Network Amplification of Politicized Information and Misinformation about COVID-19 by Conservative Media and Partisan Influencers on Twitter

ABSTRACT

Social media amplification is both a mechanism to attract public attention and a process of information diffusion shaped by the online social network structure. This study focuses on amplification by elites on social media and examines the extent to which traditional media and emerging partisan influencers engage in “network amplification.” Defined as like-minded elites sharing similar or/and mutual messages, network amplification highlights the interrelation and interaction between elite messages in the network communication environment of social media. This is a phenomenon worth investigating because network amplification’s resulting message repetition and reinforcement can multiply the overall effectiveness of elite messaging. Using network sampling and spectral clustering, we collected 358,707 accounts that followed 2,069,311 accounts on Twitter and detected nine distinct networks of traditional media and emerging partisan influencers. We then examined their 3,540,629 tweets related to the COVID-19 pandemic. Results show that 1) conservative media and influencers engaged in network amplification of politicized information and misinformation significantly more than liberal media and influencers did; 2) conservative influencers exhibited a stronger tendency to retweet and align their messages with conservative media than liberal influencers did regarding liberal media; and 3) traditional media partially drove partisan influencers’ amplification. The implications of network amplification for partisan asymmetry, misinformation, and public opinion are discussed.

KEYWORDS:

• Amplification
• social networks
• social media
• partisan asymmetry
• news media

Acknowledgments

We would like to thank our anonymous reviewers, editors, and Karl Rohe at the University of Wisconsin-Madison for helpful comments and suggestions.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Data Availability

The data underlying this article are available upon request.

Supplementary Material

Supplemental data for this article can be accessed on the publisher’s website at https://doi.org/10.1080/10584609.2022.2113844.

Additional information

Notes on contributors

Yini Zhang

Yini Zhang is an assistant professor in the Department of Communication at the University at Buffalo. She studies social media and political communication, focusing on social media public attention and opinion and using computational methods. She received her PhD from the University of Wisconsin-Madison.

Fan Chen

Fan Chen is currently a data scientist at Google. He received his PhD in statistics from the University of Wisconsin-Madison.

Josephine Lukito

Josephine Lukito is an Assistant Professor at the University of Texas at Austin. She studies global political communication, disinformation, and journalism language using mixed-methods sociolinguistic analysis and computational communication methods. She received her PhD from the University of Wisconsin-Madison.