Exploring the Fragmentation of the Representation of Data-Driven Journalism in the Twittersphere: A Network Analytics Approach

Xinzhi Zhang · Jeffrey C. F. Ho

19 citations0/0 files storedSocial Science Computer Review

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Media Studies and Communication Journalism and Media Studies scientometrics and bibliometrics research

Abstract

As an interdisciplinary field, data-driven journalism integrates the intellectual origins of investigative journalism, computer-assisted reporting, and the emerging paradigm of computational social science. Studies of news production have revealed, however, that news professionals are reinforcing existing power structures via an interpretive community, where homophily-evoked social interactions—even in the social media context—create echo chambers and discussion fragmentation. Is the representation of data-driven journalism in the electronic public sphere breaking boundaries among people from different domains or does it resemble the existing power structure? This study adopts a network analytics approach and constructs a representational network among actors who joined the public discussion of data-driven journalism in the Twittersphere—the co-retweeted network—such that two accounts are connected if their tweets are retweeted by the same user. Public tweets containing search queries related to data-driven journalism published from February 2017 to February 2018 were collected with Twitter real-time streaming application programming interface (API). A co-retweeted network with 1,148 accounts was derived from verified accounts’ retweeting posts. Results found that several communities emerged, and news organizations, nongovernmental and nonprofit professional organizations, and academic institutions were in the crucial positions of the network. The exponential random graph models (ERGMs) based on this network revealed the extent to which gender, geographical location, and institutional type of the users were associated with the tie-formation. This study documents the major actors who are discussing the subject of data-driven journalism and raises critical reflections toward the interdisciplinary collaboration in the production of public knowledge.

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2009Computational Social Science 1998Image and logic: a material culture of microphysics 1993Journalists as interpretive communities 2014Clarifying Journalism’s Quantitative Turn 2012SOCIAL MEDIA AS BEAT 2018Hashtag activism and message frames among social movement organizations: Semantic network analysis and thematic analysis of Twitter during the #MeToo movement 1973Precision Journalism: A Reporter's Introduction to Social Science Methods 2007On “Having Been There”: “Eyewitnessing” as a Journalistic Key Word

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