

Treatment Difference(Treatment Control)612
NA0.031850.000420.030540.031070.00053

0.032150.03057-0.00159

0.67395

0.675130.001180.68441

0.67339-0.01103

0.703090.71415

0.01106

0.329380.31552-0.01386

0.349020.37805

0.029020.24346

0.25249

0.00904

1.73446

1.73218-0.00228

1.85586

1.83976-0.0161

1.56868

1.5284-0.04028

87.787987.52881-0.25909

87.26024

87.31514

0.054987.11857

87.29597

0.17741

Figure 13: This figure presents the distribution of the proportion of news sites viewed that are unreliable
among respondents in the treatment and control groups with a vertical dashed line indicating the mean
reliability score for each group. Panels A, B, and C present these distributions during the pre-treatment
period, treatment period, and post-treatment period respectively.

(a) Pre-Treatment Period(b) Treatment Period(c) Post-Treatment Period

134

Figure 14: This figure presents the distribution of the proportion of news sites viewed that are reliable
among respondents in the treatment and control groups with a vertical dashed line indicating the mean
reliability score for each group. Panels A, B, and C present these distributions during the pre-treatment
period, treatment period, and post-treatment period respectively.

(a) Pre-Treatment Period(b) Treatment Period(c) Post-Treatment Period

135

Figure 15: This figure presents the distribution of the count of unreliable news sites viewed (log) among
respondents in the treatment and control groups with a vertical dashed line indicating the mean reliability
score for each group. Panels A, B, and C present these distributions during the pre-treatment period,
treatment period, and post-treatment period respectively.

(a) Pre-Treatment Period(b) Treatment Period(c) Post-Treatment Period

136

Figure 16: This figure presents the distribution of the count of reliable news sites viewed (log) among
respondents in the treatment and control groups with a vertical dashed line indicating the mean reliability
score for each group. Panels A, B, and C present these distributions during the pre-treatment period,
treatment period, and post-treatment period respectively.

(a) Pre-Treatment Period(b) Treatment Period(c) Post-Treatment Period

137

O

Results From Covariate-Adjusted Models using Mobile Behavioral Data

Figure 17: This figure presents estimates of the effect of the intervention (ITT, with 95% confidence intervals)
on our mobile behavioral measures in the two periods after treatment assignment: before July 1 when the
NewsGuard extension was freely available and the two-week period between July 1–13 when the NewsGuard
extension was disabled. The effect is reported in standard deviations of that measure (pre-treatment).

138

Table 246: This table presents the number and percentage of the Pulse sample for which we collected only
web tracking data from their desktop/laptop, only from their mobile device, or from both

Percentage ofPulse Sample
Number of Respondents

Desktop/Laptop Only
79.14

Mobile Only
18.24

Both Desktop/Laptop and Mobile
2.62

937

216

31

139

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Mapping the Ideology of News Media Content, Citizens, and Politicians.” . Forthcoming.
Guess, Andrew, Brendan Nyhan and Jason Reifler. 2019. “National News, Local Lens? Findings from the2019 Poynter Media Trust Survey.” October 21:2019.
Marbach, Moritz and Dominik Hangartner. 2020. “Profiling Compliers and Noncompliers for InstrumentalVariable Analysis.” Political Analysis pp. 1–10.
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URL: https://www.journalism.org/2017/05/10/media-attitudes-methodology/140