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

Onur Varol () and Ismail Uluturk ()
Additional contact information
Onur Varol: Northeastern University
Ismail Uluturk: University of South Florida

Journal of Computational Social Science, 2020, vol. 3, issue 1, No 5, 83-101

Abstract: 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.

Keywords: Social bots; Online journalism; Self-branding; Social media (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s42001-019-00056-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcsosc:v:3:y:2020:i:1:d:10.1007_s42001-019-00056-6

Ordering information: This journal article can be ordered from
http://www.springer. ... iences/journal/42001

DOI: 10.1007/s42001-019-00056-6

Access Statistics for this article

Journal of Computational Social Science is currently edited by Takashi Kamihigashi

More articles in Journal of Computational Social Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().