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# Conflict Reporting in the Digital Age

Sophie Hatte, Etienne Madinier, Ekaterina Zhuravskaya

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Sophie Hatte, Etienne Madinier, Ekaterina Zhuravskaya. Conflict Reporting in the Digital Age. 2025. hal-05184425

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Distributed under a Creative Commons Attribution 4.0 International License
CERGIC Working Papers

No. 0013

July 2025

Conflict Reporting in the Digital Age

Sophie Hatte

<figure><img src="image_3.png" /></figure>

Etienne Madinier

Ekaterina Zhuravskaya

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JEL codes: D74, F51, L86.

Keywords: Social Media, Traditional media, Conflicts, Israeli-Palestinian conflict, Twitter.
# Conflict Reporting in the Digital Age*

Sophie Hatte† Etienne Madinier Ekaterina Zhuravskaya‡

July 23, 2025

## Abstract

The digital age has fundamentally reshaped international traditional news reporting of foreign conflicts. Online posts by first-hand witnesses influence the volume, slant, content, informativeness, and tone of international media conflict coverage. Exploiting exogenous variation from local internet outages in the conflict zone, we show that U.S. TV networks air more stories about the Israeli-Palestinian conflict when ordinary users caught in the conflict have internet access. These stories differ in both slant and focus: in the absence of internet disruptions, news coverage is more sympathetic toward the conflict side experiencing higher casualties, places greater emphasis on civilian suffering, and devotes less attention to foreign policy and electoral considerations. This coverage is also more emotional, includes more local detail, references social media platforms more frequently, and is more homogeneous across ideologically diverse networks. Importantly, these conflict stories generate greater viewer engagement.

<footnote>*Previous drafts were circulated under the title: "Reading Twitter in the Newsroom: Web 2.0 and Traditional Media Reporting of Conflicts." We thank Elliott Ash, Pierre Boyer, Ruben Durante, Julia Cagé, Ruben Enikolopov, Raphaël Franck, Jeffry Frieden, Matthew Gentzkow, Irena Grosfeld, Sergei Guriev, Noel Johnson, Mark Koyama, Ro'ee Levy, Maria Petrova, Pia Raffler, Dominic Rohner, Piotr Smolar, Moses Shayo, David Yanagizawa-Drott, David Yang, Tamar Yerushalmi and the participants of seminars at the Center for Study of Public Choice (GMU), CREST (Ecole Polytechnique), ENS (Lyon), Harvard University, Hebrew University of Jerusalem, IFO Institute, Paris School of Economics, Pompeu Fabra University, Stockholm School of Economics, University of Barcelona, University of British Columbia, Université de Montpellier, Universitat Manmheim, University of Sydney, University of Wisconsin-Madison, University of Zurich, CEPR Political Economy Webinar, CEPR RPN Conference on preventing conflict, and Monash-Warwick-Zurich Text-as-Data Workshop for helpful comments. Sophie Hatte acknowledges financial support from the French National Research Agency (ANR JCJC grant: ANR-22-CE26-0006). Ekaterina Zhuravskaya thanks the European Research Council (ERC) for funding from the European Union's Horizon 2020 Research and Innovation program (grant 646662). The authors wish to thank the World Wide Lightning Location Network (http://wwlln.net), a collaboration among over 50 universities and institutions, for providing the lightning location data used in this paper. We thank Natasa Tagasovska for invaluable help with collection of Twitter data and Marion Dury and Adrien Foutelet for excellent research assistance.</footnote>

<footnote>†Sophie Hatte: Center for Economic Research on Governance, Inequality and Conflict, Ecole Normale Supérieure de Lyon, CNRS.</footnote>

<footnote>‡Ekaterina Zhuravskaya: Paris School of Economics (EHESS).</footnote>
# 1 Introduction

Voters need to be informed about ongoing conflicts, even when these occur in foreign countries far from home. Public awareness and understanding of distant wars can influence key decisions in democratic societies, shaping foreign policy, public support for military interventions, and the allocation of humanitarian and military aid (e.g., Baum and Potter, 2015; Fetzer et al., 2024). Traditional media plays a crucial role in shaping public understanding of such conflicts and in exposing war crimes. Yet reporting from war zones is extremely difficult. Journalists face physical danger, limited access, and logistical constraints that restrict what they can witness and report firsthand (Creech, 2017). As a result, historically, media coverage of conflicts has often been shaped by official sources, which filter narratives through the lens of governments and militaries involved.

The digital age has fundamentally transformed conflict reporting. Starting with the Syrian civil war, journalists have increasingly relied on content produced by people living in conflict zones and witnessing events that journalists cannot access themselves (Patrikarakos, 2017). This information is often gathered from social media. As CNN's chief international correspondent Clarissa Ward described while reporting from Kyiv just one week into Russia's full-scale invasion of Ukraine, real-time footage posted online by witnesses is geolocated, fact-checked, and contextualized by newsroom support teams to help traditional journalists understand events on the ground. Ward argued that this practice, once rare, has become a crucial part of modern conflict reporting.<fnref n="1" />

In this new digital media environment, ordinary people—armed with smartphones and social media accounts—can act as de facto “citizen journalists.” Open-source investigators and media scholars note that attention from traditional journalists to such digital testimonies can elevate voices that might otherwise go unheard (e.g., Patrikarakos, 2017; Singer and Brooking, 2018; Higgins, 2021). For conflict coverage, this represents a potentially powerful shift: the focus of news may move from official military and government sources to the suffering of civilians experiencing violence and even war crimes. Despite growing anecdotal evidence (e.g., Sacco and Bossio, 2015; Patrikarakos, 2017; Löfstedt, 2024), there is little systematic evidence documenting whether and how user-generated digital media content from conflict zones affects traditional news coverage of conflicts. This paper addresses that gap.

We estimate how information generated and posted online in Israel and the Palestinian Territories about the Israeli-Palestinian conflict affected news coverage of the conflict by major U.S. television channels between 2009 and 2019. For identification, we exploit exogenous variation in online posting by ordinary people in the conflict zone, driven by local internet outages. We find that when ordinary users were able to post online in the conflict zone, compared to periods of internet disruption, the volume of U.S. television coverage of the Israeli-Palestinian

<footnote n="1">¹While Ward explained how social media helps journalists make sense of events in the fog of war, in general, and under curfew, in particular, CNN aired unedited footage from a Ukrainian Twitter account, @AlchevskUA (see https://edition.cnn.com/videos/world/2022/03/01/ukraine-clarissa-ward-cooper-full-episode-acfc-vpx.cnn, accessed January 3, 2025). Left panel of Figure A1 presents the screenshot.</footnote>
conflict increased. More importantly, the nature of the coverage changed: with internet access in the conflict zone, reporting became slanted toward the side experiencing higher casualties, and the focus shifted toward the suffering of civilians on the ground and away from high-level U.S. diplomacy and elections. We also show that, with internet in the conflict zone, the conflict-related TV news stories were more emotional and were more likely to explicitly reference Twitter, Facebook, and Instagram. In addition, in the absence of internet disruptions in the conflict zone, conflict-related coverage across different U.S. TV networks became more similar, even among networks with very different ideological leanings. Ultimately, this shift in the content of coverage led to increased interest in conflict-related stories among news consumers, as evidenced by higher public engagement with traditional media stories posted online by U.S. television outlets, including more likes, shares, and comments. This final result suggests that the public prefers conflict coverage that draws on conflict-zone digital media sources.

We do not have systematic evidence on the exact mechanism. However, informal interviews with two international war correspondents—one from a leading French newspaper and the other from a major Canadian TV channel, both with extensive experience reporting before and after the digital transition—suggest that traditional media editors have long prioritized personal stories of civilians caught in conflict. According to these journalists, audiences are generally more drawn to such narratives than to statistics or aggregated information, such as those found in newswires. The digital age has simply made these personal stories easier to find and report, transforming how conflicts are covered. Consistent with this view, we find no effect of conflict-zone internet outages on international newswire coverage of the conflict. The implications of this shift extend beyond media consumption: as Durante and Zhuravskaya (2018) show, actors in conflict, and particularly the Israeli Defense Forces (IDF), are more constrained by coverage of civilian suffering than by reports on aggregate military outcomes.

A case study, presented below, illustrates our main findings. It centers on Farah Baker, a 16-year-old Palestinian girl who tweeted in English from Gaza during the 2014 Gaza War. On Twitter, she documented Israeli bombing raids near her home, sharing both her own footage of the attacks and her emotional reactions. Her tweets quickly attracted the attention of Western reporters covering the conflict. As a result, both liberal and conservative media outlets around the world reported on Farah and incorporated content from her Twitter account into their coverage, thereby amplifying her voice (e.g., Patrikarakos, 2017, pp. 21–37).<fnref n="2" />

We focus on U.S. television news about the Israeli-Palestinian conflict because television has remained an important source of news for the American public throughout our observation period (Pew Research Center, 2018, 2019), and because U.S. TV channels do cover this conflict: between 2009 and 2019, 9% of all prime-time newscasts by major networks included a story related to it. We begin our analysis in November 2009 due to data availability and end on December 30 2019, just before COVID-19 pandemic, which significantly disrupted the ability of citizen journalists to report from the conflict zone.

Our empirical analysis combines several data sources. The main outcome variables come

<footnote n="2">²Appendix Figure A2 shows screenshots from a CNN news program that directly quoted her posts.</footnote>
from the web-scraped TV News Archive. We use full transcripts of all news broadcasts from
the following U.S. TV channels: ABC, CBS, CNN, FOX, MSNBC, NBC, PBS, and Bloomberg.
For comparison, we also use transcripts of news by Al Jazeera America, a Qatari TV network
available in the U.S. Based on these transcripts, for each day and network, we first identify
all news stories about the conflict zone based on stemmed keywords for “Israel,” “Palestin,” and
“Gaza.” We, then, build daily series for each TV network of the extent, slant, topics, and tonality
of conflict-related news coverage.

We proxy online activity in the conflict zone using the total number of tweets containing stemmed keywords for Israel, Palestin, and Gaza. We then identify the language of each tweet and train a machine learning algorithm to classify whether the topic of each English-language tweet is related to the conflict between Israelis and Palestinians. Using self-reported user profile information, we also determine the location of the user account—Israel, the Palestinian Territories, or the rest of the world. Based on this, we construct daily time series of the number of conflict-related tweets in English posted by users in the conflict zone. We proxy newsworthy events in the conflict zone with daily data on fatal casualties and injuries on both sides, protests, and house demolitions. These data are supplemented with information on local weather conditions and domestic U.S. news pressure. Our main source of exogenous variation is local internet outages.

The key challenge in identifying the effect of the internet on traditional media reporting
is identification: both reverse causality and omitted variable bias may explain the observed
association between content posted online and coverage by traditional media outlets. To address
this endogeneity problem, we use plausibly exogenous internet outages in the conflict zone as a
source of exogenous variation in online activity. We both directly relate outages to the outcomes
of interest in a reduced-form framework and use them as an instrument to predict users' posting
online from the conflict zone on digital media platforms. In this case, the number of tweets from
the conflict zone is the endogenous explanatory variable, which we treat as a proxy for overall
online activity.

We use two sources of variation to measure internet outages: disruptions from lightning strikes and technical failures in local infrastructure. First, we rely on high-resolution data on the timing and location of all cloud-to-ground lightning strikes in populated areas of Israel and the Palestinian territories. Information and Communication Technology (ICT) experts note that electrostatic discharges from such strikes can damage ICT infrastructure and limit internet connectivity, especially in the absence of adequate power surge protection (Zeddam and Day, 2014; Martin, 2016). Second, we apply a method developed in computer science that detects internet outages by monitoring data traffic between a geographic region and the rest of the broader internet (Dainotti et al., 2011; Padmanabhan et al., 2019). Because some IP addresses remain consistently active—many of them automated and powered by off-grid generators—a sudden, sharp drop in traffic signals a likely outage caused by local technical failure. We collect data on internet traffic flowing to and from Autonomous Systems (i.e., regional clusters of IP addresses) in Israel and the Palestinian territories, and identify each episode where traffic
collapses sharply.

Both the occurrence of lightning strikes and the absence of traffic strongly and significantly predict tweeting activity from the conflict zone, including both total tweets and conflict-related tweets, conditional on the intensity of violence on both sides, seasonal variation, and other potentially confounding weather shocks that may influence both conflict dynamics and the quality of the internet connection, such as rainfall and wind strength.

We provide several pieces of evidence in support of the exclusion restriction. First, neither lightning strikes nor instances of internet traffic absence correlate with the timing of observable newsworthy conflict events, such as the attack severity, protests, house demolitions, or power outages. Importantly, although Israel has the technical capacity to shut down internet access in Gaza entirely, this does not pose a threat to our identification strategy: If such shutdowns had occurred during our observation period, they would have biased our estimates against finding an effect, since internet blackouts are more likely during high-intensity, newsworthy events. However, extensive anecdotal evidence suggests that the first such internet shutdown in Gaza occurred only after the October 7, 2023 attacks, i.e., outside our sample period. Media scholars argue that prior to this, Israel had little incentive to pursue a full shutdown, as there were cheaper and more targeted alternatives for restricting social media activity. For instance, authorities could jam signals in specific geographic areas during protests or launch Distributed Denial of Service (DDoS) attacks on particular platforms used for coordination. Neither of these methods interferes with our instrument. Second, we provide evidence that the technology of international news production does not depend on conflict-zone internet outages: while internet outages have a strong and significant negative effect on Twitter activity by users in Israel and the Palestinian Territories, they have no measurable impact on newswires about the conflict by major international news agencies. There is abundant anecdotal evidence that foreign correspondents in major international media outlets and news agencies, reporting from the conflict zone, typically have satellite internet connections and power surge protection, which make their communications largely immune to lightning strikes and local internet failures.

Overall, we conclude that an indicator for days with internet outages—defined by the occurrence of local lightning strikes and the absence of observable internet traffic—is both relevant and excludable. It is relevant because it explains variation in the volume of tweets originating from the conflict zone. It is excludable because it is highly unlikely to correlate both with the unobservable component of newsworthiness of conflict events and with the technology of traditional news production, which does not rely on information sourced via social media. (We discuss the validity of these identification assumptions in greater detail below.)

The magnitude of the results is substantial. On days without internet outages in the conflict zone, the number of tweets from Israel and the Palestinian Territories about the conflict is 12% higher than on days with internet disruptions. Conflict-related coverage on an average U.S. TV channel is 1.8 times longer on days without internet outages compared to days with outages. In addition, the absence of internet disruptions in the conflict zone results in a 2.7 percentage point increase (or 31.3%) in the likelihood of prime-time coverage. Compared to days with internet
outages, U.S. TV news is 5.6 percentage points more likely to adopt a pro-Palestinian slant when there are Palestinian fatalities (relative to a baseline frequency of 12.7% pro-Palestinian stories), and 10.6 percentage points more likely to adopt a pro-Israeli slant when there are Israeli fatalities (relative to a baseline frequency of 6.9% pro-Israeli stories). When the conflict is covered, stories aired on days without internet outages mention civilians 8.4% more often, civilian casualties 33% more often, and young people 50% more often, compared to their respective means. At the same time, mentions of U.S. diplomats decrease by 40.8%, and mentions of elections decrease by 34.5%. On days without internet outages in the conflict zone, conflict stories are 3.3 percentage points more likely to mention small, specific geographic locations in the Palestinian Territories, and exhibit a 0.12 standard deviation increase in the intensity of negative emotional language. Finally, conflict-related stories posted by U.S. TV networks on their official Twitter accounts receive 24.2% more likes, shares, and comments on days when internet access is not disrupted in the conflict zone, compared to days with outages.

Surveys of traditional media journalists who rely on social media in their reporting indicate that many are concerned about the accuracy of information found on these platforms (Cision, 2017, 2020). While we lack data to directly assess the accuracy of online sources used by journalists in our context, the risk of misinformation is well documented: false news circulates widely on social media (e.g., Allcott and Gentzkow, 2017; Vosoughi et al., 2018), and conflict is a domain where actors often have strong incentives to spread misleading narratives (Patrikarakos, 2017; Singer and Brooking, 2018).<fnref n="3" /> Whether such misinformation reaches audiences through traditional media depends on the fact-checking standards of each outlet, which vary in practice. Since our analysis focuses on major U.S. television networks—all of which maintain in-house fact-checking teams—it is reasonable to assume that these outlets are able to verify social media content when they choose to do so.<fnref n="4" />

Although the Israeli-Palestinian conflict is unique in many respects, there is reason to believe that our findings have some external validity. Anecdotal evidence from other conflicts, such as the Syrian civil war and the war in Ukraine, suggests that citizen journalists have played a meaningful role in shaping the narrative of war reporting (see, for example, Patrikarakos, 2017; Singer and Brooking, 2018; Higgins, 2021; Kharchenko, 2023).

We contribute to the literature on the role of media in conflicts (see, for instance, Yanagizawa-Drott, 2014; Adena et al., 2015; Berman et al., 2018; Durante and Zhuravskaya, 2018; Gagliar-ducci et al., 2020; Armand et al., 2020; Sonin and Wright, 2022; Adena et al., 2020; Fetzer et al., 2024). We are the first to systematically document that user-generated content posted on digi-

<footnote n="3">³One example is a Facebook post by the Palestinian Ministry of Interior during the summer of 2014 promoting the campaign “Be Aware,” aimed at guiding Palestinian social media activists. The post stated (in Arabic): “(1) *Anyone killed or martyred is to be called a civilian from Gaza or Palestine before we talk about his status in Jihad or his military rank.* (2) *Do not forget to always add ‘innocent civilian’ or ‘innocent citizen’ in your description of those killed in Israeli attacks on Gaza.*” (https://www.facebook.com/moigovps/posts/946767052016152, accessed January 3, 2025).</footnote>

<footnote n="4">⁴Local associations of citizen journalists often assist foreign correspondents in verifying information from online sources in the conflict zones. See, for instance, a story in the *Columbia Journalism Review* about the Local Coordination Committees of Syria, a network of citizen journalists operating during the Syrian conflict: *Columbia Journalism Review: “Straight News from the Citizen”* (accessed January 3, 2025).</footnote>
tal media from conflict zones helps focus traditional news coverage on civilian suffering, rather than on official policies or military strategies. This rebalances the information sphere, typically dominated by actors with greater military and propaganda power, toward the disadvantaged. As a result of citizen journalism from the ground, traditional media tend to slant coverage more in favor of the side experiencing greater civilian casualties, typically the side with weaker capabilities. Furthermore, since governments involved in conflicts seek to avoid negative publicity related to civilian suffering (Durante and Zhuravskaya, 2018), the effect of digital media on traditional media that we uncover imposes additional accountability constraints on actors engaged in warfare.

