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.

Highlights

  • The 2016 election of President Donald Trump in the US sent shock waves through the American political and media establishment

  • While pack journalism is well studied in the legacy media system, homophily or “virtual pack journalism,” has not received the same attention (Kiernan, 2014) and, perhaps, more importantly, while several studies have explored social media homophily among individuals, there is a lack of research into social media homophily among elite groups such as political journalists, despite journalism’s critical role in setting the news agenda (Wihbey, 2018)

  • The study begins with an overview of normalization, homophily, Twitter journalism, retweets and replies, and explores those interactions from a total of 202 UK and US political journalists through a quantitative analysis of the retweets and replies produced in the run-up to the 2016 and 2017 US and UK national elections before turning to the discussion and conclusion

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Summary

Introduction

The 2016 election of President Donald Trump in the US sent shock waves through the American political and media establishment. The same questions arose in Britain some months later as the Conservative party lost their ruling majority to the surprise of much of the political media who were described as falling victim to “confirmation bias” in their reporting (Enten & Silver, 2017) Such homogenous reporting is a hallmark of “pack journalism” where political journalists are more likely to aim for unanimity than dissent in their work processes and in doing so build echo chambers or filter bubbles, albeit unwittingly, by quoting from the same sources and focusing on the same issues and profoundly shaping news coverage as a result (Matusitz & Breen, 2012; Mourão, 2015; Usher, Holcomb, & Littman, 2018). The study begins with an overview of normalization, homophily, Twitter journalism, retweets and replies, and explores those interactions from a total of 202 UK and US political journalists through a quantitative analysis of the retweets and replies produced in the run-up to the 2016 and 2017 US and UK national elections before turning to the discussion and conclusion

Literature Review
Research Questions
Data and Methods
RQ1: Homophily in Retweets
RQ2: Homophily in Replies
RQ3: Shared Characteristics in Retweets
RQ4: Shared Characteristics in Replies
Discussion
Limitations
Full Text
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