Shifting Polarization and Twitter News Influencers between two U.S. Presidential Elections

Abstract

Social media are decentralized, interactive, and transformative, empowering users to produce and spread information to influence others. This has changed the dynamics of political communication that were previously dominated by traditional corporate news media. Having hundreds of millions of tweets collected over the 2016 and 2020 U.S. presidential elections gave us a unique opportunity to measure the change in polarization and the diffusion of political information. We analyze the diffusion of political information among Twitter users and investigate the change of polarization between these elections and how this change affected the composition and polarization of influencers and their retweeters. We identify "influencers" by their ability to spread information and classify them into those affiliated with a media organization, a political organization, or unaffiliated. Most of the top influencers were affiliated with media organizations during both elections. We found a clear increase from 2016 to 2020 in polarization among influencers and among those whom they influence. Moreover, 75% of the top influencers in 2020 were not present in 2016, demonstrating that such status is difficult to retain. Between 2016 and 2020, 10%

Growing polarisation around climate change on social media

“…Clearly, individual views on climate action are more nuanced than anything that can be expressed by a single number. However, previous studies using the latent ideology have shown the utility of the method for detecting ideological views, and have shown a good correlation between the numerically derived ideology score, and third party ideology scores taken from surveys [18].…”

“…To extract ideological scores from the Twitter retweet network, we use the "latent ideology" method introduced in [18,23,24], which partitions accounts into a set of influencers, and a set of users who retweet those influencers (see Methods for a technical definition). As an analogy to the mathematics, we can think of the method as starting with an arbitrary ordering of influencers in one dimension.…”

“…The latent ideology estimation was developed in [23,24] and adapted for exploiting retweet interactions in [18]. Following [18], we infer ideological positions of Twitter users using correspondence analysis [35] (CA) and retweet interactions.…”

“…In the wider literature, there has been major growth in the study of online polarisation, misinformation, and the recently coined "infodemic" [13][14][15][16][17][18][19][20]. Historically, the dominant focus of these studies has been political polarisation in the United States and Europe [18,21], with a recent shift towards studying the online discussion around Covid-19 [15,19,20].…”

“…In the wider literature, there has been major growth in the study of online polarisation, misinformation, and the recently coined "infodemic" [13][14][15][16][17][18][19][20]. Historically, the dominant focus of these studies has been political polarisation in the United States and Europe [18,21], with a recent shift towards studying the online discussion around Covid-19 [15,19,20]. Three gaps stand out when assessing the literature on climate polarisation: (1) quantitative longitudinal studies which span a multi-year period and address the evolving nature of polarisation beyond a particular event, (2) studies analysing very large datasets (with the recent exception of [22]), and (3) studies where polarisation is measured quantitatively.…”