DOI: 10.5061/dryad.44j0zpcmq
title: Supersharers of fake news on Twitter

description: Governments may have the capacity to flood social media with fake news,
 but little is known about the use of flooding by ordinary voters. In this
 work, we identify 2107 registered US voters that account for 80% of the
 fake news shared on Twitter during the 2020 US presidential election by an
 entire panel of 664,391 voters. We find that supersharers are important
 members of the network, reaching a sizable 5.2% of registered voters on
 the platform. Supersharers have a significant overrepresentation of women,
 older adults, and registered Republicans. Supersharers' massive
 volume does not seem automated but is rather generated through manual and
 persistent retweeting. These findings highlight a vulnerability of social
 media for democracy, where a small group of people distort the political
 reality for many.
description: This dataset contains aggregated information necessary to
 replicate the results reported in our work on Supersharers of Fake News on
 Twitter while respecting and preserving the privacy expectations of
 individuals included in the analysis. No individual-level data is provided
 as part of this dataset.  The data collection process
 that enabled the creation of this dataset leveraged a large-scale panel of
 registered U.S. voters matched to Twitter accounts. We examined the
 activity of 664,391 panel members who were active on Twitter during the
 months of the 2020 U.S. presidential election (August to November 2020,
 inclusive), and identified a subset of 2,107 supersharers, which are the
 most prolific sharers of fake news in the panel that together account for
 80% of fake news content shared on the platform. We rely on a source-level
 definition of fake news, that uses the manually-labeled list of fake news
 sites by Grinberg et al. 2019 and an updated list based on NewsGuard
 ratings (commercially available, but not provided as part of this
 dataset), although the results were robust to different
 operationalizations of fake news sources. We restrict the analysis to
 tweets with external links that were identified as political by a machine
 learning classifier that we trained and validated against human coders,
 similar to the approach used in prior work. <br>We address our
 research questions by contrasting supersharers with three reference
 groups: people who are the most prolific sharers of non-fake political
 tweets (supersharers non-fake group; SS-NF), a group of average fake news
 sharers, and a random sample of panel members. In particular, we identify
 the distinct sociodemographic characteristics of supersharers using a
 series of multilevel regressions, examine their use of Twitter through
 existing tools and additional statistical analysis, and study
 supersharers' reach by examining the consumption patterns of voters
 that follow supersharers.
description: # Supersharers of Fake News on Twitter This repository contains data and
 code for replication of the results presented in the paper. The folders
 are mostly organized by research questions as detailed below. Each folder
 contains the code and publicly available data necessary for the
 replication of results. Importantly, no individual-level data is provided
 as part of this repository. De-identified individual-level data can be
 attained for IRB-approved uses under the terms and conditions specified in
 the paper. Once access is granted, the restricted-access data is expected
 to be located under `./restricted_data`. The folders in this repository
 are the following: ## Preprocessing Code under the `preprocessing` folder
 contains the following: 1. source classifier - the code used to train a
 classifier based on NewsGuard domain flags to match the fake news labels
 source definition use in Grinberg et el. 2019 labels. 2. political
 classifier - the code used to identify political tweets, including daily
 training of the models. 3. sample creation - the code used to identify
 supersharers in the larger sample of active panelists as well as the
 reference groups. ## RQ0 Prevalence Data and replication code for Figure 1
 in the main paper as generated by `01_prevalence.Rmd`. The data in this
 folder is the following: ### fig1\_panelA\_data.csv * date - value ranging
 from 2020-08-01 to 2020-11-30 * total_shared_pol_tweets - aggregate number
 of political news tweets shared by the panel. *
 total_shared_fake_pol_tweets - aggregate number of political fake news
 news tweets shared by the panel. * ss_fake_pol_tweets - aggregate number
 of political fake news tweets shared by supersharers. ###
 Fig1\_panelB\_data.csv.gz * cat - content category: political news or fake
 news. * pct_ppl - percent of people that shared this percent of the
 aggregate number of content. * pct_shared - percent of the aggregate
 amount of content shared. ### Fig1\_panelC\_data.csv.gz * bins -
 percentile. * cat - group category: supersharers (ss-fn) or SS-NF
 (ss-pol). * n - numbers of users in this bin. * percent - percent of users
 in bin. ### article\_level\_claims\_validation.csv * fake news source -
 yes/no indication of whether the link originated from a fake news source *
 article link / claim - article URL or main claim. * verifiably false - is
 the main article claim verifiably false. * fact-checking - URL for
 fact-checked information. * fact-checking additional - additional URL for
 fact-checked information. ## RQ1 Code under the `RQ1` folder contains the
 following: 1. `01_reach.ipynb` - Python (mostly pySpark) code for
 calculating various measures of supersharers' reach. 2.
