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Classification and analysis of PubPeer comments: How a web journal club is used

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  • José Luis Ortega

Abstract

This study explores the use of PubPeer by the scholarly community, to understand the issues discussed in an online journal club, the disciplines most commented on, and the characteristics of the most prolific users. A sample of 39,985 posts about 24,779 publications were extracted from PubPeer in 2019 and 2020. These comments were divided into seven categories according to their degree of seriousness (Positive review, Critical review, Lack of information, Honest errors, Methodological flaws, Publishing fraud, and Manipulation). The results show that more than two‐thirds of comments are posted to report some type of misconduct, mainly about image manipulation. These comments generate most discussion and take longer to be posted. By discipline, Health Sciences and Life Sciences are the most discussed research areas. The results also reveal “super commenters,” users who access the platform to systematically review publications. The study ends by discussing how various disciplines use the site for different purposes.

Suggested Citation

  • José Luis Ortega, 2022. "Classification and analysis of PubPeer comments: How a web journal club is used," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(5), pages 655-670, May.
  • Handle: RePEc:bla:jinfst:v:73:y:2022:i:5:p:655-670
    DOI: 10.1002/asi.24568
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    1. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2012. "Sentiment strength detection for the social web," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 163-173, January.
    2. S. P. J. M. Horbach & W. Halffman, 2019. "The ability of different peer review procedures to flag problematic publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 339-373, January.
    3. José Luis Ortega, 2017. "Are peer-review activities related to reviewer bibliometric performance? A scientometric analysis of Publons," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 947-962, August.
    4. Ed Yong & Heidi Ledford & Richard Van Noorden, 2013. "Research ethics: 3 ways to blow the whistle," Nature, Nature, vol. 503(7477), pages 454-457, November.
    5. Elizabeth L. Pier & Markus Brauer & Amarette Filut & Anna Kaatz & Joshua Raclaw & Mitchell J. Nathan & Cecilia E. Ford & Molly Carnes, 2018. "Low agreement among reviewers evaluating the same NIH grant applications," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(12), pages 2952-2957, March.
    6. Charles W. Fox, 2017. "Difficulty of recruiting reviewers predicts review scores and editorial decisions at six journals of ecology and evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 465-477, October.
    7. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2012. "Sentiment strength detection for the social web," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 163-173, January.
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