Mapping the Political Landscape on Social Media Using Bibliometrics: A Longitudinal Co-Word Analysis on Twitter and Facebook Publications Published Between 2012 and 2021

Abstract

Topics such as disinformation, misinformation, political polarization, and populism are frequently discussed in the social media literature. The purpose of this article is to investigate how the political emphasis on social media has evolved in the academic publications published in the last decade. Thus, using co-word analysis of the social science articles published between 2012 and 2021, which discuss politically about Facebook or Twitter ( N = 3389), this article investigates whether certain major and unexpected political events—such as Donald Trump’s presidential victory and the Brexit referendum—have influenced in any way the knowledge field related to social media publications. Thus, the 2017–2021 map brings new and popular words, such as “Covid-19,” which is associated in similar clusters with words such as disinformation, fake news, and infodemic. Furthermore, the emergence of the word “Russia” places it in a common cluster with words such as bots, elections, and agenda-setting. Also, the Twitter map, unlike the Facebook one, brings a particular emphasis on Donald Trump’s activity, which appears in clusters that are similar to topics that brought him popularity on Twitter, such as: meme, migration, and refugees. Such bibliometric associations should increase policymakers’ attention to the potential use of social media as a political tool, along with designing the solutions to limit such intrusions into future political events.

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Stored only when both papers are in the corpus — 14 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.