 `02_importance.Rmd` - Code for replicating statistical testing of the
 calculated measures of network (topological) importance and engagement in
 RQ1. Requires access to the restricted-access data. ## RQ2 Data and
 replication code for RQ2 including: 1. `01_regressions.Rmd` - Code for the
 regressions comparing supersharers to reference groups. Requires access to
 the restricted-access data. 2. `02_fig2.Rmd` - Code for producing Figure
 2. Panels A, C, and D can be recreated without access to restricted-access
 data. Panel B, which shows the full age distribution across groups,
 requires restricted data. 3. `03_figS5.Rmd` - Code for producing Figure S5
 in the SM.13. The data in this folder is the following: ###
 Fig2\_panelA\_data.csv * cat - group category: Supersharers, SS-NF, Panel,
 and AVG. fake. * gender - Male, Female, or Unkown. * prp - proportion of
 male, female, or unknown gender in the group. * prp_lo - the above
 proportion lower 95% CI. * prp_hi - the above proportion upper 95% CI. ###
 Fig2\_panelC\_data.csv * cat - group category: Supersharers, SS-NF, Panel,
 and AVG. fake. * party_reg - party of registration: Democrat (Dem.),
 Republican (Rep.), or Independent (Ind.). * prp - proportion of registered
 Democrats, Republicans, or Independents in the group. * prp_lo - the above
 proportion lower 95% CI. * prp_hi - the above proportion upper 95% CI. ###
 Fig2\_panelD\_data.csv * cat - group category: Supersharers, SS-NF, Panel,
 and AVG. fake. * prp - proportion of Caucasians in the group. * prp_lo -
 the above proportion lower 95% CI. * prp_hi - the above proportion upper
 95% CI. ### Fig\_S5\_state\_aggregated\_percent.csv * state_x - US state.
 * cat - group category: Supersharers (ss-fn), SS-NF (ss-pol), Panel
 (other), and AVG. fake (fake_sharer). * percent - percent of panelists in
 state from this group. ## RQ3 Data and replication code for RQ3 including:
 1. `01_temporal_analysis.ipynb` - pySpark code for calculating various
 time of day, interval between subsequent posts, etc. to facilitate
 temporal analysis described in SM.6. 2. `02_fig_S3.Rmd` - code for
 reproducing Figure S3. The data in this folder is the following: ###
 bot\_manual\_labeling.csv This is the manual labeling of a sample of
 accounts as described in SM.6. * bot_or_not - indication of the annotator
 label of the account as potentially bot or not. * cat - group category:
 Supersharers (ss-fn), SS-NF (ss-pol), or Panel (other). ###
 Automation\_sharing\_hours.csv * cat - group category: Supersharers
 (ss-fn), SS-NF (ss-pol), or Panel (other). * hour_of_the_day - hour of the
 day. * count - aggregate number of tweets shared by the group in this
 hour. ### Automation\_time\_between\_tweets.csv * cat - group category:
 Supersharers (ss-fn), SS-NF (ss-pol), or Panel (other). * bins_cat - bin
 category: minutes or seconds. * bins - bin of time between tweets. * prp -
 proportion of tweets in bin in this group. * prp_lo - the above proportion
 lower 95% CI. * prp_hi - the above proportion upper 95% CI. ###
 Automation\_session\_length.csv * cat - group category: Supersharers
 (ss-fn), SS-NF (ss-pol), or Panel (other). * bins - time bin of session
 lengths. * prp - proportion of sessions in bin in this group. * prp_lo -
 the above proportion lower 95% CI. * prp_hi - the above proportion upper
 95% CI. ### Automation\_sessions\_count.csv * cat - group category:
 Supersharers (ss-fn), SS-NF (ss-pol), or Panel (other). * avg_sessions -
 average number of sessions per day. * prp - percent of users in this group
 with this number of sessions per day. * prp_lo - the above proportion
 lower 95% CI. * prp_hi - the above proportion upper 95% CI. ## Utils The
 code used for plotting in R.