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Journalists on Twitter: self-branding, audiences, and involvement of bots

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  • Onur Varol

    (Northeastern University)

  • Ismail Uluturk

    (University of South Florida)

Abstract

Spread of news and misinformation on social networks have been a topic of extensive study in the recent years. There are concerns about the possibility of ongoing information operations, which has lead to studies on a wide scope including the truthfulness of content and the participation of social bots in the process. Studying how online entities of journalists is embedded in the Twitter network is crucial for understanding the core of this problem, since they hold a valuable broadcast platform in informing the public. In this work, we collected over 290,000 accounts that self-identify as a journalist or a reporter and analyzed their professional and follower networks on the platform. Twitter follower composition of journalists reflect their potential audiences and who disseminates their messages further on the network. It is essential for a journalist to reach a broad, organic readership as opposed to a following of bots and bot-assisted accounts. We looked at the followers of journalists for an analysis of the composition and evolution of their audiences, particularly looking out for social bot involvement. We found the trends for verified and non-verified accounts to be opposite of each other; among verified accounts bot follower tend to target more popular ones, whereas unverified accounts have a higher fraction of bot followers early on when they have fewer followers, possibly indicating attempts at boosting apparent popularity artificially. Outcomes of this research emphasize the importance of editorial oversight and that the prestige of journalists should not be confused with their apparent popularity online.

Suggested Citation

  • Onur Varol & Ismail Uluturk, 2020. "Journalists on Twitter: self-branding, audiences, and involvement of bots," Journal of Computational Social Science, Springer, vol. 3(1), pages 83-101, April.
    • Handle: RePEc:spr:jcsosc:v:3:y:2020:i:1:d:10.1007_s42001-019-00056-6
    DOI: 10.1007/s42001-019-00056-6
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    References listed on IDEAS

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    1. Chengcheng Shao & Giovanni Luca Ciampaglia & Onur Varol & Kai-Cheng Yang & Alessandro Flammini & Filippo Menczer, 2018. "The spread of low-credibility content by social bots," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
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    Cited by:

    1. Kai-Cheng Yang & Emilio Ferrara & Filippo Menczer, 2022. "Botometer 101: social bot practicum for computational social scientists," Journal of Computational Social Science, Springer, vol. 5(2), pages 1511-1528, November.

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