Our paper also contributes to the burgeoning literatures on the political economy of social media (surveyed by Zhuravskaya et al., 2020; Aridor et al., 2024) and traditional media (surveyed by DellaVigna and Gentzkow, 2010; DellaVigna and La Ferrara, 2015; Enikolopov and Petrova, 2015). Much of this literature examines how either social media or traditional media affect citizens directly. A small but growing body of research explores the interplay between social and mainstream media. Cagé et al. (2020) analyze how the incentives to invest in investigative journalism changed with the advent of online media in environments with relatively weak legal protections for intellectual property rights. Cagé et al. (2024) estimate the causal effect of spikes in social media popularity of a particular topic on the likelihood that traditional news media will cover that topic (see also related work by Sen and Yildirim, 2016; Leung and Strumpf, 2023).<fnref n="5" />

Our contribution to this literature is threefold. First, we focus on news about conflict, which allows us to move beyond merely documenting a causal effect of digital media on the extent of traditional media coverage. Our paper is the first to shed light on the impact of digital media on both the slant and the nature of coverage within a specific domain, namely, conflict reporting. Second, we use a novel instrument based on local internet outages, which helps identify the causal nature of this relationship. Third, our findings suggest that the influence of social media on traditional media reporting extends well beyond signaling public interest in a given topic: traditional journalists use social media as a direct source when they cannot witness newsworthy events themselves.

The rest of the paper is organized as follows. In section 2, we describe data sources and the main variables used in the analysis. In Section 3, we present our empirical strategy and discuss identification assumptions. Section 4 presents the main results. Section 5 illustrates the results with a case study. Section 6 concludes.

<footnote n="5">⁵There is also a large body of research in the field of communications (surveyed by Lewis and Molyneux, 2018) that examines how traditional media use social media in the production and dissemination of news. This work includes surveys of traditional media outlets (e.g., Lysak et al., 2012; Adornato, 2016; Adornato and Lysak, 2017), analyses of content similarity between traditional and social media (e.g., von Nordheim et al., 2018), and citation analysis (e.g., Moon and Hadley, 2014). These papers provide important descriptive evidence but are not concerned with causal identification.</footnote>
## 2 Data sources and the main variables

In this section, we describe the construction of all our main variables of interest and their sources. The time span of the resulting data set is between November 24, 2009 and December 30, 2019. Summary statistics for variables used in the analysis are presented in Tables A1 to A5 in the Appendix.

### 2.1 Conflict-zone newsworthy events

We proxy for newsworthy events in the Israeli-Palestinian conflict using casualty data from both sides. Fatality data are sourced from the Israeli Information Center for Human Rights, *B'Tselem* (http://www.btselem.org/, accessed January 3, 2025), which provides daily information on each fatality in the conflict. The dataset includes the perpetrator's side (Israeli or Palestinian) and basic characteristics of the victims, such as citizenship, gender, and age. Panel A of Appendix Figure A3 presents the total number of casualties over time and highlights the names of particularly deadly campaigns. Data on injuries for each side come from the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA), accessible at https://www.ochaopt.org/data/casualties (accessed January 3, 2025). Information on the occurrence and daily number of demonstrations, including violent riots, is provided by the Armed Conflict Location & Event Data Project (ACLED). These data are available for Israel and the Palestinian Territories from January 2016 onward at https://acleddata.com/ (accessed January 3, 2025). Data on house demolitions in the West Bank, covering the entire study period, are also sourced from *B'Tselem* (http://www.btselem.org/, accessed January 3, 2025). We also use UNOCHA data on electricity supply in Gaza, available from January 2017 at https://www.ochaopt.org/page/gaza-strip-electricity-supply (accessed January 3, 2025). This dataset includes information on electricity supply and shortages (in kilowatts), as well as the estimated daily number of hours with electricity. In addition, we create a binary variable that identifies days when Israel is involved in other armed conflicts in the region, such as the Israel-Lebanon border clashes on August 3, 2010, and on December 15 and 16, 2013, as well as repeated incidents on the Golan Heights along the Israel-Syria ceasefire line, the list of episodes and data sources are provided in Appendix Tables A6 and A7.

### 2.2 US TV coverage of the Israeli-Palestinian conflict

To construct our main outcome variables—describing whether and how U.S. TV covers the Israeli-Palestinian conflict—we rely on the U.S. Television News Archive, a part of the Internet Archive (https://archive.org/details/tv/, accessed January 3, 2025). Our analysis focuses on the following U.S. networks: ABC, CBS, CNN, FOX, MSNBC, NBC, PBS, and Bloomberg. For comparison, we also include data for Al Jazeera America, a Qatari network that was available in the U.S. Data for all U.S. networks, except Bloomberg, are available starting July 2, 2009. Bloomberg coverage begins on December 5, 2013. Al Jazeera America operated from August 20, 2013, to April 12, 2016. After the channel shut down, Al Jazeera continued its presence in the
U.S. primarily through its digital platform, AJ+. We web-scraped full transcripts of all news broadcasts from these networks using the following tags defined by the archive: "east jerusalem," "gaza," "gaza city," "gaza strip," "hamas," "hebron," "hezbollah," "israel," "jerusalem," "palestine," "palestinians," and "westbank." We then analyzed these transcripts to identify stories related to the conflict zone and constructed measures to assess the extent of coverage, the slant of reporting, the topics discussed, content similarity across networks, and the emotional intensity of the coverage. In what follows, we provide a detailed description of how these variables were constructed.

**The extent of conflict coverage.**—For each network, we define news stories about the conflict zone as transcript segments in which the stemmed keywords for both sides of the conflict—"Israel" and "Palestin," or "Israel" and "Gaza"—appear multiple times. Specifically, we identify all contiguous news segments in which these keywords occur within a maximum of three minutes of each other.<fnref n="6" /> We define a conflict story as a news segment that mentions these actors at least five times. Based on this definition, our dataset includes 22,543 U.S. TV news stories about the conflict zone and 11,524 stories from Al Jazeera.

To measure the extent of coverage for each TV network and each day, we construct several variables: (1) the number of times each network mentioned conflict-related keywords (a measure that does not rely on defining conflict-related stories), (2) the number of identified news stories about the conflict, (3) a dummy indicating whether the network aired a conflict-related story during prime-time news, and (4) the total length of conflict-related stories (in minutes).

On average, a TV network in our sample runs at least one story about the conflict on 27% of days. Appendix Table A5 summarizes the probability and length of coverage for each TV network (see also Panel A of Appendix Figure A4). Among U.S. networks, Fox News, PBS, and CNN report on the conflict most frequently. The unconditional probability that a story appears on these networks on an average day is about 34%. In contrast, ABC reports on the conflict just 8% of days. All U.S. networks covered the conflict less frequently than Al Jazeera America, which reported on the conflict zone on 76% of days. Appendix Figure A5 shows that U.S. coverage is responsive to conflict events: the probability of coverage is substantially higher on days with fatal casualties than on days without fatal casualties.

**The slant of conflict coverage.**—To measure the slant of conflict coverage for each day and TV network, we rely on LLM. First, we randomly selected 500 conflict-related stories from the Internet Archive, yielding 864 stories about the conflict zone. A human annotator assigned slant scores ranging from -2 (strongly pro-Israel) to 2 (strongly pro-Palestinian), with 0 indicating neutrality. We then fine-tuned a GPT-3 Curie model on the human-coded data to classify the slant of each story. The model was applied to all stories to generate slant predictions. It achieved perfect accuracy on the training sample (accuracy = 1), with a training loss of 0.02 and a validation loss of 0.03. For stories exceeding the model's maximum input length, we split them into sub-segments and averaged the slant scores to obtain the final value.

<footnote n="6">⁶To ensure we capture the full story, we include a margin of one minute before the first keyword and three minutes after the last.</footnote>
Although the theoretical slant range is -2 to 2, the model predicts slant scores between -1 (pro-Israel) and 1.19 (pro-Palestinian) for the stories in our sample, as shown in Appendix Figure A6. As expected, Al Jazeera America exhibits a clear pro-Palestinian slant, with an average score of 0.69, while U.S. TV networks generally display lower slant scores (Panel B of Appendix Figure A4). Among U.S. networks, Fox News has the lowest average slant score (-0.08), indicating its pro-Israel stance, while PBS has the highest average pro-Palestinian slant at 0.24.

**The topics of conflict coverage.**—To analyze the content of U.S. TV news stories covering Israel and the Palestinian Territories, we employ two complementary approaches. First, we calculate the frequency of specific keywords within each news segment about the conflict zone. We divide the number of keyword occurrences by the total number of words in the segment and multiply by 1,000. To reduce the influence of outliers, we winsorize each measure at the 99th percentile of its distribution. We search for keywords across a range of topics, from civilian casualties to the involvement of U.S. foreign policy officials in conflict resolution. The list of topics and their corresponding keywords is provided in Appendix Table A8. This approach is based entirely on our own selection of topics and keywords.

As a hands-free alternative, we use the Correlation Explanation (CorEx) model (Gallagher et al., 2017) to identify topics in the stories. Unlike Latent Dirichlet Allocation (LDA), which assumes topics are independent, CorEx leverages information-theoretic principles to identify deeply intertwined and overlapping themes. This ability to capture complex semantic relationships is particularly valuable in the context of conflict-related reporting, where topics are often closely connected. CorEx supports soft clustering, allowing words to belong to multiple topics with varying degrees of relevance. We apply CorEx in an unsupervised manner, specifying 50 topics, and consider a topic to be present in a story if its probability is greater than or equal to 0.5. This approach enables us to capture nuanced and interrelated themes without the need for predefined labels. Details of all 50 CorEx topics and summary statistics are presented in Appendix Section B.

To characterize the informativeness of conflict-related broadcasts, we identify whether they mention specific, small geographical locations in Israel and the Palestinian Territories.<fnref n="7" /> We also track whether the news segments reference social media platforms such as Twitter, Facebook, Instagram, and YouTube, or news agencies.

**The emotional intensity of conflict coverage.**—To measure the emotional intensity of conflict-related broadcasts by day and TV network, we use the NRC Emotion Lexicon, first developed by Mohammad (2018). This lexicon, based on human evaluations of word-emotion associations, assigns continuous intensity scores between 0 and 1 to nearly 6,000 English words across eight basic emotions: anger, fear, sadness, disgust, joy, trust, anticipation, and surprise.

<footnote n="7">⁷We use the list of all locations in Israel and the Palestinian Territories from the GeoNames dataset (https://www.geonames.org/, accessed January 3, 2025). To proxy for the informativeness of a broadcast, we exclude major, widely known locations that are unlikely to convey novel information and are frequently mentioned on U.S. TV, such as Bethlehem, East Jerusalem, Israel, Jerusalem, Gaza (Strip), the Old City, Palestine, Ramallah, Tel Aviv, and the West Bank.</footnote>
An update by Zad et al. (2021) converts each association into a binary metric. For each conflict-related story and each emotion, we compute emotional intensity using both the original and updated lexicons by summing the scores of all stemmed words (assigning a score of zero to words not found in the lexicon), dividing by the total word count, and multiplying by 100. This yields first-approximation measures of emotional intensity for each story. We use the continuous lexicon scores as our baseline and establish full robustness to the binary lexicon scores. The continuous scores allow us to illustrate differences in conflict-news coverage using pairs of words that are equally distant in emotional intensity.

To capture more nuanced emotional content, we also apply the Contextual Sentiment Analysis method developed by Hutto and Gilbert (2014), which adjusts for degree modifiers, contrastive structures, and negations.<fnref n="8" /> This approach yields two additional measures for each story: positive contextual sentiment and negative contextual sentiment.

We then aggregate both emotion and contextual sentiment scores by taking the *maximum* value across all conflict-related stories for each network on each day (provided there is at least one such story). We choose the maximum rather than the mean to preserve variation in emotional expression, as some news formats allow for little or no emotional content. Finally, we compute average scores by network and day for each individual emotion, as well as aggregate measures for all negative, all positive, and all neutral emotions. All resulting measures range from 0 to 100.

**The similarity of conflict coverage across TV networks.**—We construct measures of the similarity between conflict-related stories by different networks on the same day. This is achieved by first reducing dimensionality using embeddings, and then calculating cosine similarity. We use the sentence-transformer model *paraphrase-MiniLM-L6-v2*, developed by Reimers and Gurevych (2019), which is designed to capture nuanced semantic relationships between texts. This model was fine-tuned on a diverse dataset of over one billion sentence pairs sourced from platforms such as Reddit, Wikipedia, and Quora, enabling it to map each text into a 384-dimensional dense vector space. In our analysis, we define “text” as the combined transcripts of all conflict-related stories aired by a given network on a specific day. We compute cosine similarity to quantify the semantic similarity between the texts of each network pair for every day in the dataset, and then average these similarities across relevant groups of network pairs, depending on the comparison of interest. The resulting measure ranges from -1 to 1. Specifically, we construct daily similarity measures for the following groupings: all available networks; all U.S. networks compared to Al Jazeera; U.S. cable networks; and U.S. non-cable networks.

**Online Activity of U.S. TV Networks.**—To construct proxies for consumer interest in U.S. TV news coverage of the conflict, we collected all Facebook posts from the official accounts of major U.S. TV networks using Facebook’s CrowdTangle API.<fnref n="9" /> We collected posts

<footnote n="8">⁸Unlike simple word-score summation, this method considers context. For example, “I am very happy” and “I am not happy” both include the word “happy,” but their emotional tone differs. Contextual Sentiment Analysis captures these differences in sign and intensity, while basic summation does not.</footnote>

<footnote n="9">⁹We accessed the data via https://apps.crowdtangle.com/ on May 23, 2024. This service was discontinued on August 14, 2024. When operational, it provided access to historical Facebook post data.</footnote>
from the following verified accounts, which regularly featured international news: “@ABCNews” (ABC), “@CBSNews” (CBS), “@cnn” and “@cnninternational” (CNN), “@FoxNews” (Fox News), “@msnbc” (MSNBC), “@NBCNews” and “@NBCNewsWorld” (NBC), “@BloombergTelevision” (Bloomberg), and “@newshour” and “@pbs” (PBS). All networks had active Facebook accounts before the start of our observation period, with the exception of NBC and MSNBC. The “@NBC-News” page became active in October 2010, “@NBCNewsWorld” in May 2011, and “@msnbc” in May 2012. We include data from these accounts only from their respective launch dates onward. Using CrowdTangle’s historical data tool, we retrieved all Facebook posts from these accounts with any of the keywords: “Israel,” “Gaza,” or “Palestin.” For each post, we collected the full content along with metadata, including total engagement metrics such as likes, shares, and comments.

## 2.3 Twitting from the conflict zone

We web-scraped all original tweets (excluding retweets) containing the stemmed keywords “Israel,” “Palestin,” and “Gaza” from Twitter’s inception through December 30, 2019, in two collection waves. The first wave occurred prior to the availability of the Twitter API and ended on April 18, 2016. The second wave, conducted using the API, covered tweets from April 19, 2016, to December 30, 2019. In total, we collected 144,855,991 tweets.

We identified the language of each tweet and found that 105,616,403 (73%) were in English, as expected given the use of English-language keywords. These tweets were posted by 9,161,383 unique Twitter accounts. Using self-reported profile information, we classified accounts into categories such as ordinary individuals, media outlets, officials, businesses, and organizations. To determine geolocation, we matched reported user locations to the GeoNames dataset (https://www.geonames.org/, accessed January 3, 2025). Of the English-language tweets, 7% (7,263,514) originated from the conflict zone, with 33% of those from the Palestinian Territories.

We then used machine learning to classify each tweet as conflict-related or not using a bag-of-words approach and a Naive Bayes classifier trained on 5,000 manually labeled tweets (4,000 for training and 1,000 for testing). Among English-language tweets from Israel and the Palestinian Territories, 43% were about the conflict, 1,741,885 from Israel and 1,368,040 from the Palestinian Territories. We performed human validation of this classification: a research assistant independently labeled a subset of tweets. The classifier performed comparably to the human labeler, as shown in Appendix Table A9. We use the number of conflict-related tweets originating from the conflict zone as a proxy for user-generated content on digital platforms.

The temporal dynamics of these tweets closely mirror the dynamics of conflict fatalities, as shown by comparing the two panels of Appendix Figure A3. Appendix Figure A7 further illustrates the composition of conflict-related tweets from the conflict zone by language and location.
## 2.4 Internet outages

To identify exogenous variation in internet access in the conflict zone, we rely on two sources of data on internet outages: lightning strikes and technical failures.

**Lightning strikes.**—We obtain lightning strike data from the World Wide Lightning Location Network (WWLLN) dataset.<fnref n="10" /> This dataset provides precise coordinates and times-tamps for cloud-to-ground lightning strikes worldwide. Using these data, we construct a time-series indicator for days with lightning strikes in Israel or the Palestinian Territories, excluding deserts—defined as subdistricts with fewer than 250 people per square kilometer. Since not all lightning strikes affect internet infrastructure, we only focus on strikes that occurred within 500 meters of a cellular antenna. To determine antenna locations, we rely on OpenCellID data (https://www.opencellid.org/ accessed January 3, 2025). We define “thunderstorm days” as those with at least one such strike. Thunderstorms occurred on 12% of days during the observation period, with an average of 137 antennas struck per day (with a standard deviation of 332).

Lightning strikes tend to be spatially correlated, often affecting large parts of the conflict zone simultaneously. Due to the region's limited size, we rely on temporal rather than spatial variation in lightning activity. Panel A of Appendix Figure A8 provides an illustration, mapping the locations of strikes on a stormy day and showing affected areas, deserts, antennas, and strike locations in a vicinity of an antenna. Strikes are also highly seasonal, with 88% occurring between October and March. To account for this seasonality, all specifications include calendar-month fixed effects. Panel B of Figure A8 visualizes the seasonal distribution of lightning activity.

As thunderstorms often co-occur with other weather events that can both influence how lightning affects internet connectivity and directly impact human behavior, we control for daily rain and wind conditions in the conflict zone. The data are sourced from the Israel Meteorological Service Weather Data.<fnref n="11" /> Weather data are provided hourly at a 1×1 km resolution. We aggregate these data to the daily level, weighting by the population density of each grid cell to capture weather conditions only in areas where people actually live.<fnref n="12" /> Rainfall is measured in millimeters, and wind speed in meters per second.

