TWITTER SEARCH UNITED STATES DRIVERS

For fake news, we find that it is the activity of Trump supporters that governs their dynamics and top spreaders of fake news are merely following it.

TWITTER SEARCH UNITED STATES DRIVERS

The top spreaders of center and left leaning news outlets, who are mainly journalists, are the main drivers of Twitter’s activity and in particular of Clinton supporters’ activity, who represent the majority in Twitter 27.

We find two different mechanisms for the dynamics of fake news and traditional news. Finally, we explore the dynamics between the top news spreaders and the supporters’ activity with a multivariate causal network reconstruction 28. The presence of two clusters of media sources and their relation with the supporters of each candidate is revealed by the analysis of the correlation of their activity. While top news spreaders of traditional news outlets are journalists and public figures with verified Twitter accounts, we find that a large number of top fake and extremely biased news spreaders are unknown users or users with deleted Twitter accounts. User diffusing fake news form more connected networks with less heterogeneous connectivity than users in traditional center and left leaning news diffusion networks. We separate traditional news outlets from the least biased to the most biased and reconstruct the information flow networks by following retweets tree for each type of media. When considering only tweets originating from non-official Twitter clients, we see a tweeting rate for users tweeting links to websites containing news classified as fake more than four times larger than for traditional media, suggesting a larger role of bots in the diffusion of fake news. We find that, among the 30.7 million tweets containing an URL directing to a news outlet website, 10% point toward websites containing fake news or conspiracy theory and 15% point toward websites with extremely biased news. We investigate the diffusion in Twitter of each type of media to understand what is their relative importance, who are the top news spreaders, and how they drive the dynamics of Twitter opinion. We relied upon the opinion of communications scholars (see Methods for details) who have classified websites as containing fake news or extremely biased news. We compare the spread of news coming from websites that have been described as displaying fake news with the spread of news coming from traditional, fact-based, news outlets with different political orientations. Here, we use a dataset of 171 million tweets sent by 11 million users covering almost the whole activity of users regarding the two main US presidential candidates, Hillary Clinton and Donald Trump, collected during the five months preceding election day and used to extract and analyze Twitter opinion trend in our previous work 27. These results have raised the question of whether such misinformation campaigns could alter public opinion and endanger the integrity of the presidential election 24. found that, during the 2016 US presidential election on Twitter, bots were responsible for the early promotion of misinformation, that they targeted influential users through replies and mentions 26 and that the sharing of fact-checking articles nearly disappears in the core of the network, while social bots proliferate 13. automated accounts, in the spread of misinformation 12, 23, 24, 25. Recent works also revealed the role of bots, i.e. A polarization in communities is also observed in the consumption of news in general 20, 21 and corresponds with political alignment 22. A comprehensive investigation of the spread of true and false news in Twitter also showed that false news is characterized by a faster and broader diffusion than true news mainly due to the attraction of the novelty of false news 12. echo chambers, where unsubstantiated claims or true information, aligned with these beliefs, are as likely to propagate virally 6, 19.

These investigations, as well as theoretical modeling 16, 17, suggest that confirmation bias 18 and social influence results in the emergence, in online social networks, of user communities that share similar beliefs about specific topics, i.e. A substantial number of studies have recently investigated the phenomena of misinformation in online social networks such as Facebook 4, 5, 6, 7, 8, 9, 10, Twitter 10, 11, 12, 13, YouTube 14, or Wikipedia 15. Indeed, massive digital misinformation has been designated as a major technological and geopolitical risk by the 2013 report of the World Economic Forum 3. While misinformation and propaganda have existed since ancient times 2, their importance and influence in the age of social media is still not clear. fabricated information that disseminate deceptive content, or grossly distort actual news reports, shared on social media platforms. Recent social and political events, such as the 2016 US presidential election 1, have been marked by a growing number of so-called “fake news”, i.e.

Twitter search united states