Polarization dynamics: a study of individuals shifting between political communities on social media
Federico Albanese, Esteban Feuerstein, Pablo Balenzuela

TL;DR
This study investigates how individuals on social media switch between political communities, analyzing their interaction patterns and content bias during the 2020 US election to understand polarization dynamics.
Contribution
It introduces an analysis of users who diverge from typical community patterns, highlighting their unique interaction features and content biases in a polarized environment.
Findings
Switching users have distinct topological interaction features.
They exhibit different sentiment biases towards Donald Trump.
Community switching correlates with specific network centrality measures.
Abstract
Individuals engaging on social media often tend to establish online communities where interactions predominantly occur among like-minded peers. While considerable efforts have been devoted to studying and delineating these communities, there has been limited attention directed towards individuals who diverge from these patterns. In this study, we examine the community structure of re-post networks within the context of a polarized political environment at two different times. We specifically identify individuals who consistently switch between opposing communities and analyze the key features that distinguish them. Our investigation focuses on two crucial aspects of these users: the topological properties of their interactions and the political bias in the content of their posts. Our analysis is based on a dataset comprising 2 million tweets related to US President Donald Trump, coupled…
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Taxonomy
TopicsSocial Media and Politics