**Technical failures.**—We measure outages caused by technical failures using the Internet Outage Detection and Analysis (IODA) methodology developed by researchers at the Center for Applied Internet Data Analysis (Dainotti et al., 2011; Padmanabhan et al., 2019). This approach identifies sudden drops in internet traffic, measured as the number of visible Border Gateway Protocol (BGP) prefixes, between a given Autonomous System and the global internet.<fnref n="13" /> Under normal conditions, IP activity is consistently visible due to continuous use, including automated

<footnote n="10">¹⁰Collected by the University of Washington, available under a license agreement at http://wwlln.net (accessed January 3, 2025).</footnote>

<footnote n="11">¹¹http://www.iacdc.tau.ac.il/what-is-space-weather/, accessed January 3, 2025.</footnote>

<footnote n="12">¹²Population density data are from the NASA Socioeconomic Data and Applications Center (https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-rev11, accessed January 3, 2025).</footnote>

<footnote n="13">¹³An Autonomous System (AS) is a collection of IP addresses managed under a unified routing policy, typically by an internet service provider (ISP), and corresponds to a specific geographical area. The BGP prefix identifies the AS to which an IP address belongs.</footnote>
traffic that relies on off-grid power generators. A sharp drop in visible BGP prefix counts indicates an internet outage. We define “absence of visible traffic” as a day when traffic from at least one AS falls below six standard deviations from its 30-day moving average (using uniform weights). These severe outages occurred on 4.5% of observed days.

Internet traffic is highly correlated across interconnected networks: on 85% of days with an “absence of visible traffic,” at least four out of six ASs were affected. Appendix Figure A9 illustrates these system disruptions, showing significant declines in BGP prefix counts across all ASs.

We have verified the claim of IODA creators (Dainotti et al., 2011; Padmanabhan et al., 2019) that the sudden fall in the internet traffic does not reflect the demand-driven differences in the internet use in application to our context. In Appendix Table A10, we show that BGP prefix count is not lower on weekends (compared to weekdays) and at nighttime (compared to daytime), whereas the Twitter activity in the conflict zone is, as one would expect.

The “absence of traffic” incidents are distinct from power outages. In both Israel and the Palestinian Territories, many active IP addresses are powered by independent generators. For example, internet outages in the Gaza Strip were relatively rare during our observation period compared to frequent electricity outages, with residents often relying on backup generators. Below, in the balance regressions, we show that internet and power outages are uncorrelated.

## 2.5 Other traditional-media data

**Domestic U.S. news pressure.**—We use the Vanderbilt Television News Archive (VTNA) to compute domestic U.S. news pressure, namely a proxy for the importance of domestic U.S. newsworthy events that can crowd out international news. We use the methodology first developed by (Eisensee and Stromberg, 2007) and adapted to news coverage of international news by Durante and Zhuravskaya (2018). We use VTNA data on the length of each story aired on ABC, CBS, and NBC daily on prime-time news and exclude stories that mention any foreign country or region to form the list of domestic news stories. We define domestic news pressure as the total airtime devoted to the top three domestic stories each day during prime-time broadcasts on ABC, CBS, and NBC.

**Newswires.**—We collected all newswire articles related to the conflict zone published by the two major English-language news agencies: Reuters and the Associated Press (AP). These articles were retrieved from the Factiva database, available by subscription at: https://professional.dowjones.com/factiva/ (accessed January 3, 2025). We searched for wires containing the same stemmed keywords: “Israel,” “Palestin,” and “Gaza.” For each retrieved article, we recorded the date and time of publication. The data begin on September 1, 2012. Using these data, we construct measures of the extent of conflict coverage by newswires, as well as the similarity in content between newswire reports and TV conflict stories.
# 3 Empirical Strategy

We aim to identify the causal effect of digital media activity originating from the conflict zone on U.S. TV news coverage of the conflict. We aim to causally estimate the following equation, where the unit of observation is a U.S. TV network by day:

$$TVnews_{n,d}^{US} = \alpha_0 DigitalMedia_{d}^{CONF} + \alpha_1 Fatalities_{d}^{PS} + \alpha_2 Fatalities_{d}^{IL} + \bar{X}_{d}'\gamma + \delta_n + \varepsilon_{n,d}, (1)$$

where n indexes TV networks and d indexes days. The dependent variable TVnews$_{n,d}^{US}$ captures various aspects of U.S. TV coverage of the Israeli-Palestinian conflict, including the extent of coverage, slant, topics, audience engagement, emotional intensity, and other measures describing conflict-related content aired on day d by network n. The main explanatory variable DigitalMedia$_{d}^{CONF}$ reflects online information posted by eyewitnesses in the conflict zone, i.e., Israel and the Palestinian Territories. It is both unobserved and endogeneous, due to potential reverse causality and omitted variables. While we do not observe the full universe of online content, we proxy it using the daily log number of tweets about the conflict originating from the conflict zone.<fnref n="14" /> This measure is subject to both measurement error and endogeneity. In the next section, we describe our instrumentation strategy using conflict-zone internet outages. We also consistently report reduced form relating U.S. TV reporting of the conflict directly to conflict-zone internet outages.

The variables Fatalities$_{d}^{IL}$ and Fatalities$_{d}^{PS}$ are indicators for the occurrence of fatal casualties on the Israeli and Palestinian sides of the conflict, respectively. There are at least some fatalities on 18% of days over our observation period. $\delta_n$ is the TV network fixed effect. The vector of additional control variables $X_d$—included to aid identification and improve precision—contains the following components. To account for seasonality, climate variation, and the rise in social media penetration over time, we include year, month-of-year, and day-of-week fixed effects. To control for the impact of prior conflict intensity, we include the inverse hyperbolic sine (IHS) transformation of the total number of casualties on each side of the conflict during the four weeks preceding day d. A dummy for the two-week period of the 2014 Gaza War captures the most intense episode of fighting during our observation window, which accounts for over half of all fatal casualties (see Panel A of Appendix Figure A3). To account for variation in the ex-ante likelihood that U.S. media would cover the Israeli-Palestinian conflict, we include a measure of U.S. domestic news pressure. We also control for weather conditions that could affect both conflict events and the effectiveness of our instrument in predicting digital media activity (discussed below). In particular, we include a dummy for rainy days and continuous measures of rain and wind intensity. To isolate the Israeli-Palestinian conflict from other episodes of regional violence, we control for a dummy indicating days when Israel was involved in other armed conflicts in the region (specifically with Syria and Lebanon). To account for possible differences in data collection procedures, we also include a dummy for the period corresponding to the second wave of Twitter data collection. $\varepsilon_{n,d}$ denotes the error term. As a baseline, we

<footnote n="14">¹⁴Note that the number of daily tweets is strictly positive for all days in the sample.</footnote>
cluster standard errors at the day level. We also report robustness to alternative assumptions
about the variance-covariance matrix.

**3.1 Identification**

Equation 1 cannot be estimated using OLS because digital media activity in the conflict zone is endogenous to U.S. TV coverage of the conflict, due to both omitted variable bias and reverse causality. Unobserved newsworthiness of events may simultaneously influence digital posts by Israelis and Palestinians as well as U.S. television coverage. In addition, unrelated major newsworthy events may crowd out both digital media activity related to the conflict and its coverage on U.S. TV. Furthermore, television content may trigger digital media responses in the conflict zone. Finally, digital media activity is measured with error, as it is proxied by the number of conflict-related tweets from the conflict zone.

To address these identification challenges, we rely on two sources of exogenous variation: lightning strikes and technical failures leading to internet outages. First, in the absence of power-surge protection, lightning strikes can significantly impair users’ ability to access the internet. This is because electrostatic discharges caused by lightning induce power surges that damage internet infrastructure, resulting in outages.<fnref n="15" /> To account for seasonality in thunderstorm frequency (see Panel B of Appendix Figure A8), we control for calendar month fixed effects. The effect of lightning on ICT infrastructure also depends on local weather conditions (Schulman and Spring, 2011); therefore, as mentioned above, we control for rain and wind intensity. Finally, we include calendar year fixed effects to account for broader temporal trends, including climate change, which accelerated over our observation period.

Second, internet outages can result not only from physical damage to infrastructure but also from technical failures, such as software or configuration errors, that temporarily disrupt connectivity between a specific geographic area and external servers. As described in Section 2.4, we use a methodology developed by computer scientists to detect days when internet users in Israel and the Palestinian Territories were unable to connect to the broader internet. We refer to the variable indicating such days as absence of traffic.

As the instrument, we use a dummy indicating the incidence of internet outages, defined as the union of days with lightning strikes and days with an absence of traffic. The dummy for the absence of traffic is available only for a subset of the observation period, specifically starting in 2013. Year fixed effects account for whether the absence of traffic is incorporated into our measure of internet outages.

Internet outages can serve as a valid instrument for digital media activity in the conflict zone provided they are a sufficiently good predictor of digital media activity in the conflict zone and the assumptions of exogeneity and the exclusion restriction are satisfied. We discuss each of these aspects in the next three subsections.

<footnote n="15">¹⁵There is a consensus among ICT specialists that lightning strikes damage ICT equipment and reduce connectivity (Zeddam and Day, 2014; Martin, 2016). In economics, this fact was used by Manacorda and Tesei (2020) and Guriev et al. (2020), who exploit cross-sectional variation in lightning frequency to predict the speed of mobile ICT technology rollout.</footnote>
3.1.1 Instrument’s relevance

Both the indicator for lightning strikes and the dummy for the absence of internet traffic are strong and significant predictors of tweeting from the conflict zone, and so is the combined measure of internet outages. We illustrate the results of the first stage in Figure 1 and present formal analysis in Table 1. In the table, the dependent variables are the log of the total number of daily tweets from the conflict zone and the log number of conflict-related daily tweets from the conflict zone. The key explanatory variables—dummies for internet outage, lightning strikes, and absence of traffic—are included sequentially. As specified in Equation 1, the unit of analysis is network-by-day, and all relevant controls are included. Panel A uses the full sample, while Panel B focuses on the subset of days for each TV network when at least one story about the conflict appeared on this network’s news. The coefficients on the dummies for internet outages and for each of its components are consistently negative and statistically significant.

At the bottom of each panel, we report F-statistics for the excluded instrument. In all four specifications, the F-statistic exceeds the conventional threshold for a strong instrument when using either the combined internet outage variable or the absence of traffic. The F-statistic for the dummy variable representing lightning strikes alone falls slightly below the conventional threshold in three out of the four specifications.

On average, the number of daily conflict-related tweets is 12% lower on days with an in-
ternet outage. It is 6.5% lower on days with lightning strikes. It is 18% lower on days when
there is no visible internet traffic in the conflict zone. Neither source of internet outages implies
a complete shutdown of internet access in the conflict zone. First, where available, power-surge-
protection infrastructure significantly reduces the risk of local outages during thunderstorms.
Second, users with satellite internet connections are unaffected by the absence of visible in-
ternet traffic, as their data is routed through a different autonomous system. Third, not all
thunderstorms are strong enough to generate significant power surges.

Appendix Table A11 shows that tweets by Israeli and Palestinian usersare similarly affected
by outages. In particular, the share of conflict-related tweets from Israel in total conflict-related
tweets from both Israel and Palestinian Territories, as well as the shares of tweets composed in
English only, in English and Arabic, or in English and Hebrew, are not significantly associated
with our measures of internet outages.

To illustrate the relevance of the instrument, we examine the tweeting activity of an au-
tomated Twitter account, *Islamic Prayer Times from Gaza* (@IPT_Gaza), which informed
Muslims about daily prayer times.<fnref n="16" /> This account operated from August 3, 2011, to April 6,
2016. We collected information on all scheduled Muslim prayer times and verified that internet
outages, lightning strikes, and the absence of internet traffic in the Gaza autonomous system
significantly predict instances when the bot was expected to tweet but did not. The results are
presented in Appendix Table A12.

Theoretically, we expect ordinary people to be the primary compliers in the relationship

<footnote n="16">¹⁶An example of a typical tweet from this bot account: “5:07 Now #Fajr Prayer Time for #Gaza #Palestine”.</footnote>
between conflict-zone internet outages and digital media activity, as they are more likely to
rely on local cellular networks and less likely to have access to power surge protection than, for
example, local journalists or officials. Since the definition of compliers depends on unobserved
counterfactuals and we lack data on the use of satellite phones and power surge protection, this
premise is untestable.

3.1.2 The exogeneity of the instrument

Internet outages should not be correlated with factors that influence traditional media foreign correspondents' coverage of the Israeli-Palestinian conflict, except through their effect on digital media activity in the conflict zone. In particular, internet outages should be unrelated to the newsworthiness of conflict events and to the actions of conflict actors (ensuring exogeneity), as well as to the ability of traditional media reporters to produce news stories from the conflict zone without relying on digital media as a source of information (ensuring the exclusion restriction). These assumptions cannot be rigorously tested, as they concern unobservable factors. In this and the next section, we explain why the identifying assumptions are reasonable in our context and why violations are unlikely, providing supporting evidence.

We begin by assessing balance with respect to the most salient observable aspects of the newsworthiness of conflict events. Figure 2 presents the results. We regress the instrument and its two components on the daily counts of different types of casualties, controlling for all baseline covariates. We find that the instrument is uncorrelated with both the inverse hyperbolic sine (IHS) transformations and the dummy indicators of fatalities and injuries on either side of the conflict, or with the demographics of these casualties, or with the 2014 Gaza War dummy.17 We also show that the instrument is uncorrelated with the incidence or intensity of protests in Gaza, including violent riots. Importantly, this is the case only when we include the control for the intensity of rain, which itself is strongly negatively correlated with demonstrations and riots (illustrating that weather controls are important for identification). We also show that house demolitions in the West Bank are also balanced across days with and without internet outages in the conflict zone. Finally, power outages in Gaza are uncorrelated with the instrument and its both components.

These results provide suggestive evidence against several potential concerns. First, conflict
attacks may be influenced by weather conditions, and lightning strikes tend to coincide with
generally worse weather. However, we do not find a significant correlation between lightning
strikes and casualties. Moreover, all regressions control for the intensity of rain and wind, factors
arguably more relevant for the planning of attacks than lightning itself, given that military forces
on both sides possess equipment to mitigate the impact of lightning-induced power surges.

Second, ICT infrastructure might be a direct target or collateral damage during attacks, or
that conflict actors might deliberately disconnect autonomous systems in the conflict area from

<footnote n="17">¹⁷As there are relatively few Israeli casualties over our observation period, most of whom are military, there is not enough variation to disaggregate Israeli fatalities by type. However, we show that the age of injured Israelis is not correlated with the instruments.</footnote>
the internet—both of which could reduce visible internet traffic. To the extent that physical damage to internet infrastructure correlates with casualties, we show that the absence of internet traffic is orthogonal to them. Not all infrastructure attacks lead to human casualties, of course. However, such attacks would likely bias our IV estimates downward: because attacks themselves are newsworthy, any infrastructure damage should, a priori, be associated with increased—not decreased—coverage of the conflict in traditional media.

Third, a valid concern is whether conflict actors can disconnect an area from the internet without causing physical infrastructure damage. In principle, given Israel's intelligence and military capabilities, it is within the IDF's power to disconnect Gaza or the West Bank from the internet by “pulling the plug.” If Israel were to do this, it would likely bias our results against finding an effect and thus would not threaten our identification strategy. This is because an intentional internet shutdown is costly and would plausibly occur only during major, newsworthy events—such as a deadly attack, a riot, or a large rally. However, extensive anecdotal evidence suggests that the first instance of Israel deliberately shutting down internet access in Gaza occurred only after our observation period, in the aftermath of the October 7 2023 attacks.<fnref n="18" /> One possible reason why Israel did not shut down the internet in Gaza prior to October 2023 is that it has several alternative, less costly methods to disrupt communications in the Palestinian Territories. For example, the military can jam mobile internet signals in a specific area (e.g., where a riot is taking place) by broadcasting strong radio signals on the same frequency. Additionally, hackers on both sides of the conflict can disrupt particular websites or services by launching denial-of-service (DDoS) attacks. These alternative methods do not result in a loss of visible internet traffic and therefore do not affect our instrument.

### 3.1.3 Exclusion restriction

The exclusion restriction would be violated if U.S. TV correspondents relied on local internet infrastructure to transmit their reports from the conflict zone. However, this is extremely unlikely for any form of conflict-related reporting, and in particular for U.S. television coverage of the Israeli-Palestinian conflict.

<footnote n="18">¹⁸See https://www.accessnow.org/publication/palestine-unplugged/, accessed January 3, 2025, for the description of the internet shutdown in October 2023. Before this shutdown, there were several instances, when observers expected Israel to act, but these expectations did not materialize. For example, during the Israeli Gaza Operation Pillar of Defense in November 2012, which—during our observation period was the second most deadly attack on Gaza (with the 2014 Gaza War being the first)—rumors of a possible internet blackout of Gaza by Israel have proliferated on social media. During that time, Michael Dahan, a professor of Internet and Politics at Sapir College in Israel, in an interview, argued that a possible reason for Israel to disconnect Gaza from the internet would be to prevent citizens from recording and posting information about the presence of Israeli soldiers, highlighting the fact that this would bias our estimates against finding the effect. Dahan argued that this is because the “internet shutdown would have little effect on Hamas, which relies on Egyptian networks and long-range walkie-talkies to communicate” (see https://www.huffpost.com/entry/israel-gaza-internet_n_2159407, accessed January 3, 2025). There is no evidence that these rumors have materialized in 2012 or at any other point in time during our observation period (see, for instance, https://www.eff.org/deeplinks/2012/11/social-media-internet-access-are-latest-weapons-israeli-palestinian-conflict, accessed January 3, 2025). The fact that both fixed and mobile internet worked in Gaza at the peak of the 2014 Gaza War demonstrates that Israel did not shut the internet down even during the most intense fighting. Our data show that the traffic in Gaza autonomous system was stable during the Gaza War (as illustrated by our balancing tests in Figure 2).</footnote>
First, there is ample anecdotal evidence that foreign journalists operating in conflict zones typically use satellite phones, both for security reasons and to ensure reliable transmission regardless of local conditions. This is especially crucial for television reporting, where a stable broadband connection is essential for transmitting large images and video files in real time. For example, the Committee to Protect Journalists notes: "Satellite technology is a critical tool for journalists working in conflict zones where the internet and other international connections are unreliable or have been shut down by authorities. In 2012, in the Syrian city of Homs—an opposition stronghold bombarded by government forces and effectively cut off by authorities seeking to quash news coverage—international and local journalists used satellite technology to file reports and communicate with news organizations."<fnref n="19" /> Similarly, a company that sells satellite-phone equipment to journalists describes its mission as follows: "Satellite phones are in fact playing an increasingly important role in news and information flows during times of crisis. Think about how some journalists have had to report 'up to the minute' news from the front lines in places like Iraq... the journalists are able, for example, to speak to their news desk back in their native or working countries, without having to rely on the telephone networks of the war zones..."<fnref n="20" />

Second, to verify that international traditional-media reporters' news production does not rely on local internet availability, we use newswire data from Reuters and Associated Press (AP), available from September 1, 2012. Newswires report factual information and statistics about conflict events, either gathered by journalists working for the agencies or sourced from official channels on both sides of the conflict. Unlike traditional-media international journalists, whose aim is to tell a story, journalists working for news agencies focus on delivering reliable facts and statistics. This distinction in the nature of journalistic work between news agencies and media outlets allows us to test the implications of the exclusion restriction. If the exclusion restriction holds, newswire reports should not be affected by internet outages in the conflict zone (in sharp contrast to traditional-media reportages, which we show below).

