Open Access Journal

ISSN: 2183-2439

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Exploring Political Journalism Homophily on Twitter: A Comparative Analysis of US and UK Elections in 2016 and 2017

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Abstract:  The tendency of political journalists to form insular groups or packs, chasing the same angles and quoting the same sources, is a well-documented issue in journalism studies and has long been criticized for its role in groupthink and homogenous news coverage. This groupthink attracted renewed criticism after the unexpected victory of Republican candidate Donald Trump in the 2016 US presidential election as the campaign coverage had indicated a likely win by the Democratic candidate Hillary Clinton. This pattern was repeated in the 2017 UK election when the Conservative party lost their majority after a campaign in which the news coverage had pointed to an overall Tory victory. Such groupthink is often attributed to homophily, the tendency of individuals to interact with those most like them, and while homophily in the legacy media system is well-studied, there is little research around homophily in the hybrid media system, even as social media platforms like Twitter facilitate the development—and analysis—of virtual political journalism packs. This study, which compares Twitter interactions among US and UK political reporters in the 2016 and 2017 national elections, shows that political journalists are overwhelmingly more likely to use Twitter to interact with other journalists, particularly political journalists, and that their offline tendencies to form homogenous networks have transferred online. There are some exceptions around factors such as gender, news organizations and types of news organization—and important distinctions between types of interactions—but overall the study provides evidence of sustained homophily as journalists continue to normalize Twitter.

Keywords:  elections; groupthink; homophily; political journalism; Twitter, UK; US



© Kelly Fincham. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 license (, which permits any use, distribution, and reproduction of the work without further permission provided the original author(s) and source are credited.