Polarization, Partisanship and Junk News Consumption over Social Media in the US

What kinds of social media users read junk news? We examine the distribution of the most significant sources of junk news in the three months before President Donald Trump’s first State of the Union Address. Drawing on a list of sources that consistently publish political news and information that is extremist, sensationalist, conspiratorial, masked commentary, fake news and other forms of junk news, we find that the distribution of such content is unevenly spread across the ideological spectrum. We demonstrate that (1) on Twitter, a network of Trump supporters shares the widest range of known junk news sources and circulates more junk news than all the other groups put together; (2) on Facebook, extreme hard right pages—distinct from Republican pages—share the widest range of known junk news sources and circulate more junk news than all the other audiences put together; (3) on average, the audiences for junk news on Twitter share a wider range of known junk news sources than audiences on Facebook’s public pages. POLARIZATION ON SOCIAL MEDIA Social media has become an important source of news and information in the United States. An increasing number of users consider platforms such as Twitter and Facebook a source of news. At important moments of political and military crises, social media users not only share substantial amounts of professional news, but also share extremist, sensationalist, conspiratorial, masked commentary, fake news and other forms of junk news. News on social media also reaches users indirectly, when they browse social media for other purposes. With more than 2 billion monthly active users, Facebook is the most popular social media network. The Reuters Digital News Report 2017 finds that 71% of US respondents are on Facebook, with 48% of US respondents using it for news. Given the central role that social media play in public life, these platforms have become a target for propaganda campaigns and information operations. In its review of the recent US elections, Twitter found that more than 50,000 automated accounts were linked to Russia. Facebook has revealed that content from the Russian Internet Research Agency has reached 126 million US citizens before the 2016 presidential election. Adding to reports about foreign influence campaigns, there is increasing evidence of a rise in polarization in the US news landscape in response to the 2016 election. Trust in news is strikingly divided across ideological lines, and an ecosystem of alternative news is flourishing, fueled by extremist, sensationalist, conspiratorial, masked commentary, fake news and other forms of junk news. At the same time, legacy publishers like the New York Times and the Washington Post have reported an increase in subscriptions. Social media algorithms can be purposefully used to distribute polarizing political content and misinformation. Pariser’s claim is that filter bubble effects—highly personalized algorithms that select what information to show in news feeds based on user preferences and behavior—have polarized public life. Vicario et al. find that misinformation on social media spreads among homogeneous and polarized groups. In January 2018, Facebook announced changes to its algorithm to prioritize trustworthy news, responding to ongoing public debate as to whether its algorithms promote junk content. Consequently, social polarization is a driver—just as much as it may be a result—of polarized social media news consumption patterns. In this study, we present a three-month study of junk news and political polarization among groups of US Twitter and Facebook users. In particular, we examine the distribution of posts and comments on public pages that contain links to junk news sources, across the political spectrum in the US. We then map the influence of central sources of junk political news and information that regularly publish content on hot button issues in the US. In particular, we consider patterns of interaction between accounts that have (i) shared junk news, (ii) and that have engaged with users who disseminate large amounts of misinformation about major political issues. SOCIAL NETWORK MAPPING Visualizing social network data is a powerful way of understanding how people share information and associate with one another. By using selected keywords, seed accounts, and known links to particular content, it is possible to construct large network visualizations. The underlying networks of