We identify all reports about Israel and the Palestinian Territories using keyword searches and count the number of newswire segments related to the conflict per day for each agency. We then regress both the presence and the number of these reports on internet outage dummy, controlling for all baseline covariates and including news-agency fixed effects. For all count outcomes with zeros throughout the paper, we follow the recommendation of Chen and Roth (2024) and use Poisson Pseudo Maximum Likelihood (PPML) model to accommodate zero values and avoid taking logs. For all count variables without zeros, we use a log-linear model. Columns 1 to 5 of Table 2 present the results. We find that the volume of newswire reporting about the conflict is not affected by internet outages. In Columns 6 and 8, we confirm that the number of all tweets and conflict-related tweets from the conflict zone is significantly lower on days with internet outages, using the same sample of days for which newswire data are available. (Below, we show that U.S. TV coverage of the conflict is also lower on these days.)

<footnote n="19">¹⁹See https://cpj.org/reports/2012/04/armed-conflict/#6, accessed January 3, 2025.</footnote>

<footnote n="20">²⁰See https://www.verasatglobal.com/en/how-satellite-phones-are-helping-journalists/, accessed January 3, 2025.</footnote>
Overall, we conclude that internet outages in Israel and the Palestinian Territories serve as a valid instrument for the availability of information about the conflict on digital media in the conflict zone.

# 4 The effect of conflict-zone digital media on U.S. traditional-media conflict reporting

In this section, we estimate the effect of digital media activity in Israel and the Palestinian Territories on U.S. TV news coverage of the conflict, focusing on three sets of outcomes. We begin by examining the impact on the overall extent of coverage. Next, we explore how U.S. TV covers the conflict, specifically in terms of slant, topics, informativeness, emotional intensity, and similarity of conflict-related news across outlets. Finally, we assess the effect on consumer interest in U.S. TV coverage of the conflict.

## 4.1 The extent of conflict coverage on U.S. TV

Panels A and B of Figure 3 present raw correlations between internet outages in the conflict zone, on the one hand, and tweeting activity from within the conflict zone and the length of daily U.S. TV news coverage about the conflict, on the other. On average, both the number of conflict-related tweets and the duration of U.S. TV coverage are significantly lower on days with internet outages, illustrating the first stage and the reduced form for the extent of coverage, respectively.

Table 3 reports results using the full set of baseline controls (described in the methodology section and listed in the notes to the table). We present three specifications: (i) the reduced form, which links various measures of U.S. TV coverage directly to internet outages in the conflict zone; (ii) the second stage of the 2SLS, where the log of conflict-related tweets is treated as the endogenous variable of interest; and (iii) an uninstrumented regression for comparison. Our outcome variables, measured at the day–TV network level, include: the number of conflict-related keywords, the number of conflict-related stories (including repeated broadcasts), a dummy indicating whether there was at least one prime-time story on the conflict, and the total duration of conflict-related coverage in minutes.

Our main focus is on the reduced form. We find that all measures of U.S. TV coverage of the Israeli-Palestinian conflict are significantly higher on days without internet outages in the conflict zone compared to days with outages (Columns 1–4). There is also a significant intensive-margin effect: conditional on there being any coverage, the length of conflict-related segments is greater on days without internet outages (Column 5). The bottom of the table reports the means of dependent variables and F-statistics from the corresponding first-stage regressions: 27.99 in the full sample and 25.47 in the subsample of days with at least some coverage.

The magnitude of the effect is substantial. On days without an internet outage, the number of conflict-related keywords in U.S. TV news is twice as high, the number of stories is 1.6 times
higher, the total length of coverage is 1.8 times greater, and the probability of prime-time coverage is 2.7 percentage points higher (31% increase relative to a mean of 8.6%). Conditional on there being any coverage, the duration of the conflict-related news is 1.3 times longer.<fnref n="21" />

In Table 3, we also report both instrumented and uninstrumented effects of conflict-related tweets from the conflict zone. Despite the theoretical expectation of upward bias in the uninstrumented estimates for the extent of conflict coverage due to omitted variables related to the newsworthiness of the events, the IV point estimates are substantially larger. A plausible explanation is that, in the presence of treatment effect heterogeneity, IV estimates capture the local average treatment effect (LATE) for compliers—i.e., individuals who do not own satellite phones and have no access to power surge protection and thus can only access digital platforms when the internet is available. These compliers are likely to be ordinary citizens directly witnessing conflict events. U.S. TV journalists may be particularly responsive to such “citizen journalist” content, as it provides precisely the type of on-the-ground information that foreign correspondents often cannot obtain through other means.

In sum, international traditional media reports more on the conflict when digital media in the conflict zone is not silenced by internet outages.

## 4.2 The nature of conflict coverage on U.S. TV

We turn to describing the effect of digital media content in the conflict zone on how U.S. TV talks about the conflict, when it does so.

### 4.2.1 Slant

On average, across all days, the LLM-coded slant of U.S. TV news is not significantly affected by internet outages in the conflict zone. We present this non-result in Appendix Table A14. However, the slant of U.S. TV stories about the conflict is significantly affected by internet outages when there are casualties in the conflict, and the direction of this effect depends on which side the casualties occur. Table 4 presents the results. In addition to the direct effect of digital media activity in the conflict zone, here we include its interactions with indicators for days with Palestinian and Israeli fatalities. As additional instruments for these endogenous regressors, we use the internet outage indicator interacted with the same fatality-day indicators. Every odd column uses the full sample of network-days, with slant defined to be zero when there are no stories about the conflict. Every even column uses the sample of network-days with conflict coverage. In the full sample of days, all three instruments are sufficiently strong. However, in the subsample containing only days with conflict coverage in the specification with additional

<footnote n="21">²¹For conciseness, the tables in the main text omit control variables. In Panel A of Appendix Table A13, we restate the results from Table 3, explicitly reporting the controls for which we have clear theoretical predictions. As expected, all measures of conflict coverage on U.S. TV increase with Palestinian and Israeli casualties and decrease with greater news pressure in the U.S. In addition, prime-time coverage was significantly higher during the 2014 Gaza war. Panel B confirms that the results remain unchanged when we restrict the sample to the period for which data on international newswires are available. The days covered in Panel B match those in Table 2, highlighting the sharp contrast between the effect of internet outages on U.S. TV news and the absence of any effect on international newswires.</footnote>
endogenous interactions, two of the three F-statistics fall below the conventional threshold (7.17 and 7.85). To ensure valid inference under weak instruments with multiple endogenous variables, we construct robust confidence sets for weak identification using linear combination (LC) test developed by Sun (2018) by combining the Anderson–Rubin and K statistics to improve power in the presence of weak instruments (Anderson and Rubin, 1949a; Stock and Wright, 2000).

We report results for three outcomes: the continuous slant measure which ranges from -2 (very pro-Israeli) to 2 (very pro-Palestinian), a dummy for pro-Palestinian slant indicating continuous slant measure above 0.1, and dummy for pro-Israeli slant, indicating continuous slant below -0.1. In the full sample, we find that the continuous slant measure is significantly higher (indicating more pro-Palestinian news coverage) on days with Palestinian fatalities and no internet outages in the conflict zone compared to days with internet outage. There is no significant effect of digital media activity in the conflict zone on days with Israeli fatalities or on days without any fatalities (Column 1). However, when we examine the binary indicators for pro-Palestinian and pro-Israeli slants in the full sample of days, we find that U.S. TV news features significantly more stories aligned with the side suffering fatalities on both sides (Columns 3 and 5). Zooming into the subsample with conflict-related stories, we find significant effects for both interaction terms for all three slant measures (90% confidence sets robust to weak identification are presented in brackets).

The magnitude is as follows. On days with Palestinian fatalities, the probability of a pro-Palestinian story increases by 5.6 percentage points overall, and by 10 percentage points when there is conflict-related coverage, while the probability of a pro-Israeli story decreases by 10 percentage points under the same condition, when digital media in the conflict zone is not muted by internet outages, compared to when it is. On days with Israeli fatalities and conflict-related coverage, the probability of a pro-Israeli story increases by 27 percentage points, and the probability of a pro-Palestinian story decreases by 19 percentage points, when digital media in the conflict zone is not muted by internet outages.

We illustrate these results in Figure 4 and Appendix Figure A10, which present raw-data summaries. The figures confirm that the probability of a pro-Palestinian slant in U.S. TV news is higher when there are Palestinian fatalities and no conflict zone internet outage, while the probability of a pro-Israeli slant is higher when there are Israeli fatalities and no conflict zone internet outage (compared to when there is internet outage). However, they also show that, on days with fatalities on both sides, there is increased polarization in slant when digital media in the conflict zone is active (compared to when it is not): both pro-Israeli and pro-Palestinian broadcasts become more likely.

David Patrikarakos's book, *War in 140 Characters: How Social Media Is Reshaping Conflict in the Twenty-First Century*, helps put these results into context (Patrikarakos, 2017). He argues that social media helps level the playing field in conflicts by shifting narrative power from traditional gatekeepers in war zones to ordinary people (pp. 21, 26). In the context of the Israeli-Palestinian conflict, he contends that this democratization of wartime storytelling has disproportionately benefited the Palestinian side (p. 38). Before the rise of social media, Pa-
trikarakos writes, the IDF was able to dominate the narrative “by controlling journalists' access to war zones, or even refusing to accredit certain journalists [...] The Palestinians, conversely, could offer little by way of a counternarrative. The advent of new media has irrevocably altered this” (p. 21). Over our observation period, Palestinians suffered 19 times more casualties than Israelis; and days with Palestinian fatalities were 8.5 times more frequent than days with Israeli fatalities. As a result, due to asymmetry in human losses, digital media in the conflict zone tended to help the Palestinian side shape international news coverage more in their favor.

To test this further, we examine the details of how U.S. news outlets report on the conflict depending on whether digital media is active or muted due to internet outages.

### 4.2.2 Topics

We begin with an analysis of keywords related to several specific topics. Table 5 presents the main results. We find that U.S. TV stories about the conflict are significantly more likely to mention civilians (by 8% of its mean), young people (by 50% of its mean), and civilian casualties (by 33% of its mean), and are also more likely to include a reference to a photo or video (by 45% of its mean) when there are no internet outages in the conflict zone (Columns 1 to 4). In contrast, they are not more likely to mention any specific reporter or journalist (Column 5).<fnref n="22" /> We also find that Israeli and Palestinian officials are mentioned more (by 16% of its mean, Column 6 of in the second row of Table 5). At the same time, mentions of U.S. diplomats and election-related keywords decrease (by 40.8% and 34.5% of their respective means; Columns 7 and 8). In Appendix Table A15, we show that digital media activity in the conflict zone does not affect keywords related to the Israeli army, military operations, heavy weapons, or Hamas, further supporting the hypothesis that digital media in the conflict zone primarily helps to amplify the voices of civilians. We illustrate the raw correlations between the instrument, on the one hand, and mentions of civilian casualties as well as U.S. diplomacy, on the other, in Panels C and D of Figure 3.

We have verified that these results are not driven by our approach to topic and keyword selection. Using the unsupervised machine-learning CorEx algorithm, we identify 50 topics in U.S. TV news coverage of the conflict and obtain consistent results. Appendix Section B reports all topics and their associated keywords, as well as word clouds for those topics that are most relevant. As with other outcomes, we relate the probability of CorEx topic mentions to digital activity in the conflict zone. Appendix Table A16 summarizes all statistically significant results. Only one topic is found to increase robustly and significantly in the absence of internet outages in the conflict zone: it concerns civilian casualties and families caught in the conflict. No other CorEx topic individually shows a robust relationship with our instrument. However, when we group several topics related to international relations and diplomacy—including references to different countries in the Middle East and the U.S.—we find that the probability of U.S. TV

<footnote n="22">²²Note that uninstrumented OLS regressions yield a positive and significant coefficient on the number of conflict-related tweets from the conflict zone for mentions of reporters, suggesting that, in general, newsworthy events in the conflict zone prompt more stories from reporters, but this is not related to digital media activity on the ground.</footnote>
news covering these topics combined is significantly lower when there are no internet outages in the conflict zone.<fnref n="23" />

Overall, the topics results are consistent with the hypothesis that U.S. TV news gives voice to citizen journalists on the ground when they are able to source information from them, and discusses U.S. foreign policy and internal politics in the conflict zone less.

### 4.2.3 References to sources

We also examine how digital media presence in the conflict zone influences whether U.S. TV news programs explicitly reference digital media sources. As dependent variables, we use binary indicators for mentions of Twitter, Facebook, Instagram, and YouTube. The results are presented in Columns 1 to 4 of Table 6. (The raw-data correlation is presented in Panel E of Figure 3.) We find a significant positive effect of the absence of internet outages in the conflict zone on references to Twitter, Facebook, and Instagram in U.S. TV conflict news, and no significant effect on references to YouTube. Specifically, the absence of internet outages in the conflict zone leads to a 3.8 percentage-point increase in references to Twitter, a 4.2 percentage-point increase in references to Facebook, and a 0.9 percentage-point increase in references to Instagram.<fnref n="24" /> In contrast, internet outages in the conflict zone do not affect references to news agencies as shown in Column 5 of Table 6.

### 4.2.4 Informativeness

In the last two columns of Panel B of Table 5, we examine whether U.S. TV news stories about the conflict contain more details about events on the ground when digital media is not muted by internet outages in the conflict zone. We identify all mentions of specific small geographic locations in the Palestinian Territories and Israel, and use a dummy for such mentions as the dependent variables. Availability of digital media platforms in the conflict zone significantly increases mentions of small Palestinian geographic locations (by 3.3 percentage points or 24% from the mean), while the effect for small Israeli locations is also positive but statistically insignificant. This asymmetry may be explained by the fact that Israeli authorities provide foreign correspondents, typically stationed in Jerusalem, with detailed information about attacks against Israel, but not about their own operations against Hamas (e.g., Durante and Zhuravskaya, 2018). (Note that, on average, small Israeli geographic locations are mentioned more frequently than Palestinian ones in U.S. TV news about the conflict: in 16.4% vs. 13.5% of broadcasts.) We interpret these results as evidence that digital media in the conflict zone makes U.S. TV news more informative, particularly about the side of the conflict for which it is harder to obtain information through other channels.

<footnote n="23">²³A CorEx topic on identity, culture, and antisemitism is marginally significant in several, but not all, specifications. If anything, it is positively related to the digital media activity in the conflict zone.</footnote>

<footnote n="24">²⁴These magnitudes and the non-significant results for YouTube should be interpreted with caution, as references to video sources on TV news may not always appear in the transcript, which we analyze, but could instead be shown on-screen during the video segment (see, for instance, Figure A1 in the Appendix).</footnote>
### 4.2.5 Emotions

Stories about civilians caught in conflict are typically more emotional than news focused on facts and statistics. Table 7 shows that the availability of digital media in the conflict zone makes U.S. TV coverage more emotional, driven primarily by increased use of negative and neutral, but not positive, emotions. The results are robust to both naive measures of emotions, defined as the sum of emotional word scores divided by the total number of words in the transcript, and a more nuanced contextual sentiment measure. Table 7 reports the results for the naive measure based on the continuous lexicon and the contextual sentiment measure, while Appendix Table A17 reports results based on the binary lexicon and presents the breakdown of results by individual emotions. On days without internet outages in the conflict zone, U.S. TV news about the conflict contains 12 to 13% of a standard deviation more negative emotion (depending on the measure) than on days with internet outages. To interpret the magnitude of this effect, we compare its size to the emotional intensity of different words in the continuous emotion lexicon. The estimated effects imply that the average difference in emotional intensity between U.S. TV news stories shown during times of internet outages and those shown when there are no outages is comparable to the difference between “disagreement” and “catastrophe,” or between “misbehavior” and “atrocity.” The raw-data correlation is illustrated in Panel G of Figure 3.

### 4.2.6 Similarity across media outlets and the absence of heterogeneity in the effect

Next, we examine the similarity of conflict-related news stories broadcast by different U.S. TV networks on the same day. As discussed in the data section, similarity is measured using cosine similarity between vector embeddings of the transcripts. The first three columns of Table 8 present the results. In each specification, we consider pairs of outlets as the unit of observation and restrict the sample to days when at least two of the considered media outlets aired a story about the conflict. We analyze similarity across all U.S. TV networks, as well as separately for cable networks (CNN, MSNBC, and FOX) and non-cable networks (all other US networks). In all cases, we find that the availability of digital media in the conflict zone makes the coverage across different U.S. outlets significantly more similar. It is particularly noteworthy that, for cable networks, the uninstrumented correlation between the number of tweets from the conflict zone and similarity in conflict-related TV coverage is negative, likely reflecting the divergent editorial stances of these networks (as shown by differences in slant reported in Panel B of Appendix Figure A4). However, when we instrument tweets from the conflict zone using internet outages, the sign of the effect reverses, indicating that access to digital media content from witnesses on the ground leads to more similar reporting across networks, even when they have different slants a priori. Appendix Table A18 shows that we find no heterogeneity in the effect of internet outages in the conflict zone on similarity of conflict stories across U.S. TV networks with respect to whether the networks are similar or different in terms of ideology and their a priori slant.<fnref n="25" />

<footnote n="25">²⁵We measure ideological leaning using the ranking of the ideology of viewers of different networks provided by PEW Research Center (visualized in Appendix Figure A11) as well as our own average slant measure. In one specification, with weak first stage, we report weak instrument robust 90% confidence set.</footnote>
In sharp contrast to the effects on similarity among TV conflict news produced by traditional media outlets, Columns 4 to 7 of Table 8 show that digital media activity in the conflict zone does not affect the similarity between U.S. TV news and the AP and Reuters newswires, nor the similarity between the two newswires themselves. This finding supports the idea that news agencies and TV networks pursue different objectives: the former prioritize reporting facts and statistics, while the latter focus on telling stories of civilians, when such stories are available.

Column 8 shows that, when digital media in the conflict zone is not muted, U.S. TV news becomes more similar to Al Jazeera America, a network with a much stronger pro-Palestinian slant than any U.S. network. Since Al Jazeera aired news only during a subset of our observation period, the first-stage regression is weak, with an F-statistic of just 5.46. To address this issue, in brackets, we report the 90% Anderson-Rubin confidence set (Anderson and Rubin, 1949b; Mikusheva and Poi, 2006). Both the reduced-form and weak-instrument-robust IV estimates indicate a (marginally) significant positive effect.

