Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship

By Venkata Rama Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, Michael Mathioudakis. The Web Conference, 2018

453 citations recorded by the source corpus.

Echo chambers, i.e., situations where one is exposed only to opinions that agree with their own, are an increasing concern for the political discourse in many democratic countries. This paper studies the phenomenon of political echo chambers on social media. We identify the two components in the phenomenon: the opinion that is shared, and the »chamber» (i.e., the social network) that allows the opinion to »echo» (i.e., be re-shared in the network) -- and examine closely at how these two components interact. We define a production and consumption measure for social-media users, which captures the political leaning of the content shared and received by them. By comparing the two, we find that Twitter users are, to a large degree, exposed to political opinions that agree with their own. We also find that users who try to bridge the echo chambers, by sharing content with diverse leaning, have to pay a »price of bipartisanship» in terms of their network centrality and content appreciation. In addition, we study the role of »gatekeepers,» users who consume content with diverse leaning but produce partisan content (with a single-sided leaning), in the formation of echo chambers. Finally, we apply these findings to the task of predicting partisans and gatekeepers from social and content features. While partisan users turn out relatively easy to identify, gatekeepers prove to be more challenging.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/206c685ff6ade486f1062d1cabecc2d322fecd3b

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In Communication

1. Yulia Tsvetkov
2. Walter Quattrociocchi

In Sociology and Political Science

1. Yulia Tsvetkov
2. Walter Quattrociocchi
3. Shangbin Feng
4. Yuhan Liu
5. Chan Young Park

In Statistical and Nonlinear Physics

1. Yulia Tsvetkov
2. Walter Quattrociocchi
3. Michele Starnini

Citations in the corpus

• From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models (2023, background): “Digital and social media have become a major source of political news dissemination (Hermida et al., 2012; Kümpel et al., 2015; Hermida, 2016) with unprecedentedly high user engagement rates (Mustafaraj and Metaxas, 2011; Velasquez, 2012; Garimella et al., 2018).”
• The echo chamber effect on social media (2021, methodology): “For Twitter, we used tweets regarding three topics collected by Garimella et al. (16), namely Gun control, Obamacare, and abortion.”