Influence of fake news in Twitter during the 2016 US presidential election
نویسندگان
چکیده
We investigate the influence of fake and traditional, fact-based, news outlets on Twitter during the 2016 US presidential election. Using a comprehensive dataset of 171 million tweets covering the five months preceding election day, we identify 30 million tweets, sent by 2.2 million users, which are classified as spreading fake and extremely biased news, based on a list of news outlets curated from independent fact-checking organizations, and traditional news from right to left. We find that 29% of these tweets disseminate fake or extremely biased news. We fully characterize the networks of users spreading fake and traditional news and find the most influential users. Contrary to traditional news, where influencers are mainly journalists or news outlets with verified Twitter accounts, e.g. @FoxNews and @CNN, the majority of fake news influencers have unverified or deleted accounts. In particular, accounts with seemingly deceiving profiles are found among the top fake and extremely biased influencers. We find that the three top influencers spreading (i.e. re-tweeting) fake news websites are @PrisonPlanet, @RealAlexJones and @zerohedge and re-tweeting extremely bias news websites are @realDonaldTrump, @DailyCaller and @BreitbartNews. To understand how fake news influenced Twitter opinion during the presidential election, we perform a Granger-causality test between the time series of activity of influencers and the supporters of each presidential candidate: Trump and Clinton. Two different news spreading mechanisms are revealed: (i) The influencers spreading traditional center and left leaning news largely determine (Granger-cause) the opinion of the Clinton supporters. (ii) Remarkably, this causality is reversed for the fake news: the opinion of Trump supporters largely Granger-causes the dynamics of influencers spreading fake and extremely biased news.
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