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Can tweets be used to detect problems early with scientific papers? A case study of three retracted COVID-19/SARS-CoV-2 papers

Author

Listed:
  • Robin Haunschild

    (Max Planck Institute for Solid State Research)

  • Lutz Bornmann

    (Administrative Headquarters)

Abstract

Methodological mistakes, data errors, and scientific misconduct are considered prevalent problems in science that are often difficult to detect. In this study, we explore the potential of using data from Twitter for discovering problems with publications. In this case study, we analyzed tweet texts of three retracted publications about COVID-19 (Coronavirus disease 2019)/SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) and their retraction notices. We did not find early warning signs in tweet texts regarding one publication, but we did find tweets that casted doubt on the validity of the two other publications shortly after their publication date. An extension of our current work might lead to an early warning system that makes the scientific community aware of problems with certain publications. Other sources, such as blogs or post-publication peer-review sites, could be included in such an early warning system. The methodology proposed in this case study should be validated using larger publication sets that also include a control group, i.e., publications that were not retracted.

Suggested Citation

  • Robin Haunschild & Lutz Bornmann, 2021. "Can tweets be used to detect problems early with scientific papers? A case study of three retracted COVID-19/SARS-CoV-2 papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 5181-5199, June.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:6:d:10.1007_s11192-021-03962-7
    DOI: 10.1007/s11192-021-03962-7
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    References listed on IDEAS

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    1. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    2. Qin Zhang & Juneman Abraham & Hui-Zhen Fu, 2020. "Collaboration and its influence on retraction based on retracted publications during 1978–2017," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 213-232, October.
    3. Lutz Bornmann & Robin Haunschild, 2018. "Allegation of scientific misconduct increases Twitter attention," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 1097-1100, May.
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    5. Sergio Copiello, 2020. "Other than detecting impact in advance, alternative metrics could act as early warning signs of retractions: tentative findings of a study into the papers retracted by PLoS ONE," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2449-2469, December.
    6. Julia Vainio & Kim Holmberg, 2017. "Highly tweeted science articles: who tweets them? An analysis of Twitter user profile descriptions," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 345-366, July.
    7. Mohammadamin Erfanmanesh & Jaime A. Teixeira da Silva, 2019. "Is the soundness-only quality control policy of open access mega journals linked to a higher rate of published errors?," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 917-923, August.
    8. Lutz Bornmann, 2013. "Research Misconduct—Definitions, Manifestations and Extent," Publications, MDPI, vol. 1(3), pages 1-12, October.
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    Cited by:

    1. Salim Moussa, 2022. "The propagation of error: retracted articles in marketing and their citations," Italian Journal of Marketing, Springer, vol. 2022(1), pages 11-36, March.
    2. Constantin Bürgi & Klaus Wohlrabe, 2022. "The influence of Covid-19 on publications in economics: bibliometric evidence from five working paper series," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5175-5189, September.

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