Finally, we show robustness of the main effects separately for the liberal cable networks, CNN and MSNBC, and the conservative cable network, FOX. Since they broadcast news 24×7, we have sufficient power in the first stage to estimate effects for these networks separately (all F-statistics exceed 10, including in one specification with multiple instruments). We report the effects on three main outcomes: news length, mentions of civilians (conditional on coverage), and slant. The results are presented in Figure 5 as coefficient plots. The figure shows that the point estimates are very similar in magnitude for both liberal and conservative networks (although the precision is lower than in the full sample). Appendix Figure A12 illustrates the raw-data correlations, revealing similar effects of internet outages in the conflict zone on these outcomes despite substantial differences in the level of slant across the networks, as also visible in the figure.

Overall, we have established that digital media availability in the conflict zone substantially affects U.S. TV conflict reporting. In the next section, we test whether conflict news generates greater interest among U.S. consumers when digital media in the conflict zone is not suppressed by internet outages.

## 4.3 Interest of news consumers in U.S. TV conflict coverage

Using the population of all conflict-related Facebook posts made by the official accounts of U.S. TV networks, we first verify that such posts are more frequent when there is digital media activity in the conflict zone. This serves as a reality check, because media outlets typically post links to their own news stories. Given that internet outages in the conflict zone affect both the extensive and intensive margins of conflict coverage, it is reasonable to expect that frequency of social-media posts by news outlets about these new stories are also affected by internet outages in the conflict zone. The first two columns of Table 9 present regressions using a dummy for at least one conflict-related Facebook post and the number of such posts, as the dependent variables, both measured at the TV network-day level. We find that, on days without internet outages in the conflict zone, the probability that an average U.S. TV network posts about the
conflict on Facebook increases significantly by 1.4 percentage points (from a mean of 10%), and the number of posts rises by 33% (from a mean of 0.17 posts per day).

To proxy for consumer interest in these conflict news stories, we measure engagement as the total number of likes, shares, and comments on these posts. Focusing on the sample of network-day observations with at least one conflict-related Facebook post by a TV network, we find that user engagement is significantly higher when digital activity in the conflict zone is not muted. Column 3 of Table 9 shows that engagement with conflict-related posts increases by 24.2% on days without internet outages compared to days with internet outages. (The average conflict-related Facebook post by a U.S. TV network receives 1,156 likes, shares, and comments. Since no post had zero engagement, we estimate a log-linear model.)

Additionally, we define indicators for whether the number of likes, shares, and comments on a post falls within the top quintile of their respective distributions, and use these as alternative dependent variables. As shown in Columns 4 to 6, conflict-related posts by U.S. TV networks are significantly more likely to rank in the top 20% of each engagement metric on days without internet outages: by 4 percentage points for likes, 4.7 percentage points for shares, and 7.9 percentage points for comments.

In sum, we conclude that conflict coverage by U.S. TV networks generates more interest among viewers when it focuses on civilians caught in the conflict—the topic that is substantially more likely to be chosen for the conflict news stories when digital media in the conflict zone is not muted by internet outages.

## 4.4 Robustness to allowing for autocorrelation

In the analyses above, standard errors are clustered within the same day across different outlets. In Appendix Table A19, we show that the results are largely robust to also allowing for autocorrelation in the error term over time using (Colella et al., 2023). Specifically, we present reduced-form estimates and the power of first-stage for the main outcomes in all three parts of our analysis, allowing for correlation within ±1, 3, and 7 days of each observation.

# 5 A case study: 16-year-old Palestinian citizen journalist

In the summer of 2014, Farah Baker, a 16-year-old Palestinian girl, became an international celebrity (Reading, 2016; Patrikarakos, 2017). Media outlets around the world, including CNN, Fox News, NBC, Al Jazeera, RT, BBC, *The Daily Telegraph*, *The New York Post*, *International Business Times*, and *Le Point*, ran stories based on the content of her English-language Twitter account, where she chronicled what she saw and felt during Israeli bombing raids on her town during the Gaza War.

Patrikarakos (2017) writes: "*The majority of articles [by traditional media outlets] were based on her tweets and the narrative around them. In effect, they treated her Twitter feed like a newswire service; a tweet became comparable to an Associated Press bulletin*" (p. 34). "*Many of her tweets were simple descriptions or videos of what she saw and heard. 'This is the car which*
was bombed at my house door #Gaza #GazaUnderAttack,' she tweeted on July 26, with an accompanying photo of the destroyed vehicle. [...] But it was the detailing of her emotions—her fear for her safety and for that of her family, especially her little sister, Lamar—that was by far the most powerful and popular element of her output" (p. 27). We provide examples of her tweets as quoted by CNN in Appendix Figure A2.<fnref n="26" />

As a result, many of Farah’s tweets were retweeted thousands of times, including by journalists and opinion makers with large followings, amplifying her message. “Tweets begat retweets, which begat greater audiences, which begat news coverage, which begat demonstrations, which begat yet more news coverage, most of it pro-Gaza” (Patrikarakos, p. 35). In an interview, Farah told Patrikarakos: “[With Twitter,] more people [...] can see what you write, and crucially, journalists use it as a source. People on the ground tweeting photos and descriptions of events during wartime have become invaluable—especially as they often tweet or post from areas too dangerous for journalists to go. [...] It allows the victims of war to gain a voice and the world to view—with greater detail than ever before—just what exactly is happening inside zones of conflict” (p. 25).

Farah’s logic underpins the “B’Tselem Camera Project.” Since 2007, the NGO has distributed video cameras to Palestinians in the West Bank and trained them to become citizen journalists and document human rights abuses.<fnref n="27" /> Some of the videos filmed by project participants have gained considerable attention on social media as well as in domestic and international news coverage.

Farah Baker’s story is not unique. In 2016, 7-year-old Bana Alabed, who tweeted about her life in besieged Aleppo with the help of her mother, also gained international attention after numerous media outlets, including *Mail Online*, *The Telegraph*, and *Quartz*, reported on her story. “Through her broken English and simple messages alternating between fear and hope, she has become a representation of the suffering that children face every day within Syria.”<fnref n="28" />

# 6 Conclusions

User-generated content on the internet has transformed how traditional media report on conflicts. This paper analyzes the causal impact of online posts originating from Israel and the Palestinian Territories on U.S. TV news coverage of the Israeli–Palestinian conflict. We exploit exogenous variation in internet access caused by outages in the conflict zone, which significantly reduce online activity on the conflict zone.

We find that comparable conflict events receive significantly more coverage on U.S. TV when the internet in the conflict zone is not disrupted. Using text analysis of TV news transcripts, we document that the content of U.S. conflict coverage is shaped by conflict zone digital media activity. On average, the slant of coverage shifts toward the side experiencing casualties

<footnote n="26">²⁶Note that not only Farah’s pictures and videos, but also her tweets in the form of text were used as sources by U.S. TV news. Since international print media also ran stories based on Farah’s tweets, this suggests our results are not specific to TV news, which—unlike print—requires visuals.</footnote>

<footnote n="27">²⁷See, for instance, https://www.btselem.org/video-channel/camera-project, accessed January 3, 2025.</footnote>

<footnote n="28">²⁸See https://www.europeanpressprize.com/article/finding-bana-proving-existence-7-year-old-girl-eastern-aleppo/, accessed July 14, 2025.</footnote>
when digital media are accessible. Coverage focuses more on civilian suffering and less on U.S. foreign policy or electoral considerations. News stories include more detail and cite social media more frequently. The availability of local conflict-zone online content makes international media coverage of conflict more emotional and more similar across outlets with different political orientations. Ultimately, such coverage generates greater interest among viewers, who engage more deeply and learn about the conflict in new ways.

Our results suggest that user-generated content from conflict zones shifts traditional media narratives away from the perspective of official gatekeepers and toward the voices of ordinary people affected by violence on all sides. This underscores the democratizing role of digital media: it amplifies the perspectives of those who suffer most, particularly civilians, and helps elevate their narratives on the international stage.

To the extent that war actors are constrained by public narratives (Durante and Zhuravskaya, 2018), the digital age introduces new accountability mechanisms in warfare. If extended to other contexts, our findings suggest counterfactual predictions: broader visibility of civilian suffering could have altered key strategic decisions in past wars. For instance, Red Army soldiers might have committed fewer atrocities on their march to Berlin had Polish and German women been able to live-stream their experiences. Similarly, the Allies might have shifted their bombing priorities away from Dresden's densely populated city center toward industrial areas on its outskirts. These hypothetical examples underscore the importance of the phenomenon we study: digital media reshapes constraints under which war actors operate.

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<caption>**Figure 1:** Illustration of the first stage: conflict tweets and internet outage</caption>

<figure><img src="image_5.png" /><figcaption>Panel B: Bin scatter plot of lightning strikes and log(conflict tweets)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Panel C: Kernel density of log(conflict tweets), over days with and without absence of traffic</figcaption></figure>

<figure><img src="image_7.png" /></figure>

**Note:** In Panels A and B, internet outage, lightning strike and the log(conflict tweets) are residualized using standard set of controls: year, month-of-the-year, and day-of-the-week fixed effects, as well as dummies for Palestinian fatality incidence, Israeli fatality incidence, and the Gaza war, respectively, inverse hyperbolic sine transformation of the total number of fatalities on each respective side of the conflict during 28 days prior to day t, U.S. domestic news pressure, dummy for other conflicts involving Israel, dummy for day with rain, as well as rain and wind intensity, and dummy for second wave of Twitter data collection. The shaded area represents 95% confidence interval. Panel C shows the kernel distribution of log(conflict tweets) on days with and without absence of traffic, without controls.
<figure><img src="image_8.png" /><figcaption>Figure 2: Exclusion restriction I: Balance: Conflict events</figcaption></figure>

**Note:** The figure presents the results of a set of balance exercises, where we regress the instruments on standardized variables measuring fatalities, injuries, demonstrations, house demolitions, and electricity supply in the conflict zone. The unit of observation is a day, with robust standard errors reported in parentheses. All panels have the same dependent variables: internet outage (a binary variable indicating either the presence of a lightning strike or the absence of traffic), lightning strikes (a binary variable indicating the presence of strikes hitting a cellular antenna, based on data from WWLLN), or the absence of traffic (a binary variable indicating a drop in traffic caused by technical failure, based on data from IODA). “Year-, MoY-, DoW- FEs,” denote fixed effects for each calendar year, each month of the year, and each day of the week. All specifications have the following controls: dummy for day with rain, as well as rain and wind intensity. All explanatory variables are standardized to have a mean of zero and a standard deviation of one. We report the estimates for the specifications with the following sets of standardized explanatory variables: (i) Inverse Hyperbolic Sine (IHS) of the total number of fatalities; (ii) IHS of the total number of Palestinian (PS) and Israeli (IL) fatalities; (iii) IHS of the number of PS male, female, and child fatalities; (iv) Incidence of PS fatalities, IL fatalities, and the Gaza war; (v) IHS of the total number of injuries; (vi) IHS of the total number of PS and IL injuries; (vii) IHS of the number of PS adult and child injuries, and IL adult and child injuries; (viii) IHS of the total number of demonstrations (i.e., protests and riots); (ix) IHS of the number of violent demonstrations (i.e., riots); (x) Incidence of a violent demonstration; (xi) IHS of the number of house demolitions in the West Bank; (xii) Incidence of house demolitions in the West Bank. (xiii) Supply of electricity (in kW) in Gaza; (xiv) Shortage of electricity (in kW) in Gaza; (xv) Number of hours with electricity in Gaza; Data on fatalities comes from B'tselem. Data on injuries comes from UNOCHA. Both types of data covers the full period of our study: 11-2009 to 12-2019. Data on demonstrations comes from ACLED and covers the period 01-2016 to 12-2019. Data on electricity in Gaza comes from UNOCHA and covers the period 01-2017 to 12-2019. Data on house demolitions in West Bank comes from B'tselem and covers the full period of our study: 11-2009 to 12-2019.
<figure><img src="image_9.png" /><figcaption>**Figure 3:** Raw-data correlations, illustrating the reduced form</figcaption></figure>

**Note:** The figure illustrates the reduced-form relationship between internet outages in the conflict zone and three types of outcomes: the log of conflict-related tweets from the conflict zone (Panel A), the extent of conflict coverage by U.S. TV networks (Panel B), the characteristics of content of this coverage (Panels C to G), and online engagement with conflict-related stories posted by U.S. TV networks on Facebook (Panel H). The unit of observation is day × TV network. Panels A and B use the full sample of day × TV network observations, while Panels C through G use the subsample with conflict coverage. Panel H is based on the subsample of day × TV network observations with at least one Facebook post by the network. “Length in minutes” measures the total number of minutes devoted to the conflict by a given network on a given day. “Civilian casualties and youth” and “U.S. foreign policy officials” represent the number of keywords on these topics, divided by the total number of words and multiplied by 1,000. “Twitter, Facebook, or Instagram” is a dummy equal to 1 if any of the three platforms is mentioned. “Informativeness, PS places” is a binary variable equal to 1 if small Palestinian geographic locations are mentioned. “Negative contextual sentiment” is based on Hutto and Gilbert (2014). “Online engagement, likes in top 20%” is a dummy indicating the number of likes received by conflict-related post by U.S. TV on Facebook in the top quintile. Error bars show 90% Confidence Intervals (CIs) for the difference in the mean levels of outcome on days with and without internet outage in the conflict zone.
<figure><img src="image_10.png" /><figcaption>**Figure 4:** Internet outages and slant of conflict stories, reduced form</figcaption></figure>

**Note:** The figure illustrates the reduced-form relationship between internet outages in the conflict zone and the slant of U.S. TV news. The unit of observation is day × TV network. Each panel summarizes the slant of conflict-related broadcasts in U.S. TV, across days and TV networks. Pro-Palestinian slant dummy indicates that the continuous slant score is above 0.1. The pro-Israeli slant dummy indicates that the continuous slant score is below -0.1. The summaries are presented separately for days with fatalities on one side (Panel A) and for days with fatalities on both sides (Panel B). The sample is split into days with and without internet outages in the conflict zone. Error bars show 90% CIs for the difference in the mean levels of outcome on days with and without internet outage in the conflict zone. More details are provided in Appendix Figure A10.
<figure><img src="image_11.png" /><figcaption>**Figure 5:** Heterogeneity across TV networks</figcaption></figure>

**Note:** The figure presents the reduced-form coefficients separately for two groups of cable networks: CNN and MSNBC (liberal) and FOX (conservative). We also present the corresponding 90% (darker) and 95% (lighter) confidence intervals. The unit of observation is day × TV network. We examine three outcomes: length of conflict coverage on a given day (Panel A), the mentions of keywords associated with civilians normalized by the total number of words in conflict coverage (Panel B), and the continuous slant score (Panel C). Panel A has the sample of network-days. Panels B and C only observations with conflict coverage. All specifications include fixed effects for each calendar year, each month of the year, and each day of the week. Additionally, the “CNN and MSNBC” specification includes fixed effects for each TV network. We control for the following variables: dummies for Palestinian fatality incidence, Israeli fatality incidence, and the Gaza war; the inverse hyperbolic sine transformation of the total number of casualties on each side of the conflict during the 28 days prior to day *t*; U.S. domestic news pressure; a dummy for other conflicts involving Israel; a dummy for days with rain; rain and wind intensity; and a dummy for the second wave of Twitter data collection. The corresponding first stages are strong. In Panel A, F-stats are 56.1 for CNN and MSNBC and 27.9 for FOX. In Panel B, F-stats are: 19.8 for CNN and MSNBC and 15.5 for FOX. In Panel C, F-stats are: 24.69 (direct effect) and 24.89 (interaction) for CNN and MSNBC and 12.27 (direct effect) and 12.38 (interaction) for FOX.
<table><caption>Table 1: The first stage: Internet outages and tweets in the conflict zone</caption><thead><tr><th>Dependent variable (both panels):</th><th>Log(All tweets)<br>in conflict zone</th><th>Log(Conflict tweets)<br>in conflict zone</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A: Full sample: All days</strong></td></tr><tr><td>Internet outage, conflict zone</td><td>-0.149***<br>(0.027)</td><td></td><td></td><td>-0.127***<br>(0.024)</td><td></td><td></td></tr><tr><td>Lightning strike, conflict zone</td><td></td><td>-0.063***<br>(0.020)</td><td></td><td></td><td>-0.067***<br>(0.024)</td><td></td></tr><tr><td>Absence of traffic, conflict zone</td><td></td><td></td><td>-0.265***<br>(0.061)</td><td></td><td></td><td>-0.200***<br>(0.046)</td></tr><tr><td>Mean dep. var.</td><td>7.20</td><td>7.20</td><td>7.38</td><td>6.56</td><td>6.56</td><td>6.76</td></tr><tr><td>Observations</td><td>27596</td><td>27596</td><td>19763</td><td>27596</td><td>27596</td><td>19763</td></tr><tr><td>F-stat, instrument</td><td>31.41</td><td>9.48</td><td>19.11</td><td>27.99</td><td>7.60</td><td>18.79</td></tr><tr><td>Network-, Year-, MoY-, DoW FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td colspan="7"><strong>Panel B: Sample restriction: Days when U.S. TV covered the conflict</strong></td></tr><tr><td>Internet outage, conflict zone</td><td>-0.208***<br>(0.037)</td><td></td><td></td><td>-0.191***<br>(0.038)</td><td></td><td></td></tr><tr><td>Lightning strike, conflict zone</td><td></td><td>-0.129***<br>(0.033)</td><td></td><td></td><td>-0.123***<br>(0.043)</td><td></td></tr><tr><td>Absence of traffic, conflict zone</td><td></td><td></td><td>-0.324***<br>(0.085)</td><td></td><td></td><td>-0.307***<br>(0.069)</td></tr><tr><td>Mean dep. var.</td><td>7.33</td><td>7.33</td><td>7.57</td><td>6.76</td><td>6.76</td><td>7.03</td></tr><tr><td>Observations</td><td>5854</td><td>5854</td><td>3917</td><td>5854</td><td>5854</td><td>3917</td></tr><tr><td>F-stat, instrument</td><td>31.78</td><td>15.18</td><td>14.35</td><td>25.47</td><td>8.29</td><td>19.90</td></tr><tr><td>Network-, Year-, MoY-, DoW FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

