Reciprocal Communication and Political Deliberation on Twitter

Robert Ackland, Felix Gumbert, Ole Pütz (), Bryan Gertzel and Matthias Orlikowski
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Robert Ackland: School of Sociology, Australian National University, Canberra 2601, Australia
Felix Gumbert: Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany
Ole Pütz: Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany
Bryan Gertzel: School of Sociology, Australian National University, Canberra 2601, Australia
Matthias Orlikowski: Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany

Social Sciences, 2023, vol. 13, issue 1, 1-14

Abstract: Social media platforms such as Twitter/X are increasingly important for political communication but the empirical question as to whether such communication enhances democratic consensus building (the ideal of deliberative democracy) or instead contributes to societal polarisation via fostering of hate speech and “information disorders” such as echo chambers is worth exploring. Political deliberation involves reciprocal communication between users, but much of the recent research into politics on social media has focused on one-to-many communication, in particular the sharing and diffusion of information on Twitter via retweets. This paper presents a new approach to studying reciprocal political communication on Twitter, with a focus on extending network-analytic indicators of deliberation. We use the Twitter v2 API to collect a new dataset (#debatenight2020) of reciprocal communication on Twitter during the first debate of the 2020 US presidential election and show that a hashtag-based collection alone would have collected only 1% of the debate-related communication. Previous work into using social network analysis to measure deliberation has involved using discussion tree networks to quantify the extent of argumentation (maximum depth) and representation (maximum width); we extend these measures by explicitly incorporating reciprocal communication (via triad census) and the political partisanship of users (inferred via usage of partisan hashtags). Using these methods, we find evidence for reciprocal communication among partisan actors, but also point to a need for further research to understand what forms this communication takes.

Keywords: political communication; political deliberation; social network analysis; political partisanship; polarisation; Twitter; 2020 US presidential election (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2023
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