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

Abstract

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.

Online polarization and identity politics: An analysis of Facebook discourse on Muslim and LGBTQ+ communities in Finland

“…Recent research has broadened our understanding by providing in-depth insights into the ways social media influences public opinion and the polarization of politics, with a focus on diverse cultural and geographical contexts. These studies underscore the need to consider geographical heterogeneity and a more diverse range of social media platforms in future research (Flamino et al, 2023;Guess et al, 2023;Savchuk, 2023).…”

The Systemic Impact of Deplatforming on Social Media

“…We now measure whether the banned and matched cohorts are structurally segregated (or polarized) to assess whether the cohorts share the same, or different, audiences on Gettr. We measure segregation using the latent ideology, a well established method which constructs a synthetic ideological spectrum from user interactions on the platform [36][37][38] (see Methods). This measure orders the network of interactions between a set of influencer accounts (the banned and matched cohorts combined) and a set of accounts who interact with them (the nonverified cohort).…”

“…The unimodal ideology, and the central position of the matched and banned cohorts, indicates that these users share a common audience on Gettr; segregated audiences would appear as a multi-modal ideology distribution (see examples in [37,38])…”

“…To calculate the latent ideology on Gettr, we use the method developed in [36,37] and filtering procedures from [38]. The method uses a bipartite approach where it classifies Gettr accounts as influencers or regular users.…”

“…In order to assess the modality of the ideology distributions, we use Hartigan's diptest. This approach is used to identify polarized social media conversations and echo-chambers [37,38]. Hartigan's diptest compares a test distribution against a matched unimodal distribution to assess distribution modality [39].…”