Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections

Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter's news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers—users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels. A growing number of studies have documented increasing political polarization in the USA that is deeper than at any time since the American Civil War, with partisan division among elites and affective polarization among voters. Diffusion of political information is difficult to track with traditional data, but social media offers new opportunities to study diffusion and misinformation over networks. We use a vast amount of Twitter data from the 2016 and 2020 US presidential elections, enriched with political bias classifications, to study diffusion dynamics of political content through news media. We discovered that, proportionally, the fraction of tweets in the fake news and extremely biased news categories decreased or stayed the same on Twitter. We also focus on analysing news media influencers, defined as users with the greatest ability to broadly propagate news media information over social media, and analyse changes in their influence, composition and the types of news media they are disseminating between the two elections. We find that the proportion of top influencers affiliated with news media organizations decreased in 2020, while the proportion of those affiliated with political organizations increased. We also quantify and compare the levels of polarization between 2016 and 2020. Our polarization analysis reveals an increase in echo chamber behaviour between 2016 and 2020 resulting from Twitter users’ tendency to be less likely to disseminate information or interact with users on the other side of the political spectrum. This analysis also suggests that new influencers from 2020 are more polarized than the influencers who persisted from the 2016 US presidential election. We believe these results establish a foundation for future work by providing observations on trends and patterns arising in Twitter’s political landscape in news media.