**Note:** The table presents the first-stage estimations for two dependent variables—the log of all tweets in the conflict zone and the log of all conflict-related tweets in the conflict zone—using three instruments: our main instrument (internet outage, a binary variable indicating either the presence of a lightning strike or the absence of traffic) and its two components (lightning strikes, a binary variable indicating the presence of strikes hitting a cellular antenna; and the absence of traffic, a binary variable indicating a drop in traffic caused by technical failure). The unit of observation is day × network. Panel A uses the full sample of day × TV network observations, while Panel B focuses on the subsample of day × TV network observations with conflict coverage. “Network-, Year-, MoY-, DoW- FEs” denotes fixed effects for each TV network, each calendar year, each month of the year, and each day of the week. All specifications include the following controls: dummies for Palestinian fatality incidence, Israeli fatality incidence, and the Gaza war; the inverse hyperbolic sine transformation of the total number of casualties on each side of the conflict during the 28 days prior to day t; U.S. domestic news pressure; a dummy for other conflicts involving Israel; a dummy for days with rain; rain and wind intensity; and a dummy for the second wave of Twitter data collection (collectively referred to as “Controls”). Standard errors are clustered at the date level and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>**Table 2:** Exclusion restriction: Internet outages in the conflict zone and international newswires</caption><thead><tr><th rowspan="2">Dependent variable:</th><th colspan="4">News agencies: AP and Reuters</th><th colspan="2">Tweets in conflict zone</th></tr><tr><th>Number of reports (1)</th><th>At least one report (2)</th><th>Log (Number of reports) (3)</th><th>Number of stems (4)</th><th>Log (Number of stems) (5)</th><th>Log (All tweets) (6)</th><th>Log (Conflict tweets) (7)</th></tr></thead><tbody><tr><td>Subsample: wires coverage=1</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td></tr><tr><th>Estimation model:</th><th>PPML</th><th>OLS</th><th>OLS</th><th>PPML</th><th>OLS</th><th>OLS</th><th>OLS</th></tr><tr><td>Internet outage, conflict zone</td><td>-0.066<br>(0.063)</td><td>0.004<br>(0.018)</td><td>0.024<br>(0.052)</td><td>0.062<br>(0.068)</td><td>0.071<br>(0.076)</td><td>-0.162***<br>(0.029)</td><td>-0.136***<br>(0.026)</td></tr><tr><td>Mean dep. var.</td><td>7.09</td><td>0.91</td><td>1.61</td><td>7746.05</td><td>8.38</td><td>7.35</td><td>6.72</td></tr><tr><td>Observations</td><td>2872</td><td>2872</td><td>2621</td><td>2872</td><td>2621</td><td>2872</td><td>2872</td></tr><tr><td>Year-, MoY-, DoW- FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

Note: The table presents the estimation of the effect of our main instrument (internet outage, a binary variable indicating either the presence of a lightning strike or the absence of traffic) on the reporting activity of two major news agencies–American Press (AP) and Reuters–in columns 1 to 5. It also presents the effect on the log of all tweets in the conflict zone and the log of all conflict-related tweets in the conflict zone, using the same subsample of days for which we have data for the news agencies (09/2012–12/2019). The unit of observation is a day. “Year-, MoY-, DoW- FEs” denotes fixed effects for each calendar year, each month of the year, and each day of the week. All specifications include the following controls: dummies for Palestinian fatality incidence, Israeli fatality incidence, and the Gaza war; the inverse hyperbolic sine transformation of the total number of casualties on each side of the conflict during the 28 days prior to day t; U.S. domestic news pressure; a dummy for other conflicts involving Israel; a dummy for days with rain; and rain and wind intensity (collectively referred to as “Controls”). Tweet-related estimations also include a dummy for the second wave of Twitter data collection (columns 6 and 7). Robust standard errors are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>**Table 3:** Conflict-zone internet outages and the extent of conflict coverage on U.S. TV</caption><thead><tr><th>Dependent variable:</th><th>Number of keywords on U.S. TV</th><th>Number of stories on U.S. TV</th><th>Prime time coverage on U.S. TV</th><th>Length in minutes on U.S. TV</th><th>Log(Length in minutes) on U.S. TV</th></tr><tr><th>Subsample: TV coverage=1</th><th colspan="5" style="text-align: center;">✓</th></tr><tr><th>Estimation model:</th><th>PPML</th><th>PPML</th><th>Linear</th><th>PPML</th><th>Linear</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td colspan="6" style="text-align: center; font-weight: bold;">Reduced Form:</td></tr><tr><td>No internet outage, conflict zone</td><td>0.695***<br>(0.080)</td><td>0.457***<br>(0.061)</td><td>0.027***<br>(0.008)</td><td>0.612***<br>(0.073)</td><td>0.234***<br>(0.065)</td></tr><tr><td colspan="6" style="text-align: center; font-weight: bold;">IV:</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>3.122***<br>(0.675)</td><td>2.216***<br>(0.560)</td><td>0.209***<br>(0.062)</td><td>2.847***<br>(0.626)</td><td>1.225***<br>(0.320)</td></tr><tr><td colspan="6" style="text-align: center; font-weight: bold;">OLS:</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>1.822***<br>(0.056)</td><td>1.557***<br>(0.040)</td><td>0.168***<br>(0.011)</td><td>1.688***<br>(0.049)</td><td>0.738***<br>(0.052)</td></tr><tr><td>Mean dep. var.</td><td>19.998</td><td>0.817</td><td>0.086</td><td>11.075</td><td>3.233</td></tr><tr><td>Observations</td><td>27596</td><td>27596</td><td>27596</td><td>27596</td><td>5854</td></tr><tr><td>F-stat, excluded instrument</td><td>27.99</td><td>27.99</td><td>27.99</td><td>27.99</td><td>25.47</td></tr><tr><td>Network-, Year-, MoY-, DoW FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

**Note:** The table presents the estimation of the effect of the absence of internet outage (the reduced form), as well as the log of all conflict-related tweets in the conflict zone (estimated using 2SLS and OLS), on the slant of conflict coverage by U.S. TV. The unit of observation is day × TV network. The dependent variables are: the number of times each network mentioned keywords from the conflict zone (Col. 1); the number of news stories about the conflict (column 2); a dummy indicating whether a network aired a story about the conflict during prime-time news (Col. 3); the total length (in minutes) of conflict-related news (Col. 4); and the logarithm of the total length (in minutes) of conflict-related news (Col. 5). Columns 1 to 4 use the full sample of day × TV network observations, while Column 5 focuses on the subsample of day × TV network observations with conflict coverage. “Network-, Year-, MoY-, DoW- FEs” denotes fixed effects for each TV network, calendar year, month of the year, and day of the week. All specifications include the following controls: dummies for Palestinian fatality incidence, Israeli fatality incidence, and the Gaza war; the IHS of the total number of casualties on each side of the conflict during the 28 days prior to day t; U.S. domestic news pressure; a dummy for other conflicts involving Israel; a dummy for days with rain; rain and wind intensity; and a dummy for the second wave of Twitter data collection (collectively referred to as “Controls”). Standard errors are clustered at the date level and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table 4: Conflict-zone internet outages and the slant of conflict coverage on U.S. TV</caption><thead><tr><th>Dependent variable:</th><th colspan="2">Slant score: from -2 to +2<br>(very pro-IL to very pro-PS)<br>on U.S. TV</th><th colspan="2">Pro-Palestinian slant<br>dummy<br>on U.S. TV</th><th colspan="2">Pro-Israeli slant<br>dummy<br>on U.S. TV</th></tr><tr><th>Subsample: TV coverage=1</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7" style="text-align: center; font-weight: bold;">Reduced Form:</td></tr><tr><td>No internet outage, conflict zone</td><td>-0.008<br>(0.010)</td><td>-0.067<br>(0.045)</td><td>0.002<br>(0.010)</td><td>-0.042<br>(0.026)</td><td>0.011*<br>(0.007)</td><td>0.033<br>(0.025)</td></tr><tr><td>... × PS fatality incidence</td><td>0.053**<br>(0.023)</td><td>0.158*<br>(0.084)</td><td>0.056***<br>(0.021)</td><td>0.100**<br>(0.049)</td><td>-0.011<br>(0.016)</td><td>-0.099**<br>(0.049)</td></tr><tr><td>... × IL fatality incidence</td><td>-0.084<br>(0.052)</td><td>-0.346**<br>(0.143)</td><td>-0.005<br>(0.056)</td><td>-0.188*<br>(0.102)</td><td>0.106***<br>(0.033)</td><td>0.273***<br>(0.078)</td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">IV:</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>-0.127<br>(0.128)</td><td>-0.720<br>(0.540)<br>[-2.142,-0.009]*</td><td>-0.020<br>(0.115)</td><td>-0.457<br>(0.321)<br>[-1.300,-0.035]*</td><td>0.133<br>(0.084)</td><td>0.368<br>(0.297)<br>[-0.023,1.150]</td></tr><tr><td>... × PS fatality incidence</td><td>0.384*<br>(0.207)</td><td>0.921*<br>(0.554)<br>[0.192,2.380]*</td><td>0.307*<br>(0.179)</td><td>0.582*<br>(0.330)<br>[0.149,1.450]*</td><td>-0.172<br>(0.133)</td><td>-0.520*<br>(0.312)<br>[-1.341,-0.110]*</td></tr><tr><td>... × IL fatality incidence</td><td>-0.277<br>(0.169)</td><td>-0.555**<br>(0.245)<br>[-1.199,-0.233]*</td><td>-0.165<br>(0.152)</td><td>-0.326**<br>(0.154)<br>[-0.730,-0.124]*</td><td>0.191**<br>(0.094)</td><td>0.384**<br>(0.152)<br>[0.184,0.783]*</td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">OLS:</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>0.099***<br>(0.012)</td><td>0.138***<br>(0.032)</td><td>0.150***<br>(0.013)</td><td>0.071***<br>(0.020)</td><td>0.038***<br>(0.006)</td><td>-0.084***<br>(0.018)</td></tr><tr><td>... × PS fatality incidence</td><td>0.027*<br>(0.015)</td><td>-0.050<br>(0.033)</td><td>0.064***<br>(0.016)</td><td>0.011<br>(0.022)</td><td>0.005<br>(0.008)</td><td>0.004<br>(0.019)</td></tr><tr><td>... × IL fatality incidence</td><td>-0.040*<br>(0.024)</td><td>-0.044<br>(0.046)</td><td>-0.018<br>(0.024)</td><td>-0.045<br>(0.032)</td><td>0.031**<br>(0.015)</td><td>0.024<br>(0.026)</td></tr><tr><td>Mean dep. var.</td><td>0.051</td><td>0.242</td><td>0.127</td><td>0.599</td><td>0.069</td><td>0.324</td></tr><tr><td>Observations</td><td>27596</td><td>5854</td><td>27596</td><td>5854</td><td>27596</td><td>5854</td></tr><tr><td>F-stat, excluded instrument</td><td>12.91</td><td>7.17</td><td>12.91</td><td>7.17</td><td>12.91</td><td>7.17</td></tr><tr><td>F-stat, excl. instrument × PS fatality</td><td>12.69</td><td>7.85</td><td>12.69</td><td>7.85</td><td>12.69</td><td>7.85</td></tr><tr><td>F-stat, excl. instrument × IL fatality</td><td>10.85</td><td>39.98</td><td>10.85</td><td>39.98</td><td>10.85</td><td>39.98</td></tr><tr><td>Network-, Year-, MoY-, DoW FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

Note: The table presents the estimation of the effect of the absence of internet outage (the reduced form), as well as the log of all conflict-related tweets in the conflict zone (estimated using 2SLS and OLS), on the slant of conflict coverage by U.S. TV. The unit of observation is day × TV network. The estimation model is linear. The dependent variables are the slant score, ranging from -2 (very pro-IL) to 2 (very pro-PS) in columns 1-2; the Palestinian slant dummy, which indicates that the slant score is above 0.1 (in columns 3-4); and the Israeli slant dummy, which indicates that the slant score is below -0.1 (in columns 5-6) of conflict-related broadcasts, across days and TV networks. The explanatory variables alternatively include the dummy indicating no internet outage or the log of all conflict-related tweets in the conflict zone. We also include two interaction terms: one with a dummy indicating Palestinian fatalities, and the other with Israeli fatalities. Columns 1, 3 and 5 use the full sample of day × TV network observations, while columns 2, 4 and 6 focus on the subsample of day × TV network observations with conflict coverage. “Network-, Year-, MoY-, DoW- FEs” denotes fixed effects for each TV network, each calendar year, each month of the year, and each day of the week. All specifications include the following controls: dummies for Palestinian fatality incidence, Israeli fatality incidence, and the Gaza war; the inverse hyperbolic sine transformation of the total number of casualties on each side of the conflict during the 28 days prior to day t; U.S. domestic news pressure; a dummy for other conflicts involving Israel; a dummy for days with rain; rain and wind intensity; and a dummy for the second wave of Twitter data collection (collectively referred to as “Controls”). Standard errors are clustered at the date level and reported in parentheses. Weak instrument robust confidence set (90%) from the linear combination test (Sun, 2018) presented in brackets (in columns 2, 4 and 6 of the IV Panel). *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>**Table 5:** Conflict-zone internet outages and topics (measured by keywords) of the conflict news on U.S. TV</caption><thead><tr><th colspan="6">Panel A: Mentions of civilians, civilians casualties, and reporters</th></tr><tr><th colspan="6">Dependent variable, this panel:</th></tr><tr><td colspan="6">The number of keywords on the following topic divided by the total number of words (×1000), on U.S. TV:</td></tr><tr><th></th><th>Civilians</th><th>Youth & babies</th><th>Civilian casualties</th><th>Pictures & videos</th><th>Reporters</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td colspan="6">Reduced Form:</td></tr><tr><td>No internet outage, conflict zone</td><td>0.267**<br>(0.117)</td><td>0.002***<br>(0.001)</td><td>0.005*<br>(0.002)</td><td>0.015***<br>(0.006)</td><td>-0.010<br>(0.023)</td></tr><tr><td colspan="6">IV:</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>1.398**<br>(0.666)</td><td>0.010***<br>(0.003)</td><td>0.025**<br>(0.012)</td><td>0.080***<br>(0.031)</td><td>-0.052<br>(0.123)</td></tr><tr><td colspan="6">OLS:</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>-0.085<br>(0.064)</td><td>0.004***<br>(0.001)</td><td>0.022***<br>(0.003)</td><td>0.054***<br>(0.007)</td><td>0.132***<br>(0.015)</td></tr><tr><td>Mean dep. var.</td><td>3.164</td><td>0.004</td><td>0.015</td><td>0.033</td><td>0.381</td></tr></tbody></table><table><thead><tr><th colspan="6">Panel B: Mentions of officials and elections, as well as informativeness of news</th></tr><tr><th colspan="6">Dependent variable, this panel:</th></tr><tr><td colspan="6">The number of keywords on the following topic divided by the total number of words (×1000), on U.S. TV:</td></tr><tr><th></th><th>IL & PS<br>officials</th><th>U.S.<br>officials</th><th>Elections</th><th>Palestinian<br>territories</th><th>Israel</th></tr><tr><th></th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th></tr></thead><tbody><tr><td colspan="6">Reduced Form:</td></tr><tr><td>No internet outage, conflict zone</td><td>0.217**<br>(0.086)</td><td>-0.029***<br>(0.011)</td><td>-0.069**<br>(0.028)</td><td>0.033**<br>(0.016)</td><td>0.022<br>(0.020)</td></tr><tr><td colspan="6">IV:</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>1.137**<br>(0.504)</td><td>-0.155**<br>(0.068)</td><td>-0.361**<br>(0.168)</td><td>0.174**<br>(0.085)</td><td>0.117<br>(0.102)</td></tr><tr><td colspan="6">OLS:</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>0.090<br>(0.059)</td><td>0.001<br>(0.005)</td><td>-0.028*<br>(0.016)</td><td>0.103***<br>(0.014)</td><td>0.107***<br>(0.016)</td></tr><tr><td>Mean dep. var.</td><td>1.326</td><td>0.071</td><td>0.200</td><td>0.135</td><td>0.164</td></tr><tr><td>Observations</td><td>5854</td><td>5854</td><td>5854</td><td>5854</td><td>5854</td></tr><tr><td>F-stat, excluded instrument</td><td>25.47</td><td>25.47</td><td>25.47</td><td>25.47</td><td>25.47</td></tr><tr><td>Network-, Year-, MoY-, DoW FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

**Note:** The table presents the estimation of the effect of the absence of internet outage (the reduced form), as well as the log of all conflict-related tweets in the conflict zone (estimated using 2SLS and OLS), on the topics covered by U.S. TV conflict news. The unit of observation is day × TV network. The dependent variables are, respectively, the number of keywords related to the following topics, divided by the total number of words and multiplied by 1,000: “Civilians,” “Youth and babies,” “Civilian casualties,” “Pictures and videos,” and “Reporters” (Panel A, columns 1 to 5); and “IL and PS officials,” “US officials,” and “Elections” (Panel B, columns 6 to 8). In Panel B (columns 9 and 10) the dependent variables are a dummy indicating the presence of mentions of specific small geographical locations in the Palestinian territories and Israel, respectively. “Network-, Year-, MoY-, DoW- FEs” denotes fixed effects for each TV network, each calendar year, each month of the year, and each day of the week. All specifications include the following controls: dummies for Palestinian fatality incidence, Israeli fatality incidence, and the Gaza war; the inverse hyperbolic sine transformation of the total number of casualties on each side of the conflict during the 28 days prior to day t; U.S. domestic news pressure; a dummy for other conflicts involving Israel; a dummy for days with rain; rain and wind intensity; and a dummy for the second wave of Twitter data collection (collectively referred to as “Controls”). Standard errors are clustered at the date level and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>**Table 6:** Conflict-zone internet outages and references to social media and news agencies in the conflict news on U.S. TV</caption><thead><tr><th rowspan="3">Dependent variable:</th><th colspan="5">The dummy indicating a mention of the following, on U.S. TV:</th></tr><tr><th>Twitter</th><th>Facebook</th><th>Instagram</th><th>YouTube</th><th>News agencies</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>No internet outage, conflict zone</td><td>0.038**<br>(0.019)</td><td>0.042**<br>(0.017)</td><td>0.009*<br>(0.005)</td><td>-0.002<br>(0.009)</td><td>0.003<br>(0.005)</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>0.199**<br>(0.099)</td><td>0.221**<br>(0.096)</td><td>0.050*<br>(0.027)</td><td>-0.009<br>(0.048)</td><td>0.016<br>(0.026)</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>0.140***<br>(0.016)</td><td>0.080***<br>(0.014)</td><td>0.021***<br>(0.005)</td><td>0.037***<br>(0.009)</td><td>0.022***<br>(0.006)</td></tr><tr><td>Mean dep. var.</td><td>0.157</td><td>0.149</td><td>0.018</td><td>0.044</td><td>0.018</td></tr><tr><td>Observations</td><td>5854</td><td>5854</td><td>5854</td><td>5854</td><td>5854</td></tr><tr><td>F-stat, excluded instrument</td><td>25.47</td><td>25.47</td><td>25.47</td><td>25.47</td><td>25.47</td></tr><tr><td>Network-, Year-, MoY-, DoW FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

