TY - JOUR
T1 - You change the way you talk
T2 - Examining the network, toxicity and discourse of cross-platform users on Twitter and Parler during the 2020 US Presidential Election
AU - Park, Jaihyun
AU - Yang, Jung Hwan
AU - Tolbert, Amanda
AU - Bunsold, Katherine
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/4/28
Y1 - 2024/4/28
N2 - This study examines code-switching behaviours of cross-platform social media users specifically between Twitter and Parler during the 2020 US Presidential Election. Utilising social identity theory as a framework, we examine messages related to voter fraud by users who migrated from Twitter to Parler following Twitter bans. Our analysis covers 38,798 accounts active on both platforms, analysing 1.5 million tweets and more than 100,000 parleys. The key findings of the study are as follows: First, we discovered differing levels of network homophily between high degree centrality and low-degree centrality cross-platform users, illustrating how individuals with varying degrees of influence engage differently across platforms. Second, we observed higher toxicity levels in heterogeneous networks, which include both in-group and out-group members, compared with homogeneous networks that are primarily composed of in-group members. This suggests the level of toxicity in online spaces correlates with the level of group diversity. Third, we found that cross-platform users created distinctive discourse community with in-group and out-group members, indicating that content and discussions within these networks are influenced by the social identity dynamics of the users. Our study contributes to the current research in political communication and information science by proposing comparative user analyses across multiple social media platforms. Focusing on a critical period of platform transition during a contentious political event, our study offers insights into the dynamics of online communities and the shifting nature of political language used by social media users.
AB - This study examines code-switching behaviours of cross-platform social media users specifically between Twitter and Parler during the 2020 US Presidential Election. Utilising social identity theory as a framework, we examine messages related to voter fraud by users who migrated from Twitter to Parler following Twitter bans. Our analysis covers 38,798 accounts active on both platforms, analysing 1.5 million tweets and more than 100,000 parleys. The key findings of the study are as follows: First, we discovered differing levels of network homophily between high degree centrality and low-degree centrality cross-platform users, illustrating how individuals with varying degrees of influence engage differently across platforms. Second, we observed higher toxicity levels in heterogeneous networks, which include both in-group and out-group members, compared with homogeneous networks that are primarily composed of in-group members. This suggests the level of toxicity in online spaces correlates with the level of group diversity. Third, we found that cross-platform users created distinctive discourse community with in-group and out-group members, indicating that content and discussions within these networks are influenced by the social identity dynamics of the users. Our study contributes to the current research in political communication and information science by proposing comparative user analyses across multiple social media platforms. Focusing on a critical period of platform transition during a contentious political event, our study offers insights into the dynamics of online communities and the shifting nature of political language used by social media users.
KW - Alternative social media
KW - Parler
KW - Twitter
KW - code-switching
KW - cross-platform analysis
KW - network analysis
KW - political discourse
KW - social media
KW - toxicity
UR - http://www.scopus.com/inward/record.url?scp=85191685559&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85191685559&partnerID=8YFLogxK
U2 - 10.1177/01655515241238405
DO - 10.1177/01655515241238405
M3 - Article
AN - SCOPUS:85191685559
SN - 0165-5515
JO - Journal of Information Science
JF - Journal of Information Science
ER -