**Note:** The table presents the estimation of the effect of the absence of internet outage (the reduced form), as well as the log of all conflict-related tweets in the conflict zone (estimated using 2SLS and OLS), on the topics covered by U.S. TV conflict news. The unit of observation is day × TV network, and all estimates are based on the subsample of day × TV network observations with conflict coverage. The dependent variables are dummies indicating the presence of, respectively, “Twitter”, “Facebook”, “Instagram”, “Youtube” or one of the four platforms. “Network-, Year-, MoY-, DoW-FEs” denotes fixed effects for each TV network, each calendar year, each month of the year, and each day of the week. All specifications include the following controls: dummies for Palestinian fatality incidence, Israeli fatality incidence, and the Gaza war; the inverse hyperbolic sine transformation of the total number of casualties on each side of the conflict during the 28 days prior to day t; U.S. domestic news pressure; a dummy for other conflicts involving Israel; a dummy for days with rain; rain and wind intensity; and a dummy for the second wave of Twitter data collection (collectively referred to as “Controls”). Standard errors are clustered at the date level and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>**Table 7:** Conflict-zone internet outages and the emotional intensity of conflict coverage on U.S. TV</caption><thead><tr><th rowspan="2">Dependent variable:</th><th>All emotions on U.S. TV</th><th>Negative emotions on U.S. TV</th><th>Negative contextual sentiment on U.S. TV</th><th>Positive emotions on U.S. TV</th><th>Positive contextual sentiment on U.S. TV</th><th>Neutral emotions on U.S. TV</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7" style="text-align: center; font-weight: bold;">Reduced Form:</td></tr><tr><td>No internet outage, conflict zone</td><td>0.361*<br>(0.217)</td><td>0.418**<br>(0.198)</td><td>0.742**<br>(0.322)</td><td>-0.010<br>(0.078)</td><td>0.216<br>(0.316)</td><td>0.077*<br>(0.045)</td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">IV:</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>1.890*<br>(1.085)</td><td>2.193**<br>(0.998)</td><td>3.888**<br>(1.626)</td><td>-0.055<br>(0.407)</td><td>1.134<br>(1.652)</td><td>0.406*<br>(0.221)</td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">OLS:</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>1.576***<br>(0.137)</td><td>1.572***<br>(0.117)</td><td>2.862***<br>(0.189)</td><td>0.224***<br>(0.053)</td><td>0.412**<br>(0.202)</td><td>0.352***<br>(0.032)</td></tr><tr><td>Mean dep. var.</td><td>18.366</td><td>7.818</td><td>15.602</td><td>7.538</td><td>19.787</td><td>3.666</td></tr><tr><td>Observations</td><td>5854</td><td>5854</td><td>5854</td><td>5854</td><td>5854</td><td>5854</td></tr><tr><td>F-stat, excluded instrument</td><td>25.47</td><td>25.47</td><td>25.47</td><td>25.47</td><td>25.47</td><td>25.47</td></tr><tr><td>Network-, Year-, MoY-, DoW FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

**Note:** The table presents the estimation of the effect of the absence of internet outage (the reduced form), as well as the log of all conflict-related tweets in the conflict zone (estimated using 2SLS and OLS), on the emotional intensity of TV conflict news. The unit of observation is a day × TV network, and all estimates are based on the subsample of day × TV network observations with conflict coverage. The dependent variables are scores based, respectively, on the NRC Emotion Lexicon (Mohammad, 2018) for all emotions (column 1); negative emotions—namely anger, fear, disgust, and sadness (column 2); positive emotions—namely joy and trust (column 4); neutral emotions—namely anticipation and surprise (column 6); or the Contextual Sentiment Analysis developed by (Hutto and Gilbert, 2014) for negative and positive contextual sentiment (columns 3 and 5). “Network-, Year-, MoY-, DoW- FEs” denotes fixed effects for each TV network, each calendar year, each month of the year, and each day of the week. All specifications include the following controls: dummies for Palestinian fatality incidence, Israeli fatality incidence, and the Gaza war; the inverse hyperbolic sine transformation of the total number of casualties on each side of the conflict during the 28 days prior to day t; U.S. domestic news pressure; a dummy for other conflicts involving Israel; a dummy for days with rain; rain and wind intensity; and a dummy for the second wave of Twitter data collection (collectively referred to as “Controls”). Standard errors are clustered at the date level and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>Table 8: Conflict-zone internet outages and similarity of conflict news across TV outlets</caption><thead><tr><th rowspan="3">Dependent variable:<br>U.S. outlets considered:</th><th colspan="9">Similarity of conflict-related stories:</th></tr><tr><th colspan="3">among U.S. TV</th><th colspan="3">between U.S. TV<br>and newswires</th><th rowspan="2">between<br>newswires</th><th rowspan="2">between<br>U.S. TV<br>and<br>Al Jazeera</th></tr><tr><th>all</th><th>cable</th><th>non-<br>cable</th><th>all</th><th>cable</th><th>non-<br>cable</th><th>AP-Reuters</th><th>all</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th></th></tr></thead><tbody><tr><td colspan="10" style="text-align: center; font-weight: bold;">Reduced Form:</td></tr><tr><td>No internet outage, conflict zone</td><td>0.020**<br>(0.009)</td><td>0.039***<br>(0.014)</td><td>0.028*<br>(0.016)</td><td>0.005<br>(0.007)</td><td>0.006<br>(0.009)</td><td>0.005<br>(0.009)</td><td>0.002<br>(0.014)</td><td>0.021*<br>(0.012)</td><td></td></tr><tr><td colspan="10" style="text-align: center; font-weight: bold;">IV:</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>0.081**<br>(0.039)</td><td>0.192**<br>(0.092)</td><td>0.095*<br>(0.052)</td><td>0.027<br>(0.034)</td><td>0.035<br>(0.048)</td><td>0.020<br>(0.039)</td><td>0.013<br>(0.097)</td><td>0.157<br>(0.098)</td><td>[0.019,0.489]*</td></tr><tr><td colspan="10" style="text-align: center; font-weight: bold;">OLS:</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>0.012**<br>(0.005)</td><td>-0.018**<br>(0.008)</td><td>0.028***<br>(0.008)</td><td>0.001<br>(0.004)</td><td>-0.002<br>(0.006)</td><td>0.005<br>(0.006)</td><td>-0.034***<br>(0.011)</td><td>0.016***<br>(0.005)</td><td></td></tr><tr><td>Mean dep. var.</td><td>0.214</td><td>0.231</td><td>0.203</td><td>0.160</td><td>0.161</td><td>0.159</td><td>0.280</td><td>0.217</td><td></td></tr><tr><td>Observations</td><td>16202</td><td>3362</td><td>3708</td><td>17378</td><td>9374</td><td>8004</td><td>4279</td><td>5070</td><td></td></tr><tr><td>F-stat, excluded instrument</td><td>20.33</td><td>15.25</td><td>13.24</td><td>22.32</td><td>16.41</td><td>22.75</td><td>22.92</td><td>5.46</td><td></td></tr><tr><td>News entity dyad FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td></tr><tr><td>Year-, MoY-, DoW FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td></tr><tr><td>Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td></td></tr></tbody></table>

**Note:** The table presents the estimation of the effect of the absence of internet outage (the reduced form), as well as the log of all conflict-related tweets in the conflict zone (estimated using 2SLS and OLS), on the similarity in conflict news. The unit of observation is day × news entity dyad, and all estimates are based on the subsample of day × news entity dyad with conflict coverage. The dependent variables are the similarity scores attributed to the conflict-related content of a news entity dyad on a given day, for a set of dyads: all US TV network pairs—namely, all pairs among ABC, Bloomberg, CBS, CNN, Fox News, MSNBC, NBC, and PBS (column 1); U.S. Cable TV—namely, all pairs among CNN, Fox News, and MSNBC (column 2); all U.S. TV non-Cable—namely, all pairs among ABC, Bloomberg, CBS, NBC, and PBS (column 3); all pairs made of a US TV network and a news agency (column 4); all pairs made of a US Cable TV network and a news agency (column 5); all pairs made of a US non-Cable TV network and a news agency (column 6); the news agency dyad—namely, AP to Reuters (column 7); and all pairs made of a US TV network and Al Jazeera (column 8). “News entity dyad-, Year-, MoY-, DoW- FEs” denotes fixed effects for each news entity dyad, each calendar year, each month of the year, and each day of the week. All specifications include the following controls: dummies for Palestinian fatality incidence, Israeli fatality incidence, and the Gaza war; the inverse hyperbolic sine transformation of the total number of casualties on each side of the conflict during the 28 days prior to day t; U.S. domestic news pressure; a dummy for other conflicts involving Israel; a dummy for days with rain; rain and wind intensity; and a dummy for the second wave of Twitter data collection (collectively referred to as “Controls”). Standard errors are clustered at the date level and reported in parentheses. Anderson-Rubin weak instrument robust confidence set (90%) presented in brackets (in column 8 of the IV Panel). *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>**Table 9:** Conflict-zone internet outages and the engagement with online U.S. TV news content</caption><thead><tr><th rowspan="3">Dependent variable:</th><th colspan="2">US TV's posting activity</th><th colspan="4">Online engagement with U.S. TV content:</th></tr><tr><th>Presence of a post</th><th>Number of posts</th><th>Log of total interactions</th><th>Likes in top 20%</th><th>Shares in top 20%</th><th>Comments in top 20%</th></tr><tr><th>Linear</th><th>PPML</th><th>Linear</th><th>Linear</th><th>Linear</th><th>Linear</th></tr></thead><tbody><tr><td>Sample restriction: at least one post</td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Estimation model:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td><td>(5)</td><td>(6)</td></tr><tr><td></td><td colspan="6" style="text-align: center; font-weight: bold;">Reduced Form:</td></tr><tr><td>No internet outage, conflict zone</td><td>0.014*<br>(0.008)</td><td>0.284**<br>(0.111)</td><td>0.217**<br>(0.095)</td><td>0.040*<br>(0.022)</td><td>0.047**<br>(0.023)</td><td>0.079***<br>(0.025)</td></tr><tr><td></td><td colspan="6" style="text-align: center; font-weight: bold;">IV:</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>0.103*<br>(0.060)</td><td>1.477***<br>(0.454)</td><td>0.947**<br>(0.433)</td><td>0.175*<br>(0.098)</td><td>0.205*<br>(0.109)</td><td>0.343***<br>(0.122)</td></tr><tr><td></td><td colspan="6" style="text-align: center; font-weight: bold;">OLS:</td></tr><tr><td>Log(Conflict tweets), conflict zone</td><td>0.144***<br>(0.010)</td><td>1.466***<br>(0.079)</td><td>0.647***<br>(0.074)</td><td>0.101***<br>(0.015)</td><td>0.098***<br>(0.020)</td><td>0.162***<br>(0.019)</td></tr><tr><td>Observations</td><td colspan="2">26204</td><td colspan="2">2614</td><td colspan="2">2614</td></tr><tr><td>Mean dep. var.</td><td colspan="2">0.100</td><td colspan="2">7.053</td><td colspan="2">0.200</td></tr><tr><td>F-stat, Internet outage</td><td colspan="2">28.72</td><td colspan="2">14.99</td><td colspan="2">14.99</td></tr><tr><td>Network-, Year-, MoY-, DoW- FEs</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Controls</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

**Note:** The table presents the estimation of the effect of the absence of internet outage (the reduced form), as well as the log of all conflict-related tweets in the conflict zone (estimated using 2SLS and OLS), on online activity related to TV conflict news. The unit of observation is day × TV network. The dependent variables are based on data from the official Facebook accounts of U.S. TV networks, collected through Facebook's CrowdTangle API. These variables include the presence and number of posts related to the conflict zone (columns 1 and 2), the log of the total number of interactions on these conflict-related posts (column 3), and a dummy indicating whether the number of likes, shares, and comments falls within the top 20% of the in-sample distribution. Columns 1 and 2 use the full sample of day × TV network observations, while columns 2 to 6 focus on the subsample of day × TV network observations with at least one post related to the conflict zone. “Network-, Year-, MoY-, DoW- FEs” denotes fixed effects for each TV network, each calendar year, each month of the year, and each day of the week. All specifications include the following controls: dummies for Palestinian fatality incidence, Israeli fatality incidence, and the Gaza war; the inverse hyperbolic sine transformation of the total number of casualties on each side of the conflict during the 28 days prior to day t; U.S. domestic news pressure; a dummy for other conflicts involving Israel; a dummy for days with rain; rain and wind intensity; and a dummy for the second wave of Twitter data collection (collectively referred to as “Controls”). Standard errors are clustered at the date level and reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
A Online Appendix

<figure><img src="image_12.png" /><figcaption>**Figure A1:** CNN uses videos from Twitter and YouTube of the conflict zones referencing sources directly on the screen</figcaption></figure>

**Source:** Left: https://edition.cnn.com/videos/world/2022/03/01/ukraine-clarissa-ward-cooper-full-episode-acfc-vpx.cnn, accessed January 3, 2025.
Right: https://archive.org/details/CNNW_20140801_030000 Anderson_Cooper_360/start/1930/end/1990, accessed January 3, 2025.

<figure><img src="image_13.png" /><figcaption>**Figure A2:** CNN quotes from Twitter account of a 16-year-old Farah Baker, who describes life in Gaza during the 2014 Gaza war</figcaption></figure>

**Source:** Screenshots from CNN broadcast, available at: https://edition.cnn.com/videos/world/2014/07/31/nr-bts-life-of-gaza-resident-farah-baker.cnn, accessed January 3, 2025.
<figure><img src="image_14.png" /><figcaption>**Figure A3:** Casualties and conflict-related tweets over time</figcaption></figure>

**Note:** The figure in Panel A illustrates the timeline of weekly fatalities over the study period, with data sourced from B'tselem. The dashed orange area highlights the two weeks of the most intense fighting during the observation period, referred to as the 2014 Gaza War, during which 2,289 people lost their lives. (Note: The scale of the y-axis does not allow these fatalities to be fully represented in the graph.) Panel B depicts the timeline of weekly tweets related to the conflict and originating from the conflict zone over the study period. Tweets were collected from the Twitter website for the period from November 2009 to April 2016 and via the Twitter API for the period from April 2016 to December 2019. The label “w1” on the x-axis indicates the first week of each year.
<figure><img src="image_15.png" /><figcaption>**Figure A4:** Summary statistics of conflict coverage across networks</figcaption></figure>

**Note:** The figure presents the means and 95% confidence intervals of number of conflict keywords, slant score, mentions of civilians, youth and babies, and negative contextual sentiment score in the conflict news by TV network. U.S. networks are sorted from conservative to liberal. Panels A and B use the full sample of day × TV network observations, while Panels C and D focuses on the subsample of day × TV network observations with conflict coverage.
<figure><img src="image_16.png" /><figcaption>Figure A5: TV coverage and conflict events</figcaption></figure>

Note: The figure presents the daily probability of coverage of the conflict zone by network separately for days with and without fatalities on either side of the conflict.

<figure><img src="image_17.png" /><figcaption>Figure A6: Slant distribution, from -2 (very pro-IL) to +2 (very pro-PS)</figcaption></figure>

Note: The figure shows the distribution of slant scores, ranging from -2 (very pro-Israeli) to +2 (very pro-Palestinian), for day × U.S. TV network observations when the conflict is covered. Slant scores were generated using a fine-tuned GPT-3 Curie model trained on human-coded data. For stories exceeding the model's length limit, they were split into segments, with the final score being the average of the sub-segment scores. While the theoretical range is -2 to 2, the model's predictions for our sample range from -1 (pro-Israeli) to 1.19 (pro-Palestinian).
<figure><img src="image_18.png" /><figcaption>**Figure A7:** The composition of conflict-related tweets from the conflict zone and user accounts which post these tweets by language and location</figcaption></figure>

**Note:** This figure displays the composition of Twitter users (left side of each panel) and conflict-related tweets (right side of each panel). Panel A shows the distribution by location–users in Israel and the Palestinian territories. Panel B presents the distribution by language used: English only, English and Arabic, and English and Hebrew. Differences between user and tweet compositions reflect variation in tweeting activity across user groups.
<caption>**Figure A8:** Lightning strikes in the conflict zone</caption>

Panel A. An example of a day with a thunderstorm: a map of locations of lightning strikes on November 16, 2014

<figure><img src="image_19.png" /></figure>

Panel B. Average number of thunderstorm days by calendar month

<figure><img src="image_20.png" /></figure>

**Note:** The figure in Panel A displays the cellular antennas, lightning strikes and lightning strikes hitting an antenna for the date of November 16, 2014. Lightning strikes are considered as hitting an antenna when they fall at maximum 500 meters from the antenna. Panel B presents the histogram of the number of days with lightning strikes by calendar month, for the full sample of days included in our analysis.
<caption>**Figure A9:** Internet traffic between Israel and the Palestinian Territories and the rest of the internet</caption>

Standardized deviations of BGP prefix counts

<figure><img src="image_21.png" /></figure>

**Note:** The figure displays the mean and 95% CI of the standardized deviations of visible BGP prefix counts for each major autonomous system operating in the conflict zone—Partner, Bezeq and Cellcom (in Israel) as well as Paltel, Wataniya and Jawwal (in the Palestinian Territories)–, for days classified as days with “Absence of traffic” and “No absence of traffic”.
<figure><img src="image_22.png" /><figcaption>**Figure A10:** Internet outages and slant of conflict stories, reduced form – extended version</figcaption></figure>

**Note:** The figure illustrates the reduced-form relationship between internet outages in the conflict zone and the slant of U.S. TV news. The unit of observation is day × TV network. Each panel summarizes the slant of conflict-related broadcasts in U.S. TV, across days and TV networks. There are three outcome variables: continuous slant score, the pro-Palestinian slant dummy (indicating continuous score above 0.1), and the pro-Israeli slant dummy (indicating continuous score below -0.1). The summaries are presented separately for days with no fatalities (Panel A), Palestinian fatalities only (Panel B), Israel fatalities only (Panel C), and fatalities on both sides (Panel D). The data are summarized separately for days with and without internet outages in the conflict zone. Error bars show 90% CIs for the difference in the mean levels of outcome on days with and without internet outage in the conflict zone.
**Figure A11:** Liberal-conservative ideology of the channels' viewers, PEW Research Center (2014)

Ideological Placement of Each Source's Audience

*Average ideological placement on a 10-point scale of ideological consistency of those who got news from each source in the past week...*

<figure><img src="image_23.png" /></figure>

American Trends Panel (wave 1). Survey conducted March 19-April 29, 2014. Q22. Based on all web respondents. Ideological consistency based on a scale of 10 political values questions (see About the Survey for more details.) ThinkProgress, Dailykos, Mother Jones, and The Ed Schultz Show are not included in this graphic because audience sample sizes are too small to analyze.

PEW RESEARCH CENTER

**Note:** The figure presents the PEW Research Center's ranking of the ideology of the audience of U.S. TV networks. The networks data for which are used in the paper are highlighted orange.

Source: https://www.pewresearch.org/journalism/2014/10/21/political-polarization-media-habits/ (accessed January 3, 2025).
<figure><img src="image_24.png" /><figcaption>**Figure A12:** Internet outages and conflict coverage in cable networks, reduced form</figcaption></figure>

**Note:** The figure illustrates the reduced-form relationship between internet outages in the conflict zone and the extent and content of conflict coverage in U.S. TV networks, separately for liberal cable networks (CNN and MSNBC) and a conservative cable network (FOX). The unit of observation is day × TV network. Panel A uses the full sample of day × TV network observations, Panel B uses the subsample of news with conflict coverage, and Panel C uses the sample of day × TV network observations on the days with Palestinian fatalities. “Length in minutes” measures the total number of minutes devoted to the conflict by a given network on a given day. “Mentions of civilians” represent the number of keywords on these topics, divided by the total number of words and multiplied by 1,000. “Continuous slant score” is the LLM based measure of slant, from -2 (very pro-IL) to +2 (very pro-PS). Error bars show 90% CIs for the difference in the mean levels of outcome on days with and without internet outage in the conflict zone.
<table><caption>Table A1: Summary statistics, main variables 1/4</caption><thead><tr><th></th><th>Observations<br>(Network × Day)</th><th>Mean</th><th>Standard<br>deviation</th><th>Median</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td colspan="7"><strong>Social media in the conflict zone:</strong></td></tr><tr><td>Log(All tweets)</td><td>27596</td><td>7.20</td><td>0.57</td><td>7.20</td><td>4.20</td><td>9.25</td></tr><tr><td>Log(Conflict tweets)</td><td>27596</td><td>6.56</td><td>0.62</td><td>6.57</td><td>3.56</td><td>9.06</td></tr><tr><td colspan="7"><strong>Outages in the conflict zone:</strong></td></tr><tr><td>Internet outage</td><td>27596</td><td>0.15</td><td>0.36</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Lightning</td><td>27596</td><td>0.12</td><td>0.33</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Absence of traffic</td><td>19763</td><td>0.04</td><td>0.21</td><td>0</td><td>0</td><td>1</td></tr><tr><td colspan="7"><strong>Controls:</strong></td></tr><tr><td>Palestinian fatality incidence</td><td>27596</td><td>0.17</td><td>0.38</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Israeli fatality incidence</td><td>27596</td><td>0.02</td><td>0.16</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Gaza war</td><td>27596</td><td>0.00</td><td>0.06</td><td>0</td><td>0</td><td>1</td></tr><tr><td>IHS(Palestinian fatalities, t-28 to t-1)</td><td>27596</td><td>2.45</td><td>1.30</td><td>2.31</td><td>0.00</td><td>8.32</td></tr><tr><td>IHS(Israeli fatalities, t-28 to t-1)</td><td>27596</td><td>0.63</td><td>0.88</td><td>0.00</td><td>0.00</td><td>4.87</td></tr><tr><td>US domestic news pressure</td><td>27596</td><td>0.40</td><td>0.22</td><td>0.37</td><td>0.00</td><td>2.46</td></tr><tr><td>Other conflict with Israel</td><td>27596</td><td>0.05</td><td>0.23</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Day with rain</td><td>27596</td><td>0.64</td><td>0.48</td><td>1</td><td>0</td><td>1</td></tr><tr><td>Share of population with rain</td><td>27596</td><td>0.18</td><td>0.22</td><td>0.15</td><td>0.00</td><td>1.00</td></tr><tr><td>Mean wind speed</td><td>27596</td><td>1.96</td><td>2.72</td><td>0.25</td><td>0.00</td><td>13.40</td></tr><tr><td>Twitter data: 2nd wave</td><td>27596</td><td>0.08</td><td>0.27</td><td>0</td><td>0</td><td>1</td></tr><tr><td colspan="7"><strong>The extent of coverage of the conflict zone by news agencies:</strong></td></tr><tr><td>Number of reports</td><td>22283</td><td>7.12</td><td>10.82</td><td>5</td><td>0</td><td>139</td></tr><tr><td>At least one report</td><td>22283</td><td>0.91</td><td>0.28</td><td>1</td><td>0</td><td>1</td></tr><tr><td>Log(Number of reports)</td><td>20305</td><td>1.61</td><td>0.90</td><td>1.61</td><td>0.00</td><td>4.93</td></tr><tr><td>Number of stems</td><td>22283</td><td>7746.31</td><td>11283.77</td><td>3840</td><td>0</td><td>122129</td></tr><tr><td>Log(Number of stems)</td><td>20305</td><td>8.37</td><td>1.22</td><td>8.42</td><td>2.56</td><td>11.71</td></tr><tr><td colspan="7"><strong>The extent of coverage of the conflict zone by U.S. TV news:</strong></td></tr><tr><td>Number of keywords</td><td>27596</td><td>20.00</td><td>102.02</td><td>0</td><td>0</td><td>3340</td></tr><tr><td>Number of stories</td><td>27596</td><td>0.82</td><td>2.72</td><td>0</td><td>0</td><td>54</td></tr><tr><td>Prime time coverage</td><td>27596</td><td>0.09</td><td>0.28</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Length in minutes</td><td>27596</td><td>11.07</td><td>48.39</td><td>0</td><td>0</td><td>1316</td></tr><tr><td>Length in minutes, subsample: coverage = 1</td><td>5854</td><td>52.21</td><td>94.31</td><td>22</td><td>3</td><td>1316</td></tr><tr><td colspan="7"><strong>The slant of coverage of the conflict zone by U.S. TV news:</strong></td></tr><tr><td>Slant score, full sample</td><td>27596</td><td>0.05</td><td>0.38</td><td>0.00</td><td>-1.00</td><td>1.19</td></tr><tr><td>Slant score, subsample: coverage = 1</td><td>5854</td><td>0.24</td><td>0.81</td><td>0.51</td><td>-1.00</td><td>1.19</td></tr><tr><td>Pro-PS slant dummy, full sample</td><td>27596</td><td>0.13</td><td>0.33</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Pro-PS slant dummy, subsample: coverage = 1</td><td>5854</td><td>0.60</td><td>0.49</td><td>1</td><td>0</td><td>1</td></tr><tr><td>Pro-IL slant dummy, full sample</td><td>27596</td><td>0.07</td><td>0.25</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Pro-IL slant dummy, subsample: coverage = 1</td><td>5854</td><td>0.32</td><td>0.47</td><td>0</td><td>0</td><td>1</td></tr></tbody></table>

<footnote>Note: The table presents summary statistics for variables measuring social media activity, internet outages in conflict zones, control variables, and three sets of outcome variables: the extent of coverage of the conflict zone by news agencies, and both the extent and slant of coverage of the conflict zone by U.S. TV news. The unit of observation is TV network × day, except for the measures of coverage extent by news agencies, where it is news agency × day.</footnote>
<table><caption>Table A2: Summary statistics, main variables 2/4</caption><thead><tr><th></th><th>Observations<br>(Network × Day)</th><th>Mean</th><th>Standard<br>deviation</th><th>Median</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td colspan="7"><strong>The number of keywords on the following topic divided by the total number of words (x1000):</strong></td></tr><tr><td>Civilians</td><td>5854</td><td>3.16</td><td>2.03</td><td>2.84</td><td>0.00</td><td>10.45</td></tr><tr><td>Youth and babies</td><td>5854</td><td>0.00</td><td>0.02</td><td>0.00</td><td>0.00</td><td>0.22</td></tr><tr><td>Civilian casualties</td><td>5854</td><td>0.01</td><td>0.06</td><td>0.00</td><td>0.00</td><td>0.59</td></tr><tr><td>Pictures &amp; videos</td><td>5854</td><td>0.03</td><td>0.18</td><td>0.00</td><td>0.00</td><td>1.00</td></tr><tr><td>Reporters</td><td>5854</td><td>0.38</td><td>0.49</td><td>0.00</td><td>0.00</td><td>1.00</td></tr><tr><td>IL &amp; PS officials</td><td>5854</td><td>1.33</td><td>1.66</td><td>0.79</td><td>0.00</td><td>10.98</td></tr><tr><td>US officials</td><td>5854</td><td>0.07</td><td>0.20</td><td>0.00</td><td>0.00</td><td>1.44</td></tr><tr><td>Elections</td><td>5854</td><td>0.20</td><td>0.47</td><td>0.00</td><td>0.00</td><td>3.28</td></tr><tr><td>Israeli army</td><td>5854</td><td>0.05</td><td>0.17</td><td>0.00</td><td>0.00</td><td>1.54</td></tr><tr><td>Military operations</td><td>5854</td><td>0.01</td><td>0.03</td><td>0.00</td><td>0.00</td><td>0.32</td></tr><tr><td>Heavy weapons</td><td>5854</td><td>1.76</td><td>2.46</td><td>0.86</td><td>0.00</td><td>22.28</td></tr><tr><td>Hamas</td><td>5854</td><td>0.79</td><td>1.47</td><td>0.00</td><td>0.00</td><td>6.94</td></tr><tr><td colspan="7"><strong>The dummy indicating a mention of the following:</strong></td></tr><tr><td>Concrete small geographic locations in<br>... the Palestinian territories</td><td>5854</td><td>0.14</td><td>0.34</td><td>0</td><td>0</td><td>1</td></tr><tr><td>... Israel</td><td>5854</td><td>0.16</td><td>0.37</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Twitter</td><td>5854</td><td>0.16</td><td>0.36</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Facebook</td><td>5854</td><td>0.15</td><td>0.36</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Instagram</td><td>5854</td><td>0.02</td><td>0.13</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Youtube</td><td>5854</td><td>0.04</td><td>0.21</td><td>0</td><td>0</td><td>1</td></tr><tr><td>News agencies</td><td>5854</td><td>0.02</td><td>0.13</td><td>0</td><td>0</td><td>1</td></tr><tr><td colspan="7"><strong>The emotional intensity of conflict coverage by U.S. TV (1/2):</strong></td></tr><tr><td colspan="7">(based on NRC Emotion Lexicon (Mohammad, 2018) and sentiment scores (Hutto and Gilbert, 2014))</td></tr><tr><td>All emotions</td><td>5854</td><td>18.37</td><td>3.76</td><td>17.87</td><td>9.05</td><td>37.85</td></tr><tr><td>Negative emotions</td><td>5854</td><td>7.82</td><td>3.38</td><td>7.16</td><td>1.39</td><td>30.18</td></tr><tr><td>Negative contextual sentiment</td><td>5854</td><td>15.60</td><td>5.62</td><td>15.20</td><td>1.30</td><td>43.70</td></tr><tr><td>Positive emotions</td><td>5854</td><td>7.54</td><td>1.29</td><td>7.49</td><td>3.60</td><td>17.03</td></tr><tr><td>Positive contextual sentiment</td><td>5854</td><td>19.79</td><td>5.24</td><td>19.80</td><td>2.80</td><td>46.90</td></tr><tr><td>Neutral emotions</td><td>5854</td><td>3.67</td><td>0.83</td><td>3.60</td><td>1.34</td><td>8.03</td></tr><tr><td colspan="7"><strong>The emotional intensity of conflict coverage by U.S. TV (2/2):</strong></td></tr><tr><td colspan="7">(based on revised NRC Emotion Lexicon (Zad et al., 2021))</td></tr><tr><td>Negative emotions</td><td>5854</td><td>2.86</td><td>1.51</td><td>2.58</td><td>0.00</td><td>13.92</td></tr><tr><td>Anger</td><td>5854</td><td>0.90</td><td>0.53</td><td>0.81</td><td>0.00</td><td>5.71</td></tr><tr><td>Fear</td><td>5854</td><td>1.00</td><td>0.58</td><td>0.90</td><td>0.00</td><td>5.06</td></tr><tr><td>Disgust</td><td>5854</td><td>0.38</td><td>0.27</td><td>0.33</td><td>0.00</td><td>2.41</td></tr><tr><td>Sadness</td><td>5854</td><td>0.71</td><td>0.42</td><td>0.64</td><td>0.00</td><td>3.80</td></tr><tr><td>Positive emotions</td><td>5854</td><td>1.71</td><td>0.79</td><td>1.61</td><td>0.00</td><td>7.42</td></tr><tr><td>Joy</td><td>5854</td><td>0.74</td><td>0.43</td><td>0.67</td><td>0.00</td><td>3.51</td></tr><tr><td>Trust</td><td>5854</td><td>1.03</td><td>0.49</td><td>0.98</td><td>0.00</td><td>4.84</td></tr><tr><td>Neutral emotions</td><td>5854</td><td>1.82</td><td>0.65</td><td>1.78</td><td>0.00</td><td>6.33</td></tr><tr><td>Anticipation</td><td>5854</td><td>1.49</td><td>0.56</td><td>1.45</td><td>0.00</td><td>4.31</td></tr><tr><td>Surprise</td><td>5854</td><td>0.42</td><td>0.32</td><td>0.35</td><td>0.00</td><td>5.06</td></tr></tbody></table>

<footnote>Note: The table provides summary statistics for variables capturing the frequency of topic mentions and the emotional intensity in U.S. TV coverage of the conflict zone. The unit of observation is TV network × day. The sample includes all TV network × day observations with conflict zone coverage for variables related to topics and emotions in the news.</footnote>
<table><caption>Table A3: Summary statistics, main variables 3/4</caption><thead><tr><th></th><th>Observations<br>(Network Pair<br>× Day)</th><th>Mean</th><th>Standard<br>deviation</th><th>Median</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td colspan="7"><strong>Similarity of conflict-related stories:</strong></td></tr><tr><td>among all U.S. TV</td><td>16202</td><td>0.21</td><td>0.14</td><td>0.20</td><td>-0.15</td><td>0.76</td></tr><tr><td>among U.S. cable TV</td><td>3362</td><td>0.23</td><td>0.14</td><td>0.21</td><td>-0.14</td><td>0.68</td></tr><tr><td>among U.S. non-cable TV</td><td>3708</td><td>0.20</td><td>0.13</td><td>0.19</td><td>-0.09</td><td>0.76</td></tr><tr><td>between all U.S. TV and newswires</td><td>17378</td><td>0.16</td><td>0.14</td><td>0.14</td><td>-0.22</td><td>0.81</td></tr><tr><td>between cable U.S. TV and newswires</td><td>9374</td><td>0.16</td><td>0.14</td><td>0.14</td><td>-0.22</td><td>0.81</td></tr><tr><td>between non-cable U.S. TV and newswires</td><td>8004</td><td>0.16</td><td>0.14</td><td>0.14</td><td>-0.21</td><td>0.74</td></tr><tr><td>between newswires: AP-Reuters</td><td>4279</td><td>0.28</td><td>0.21</td><td>0.24</td><td>-0.13</td><td>0.94</td></tr><tr><td>between all U.S. TV and Al Jazeera</td><td>5070</td><td>0.22</td><td>0.13</td><td>0.21</td><td>-0.11</td><td>0.73</td></tr></tbody></table>

Note: The table presents summary statistics for variables measuring content similarity in stories about the conflict zone, both across U.S. TV networks and between each U.S. TV network and the newswires (AP and Reuters, separately). The unit of observation is outlet pair × day. The sample includes all outlet pair × day observations in which both outlets in the pair provided coverage of the conflict zone. For example, the variable “between all U.S. TV and newswires” is calculated using all pairs that include one U.S. TV network and either AP or Reuters, on days when both outlets published at least one story about the conflict zone.

<table><caption>Table A4: Summary statistics, main variables 4/4</caption><thead><tr><th></th><th>Observations<br>(Network × Day)</th><th>Mean</th><th>Standard<br>deviation</th><th>Median</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td colspan="7"><strong>US TV's posting activity and online engagement with U.S. TV content:</strong></td></tr><tr><td>Presence of a post</td><td>26204</td><td>0.10</td><td>0.30</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Number of posts</td><td>26204</td><td>0.17</td><td>0.72</td><td>0</td><td>0</td><td>23</td></tr><tr><td>Log of total interactions</td><td>2614</td><td>7.05</td><td>1.79</td><td>7.02</td><td>1.10</td><td>13.48</td></tr><tr><td>Likes in top 20%</td><td>2614</td><td>0.20</td><td>0.40</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Shares in top 20%</td><td>2614</td><td>0.20</td><td>0.40</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Comments in top 20%</td><td>2614</td><td>0.20</td><td>0.40</td><td>0</td><td>0</td><td>1</td></tr></tbody></table>

Note: The table presents summary statistics for variables documenting U.S. TV networks' online activity on Facebook regarding the conflict zone, as well as the audience engagement generated by this content. The unit of observation is network × day. The sample includes the full set of TV network × day observations (beginning when each network established a Facebook account) for variables measuring the presence and number of posts, and the subset of TV network × day observations with at least one post about the conflict zone for variables measuring engagement metrics such as interactions, likes, shares, and comments.
<table><caption>**Table A5:** Summary statistics: Extent of conflict coverage overall and by U.S. TV network</caption><thead><tr><th rowspan="2">Network</th><th rowspan="2">Obs.<br>(Days)</th><th>Dummy:</th><th colspan="4">Number of TV News Stories:</th></tr><tr><th>Coverage<br>Mean</th><th>Mean</th><th>Min</th><th>Max</th><th>Mean<br>if covered</th></tr></thead><tbody><tr><td>All US networks</td><td>3,631</td><td>0.27</td><td>1.42</td><td>0</td><td>54</td><td>4.40</td></tr><tr><td>FOX</td><td>3,631</td><td>0.38</td><td>1.65</td><td>0</td><td>54</td><td>4.30</td></tr><tr><td>PBS</td><td>3,631</td><td>0.34</td><td>1.16</td><td>0</td><td>30</td><td>3.37</td></tr><tr><td>CNN</td><td>3,631</td><td>0.29</td><td>1.11</td><td>0</td><td>35</td><td>3.82</td></tr><tr><td>MSNBC</td><td>3,631</td><td>0.21</td><td>0.69</td><td>0</td><td>28</td><td>3.29</td></tr><tr><td>Bloomberg</td><td>2,179</td><td>0.14</td><td>0.31</td><td>0</td><td>13</td><td>2.26</td></tr><tr><td>CBS</td><td>3,631</td><td>0.12</td><td>0.57</td><td>0</td><td>37</td><td>4.90</td></tr><tr><td>NBC</td><td>3,631</td><td>0.10</td><td>0.44</td><td>0</td><td>29</td><td>4.41</td></tr><tr><td>ABC</td><td>3,631</td><td>0.08</td><td>0.40</td><td>0</td><td>24</td><td>4.76</td></tr><tr><td>Al Jazeera</td><td>1,785</td><td>0.76</td><td>6.46</td><td>0</td><td>37</td><td>8.47</td></tr></tbody></table>

**Note:** The table provides summary statistics on the extent of coverage of the conflict zone by each U.S. TV network and Al Jazeera. The unit of observation is TV network × day. The sample includes all TV network × day observations.