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

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...

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Vydané v:	Scientometrics Ročník 126; číslo 6; s. 5181 - 5199
Hlavní autori:	Haunschild, Robin, Bornmann, Lutz
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.01.2021 Springer Nature B.V
Predmet:	Blogs Case studies Computer mediated communication Computer Science Coronaviruses COVID-19 Documents Early warning systems Emergency communications systems Errors Information Storage and Retrieval Library Science Misconduct Notices Professional misconduct Scientific community Scientific papers Severe acute respiratory syndrome Severe acute respiratory syndrome coronavirus 2 Texts Viral diseases COVID-19 Scientometrics Twitter SARS-CoV-2 Retracted papers Altmetrics
ISSN:	0138-9130, 1588-2861
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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.
AbstractList	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. 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.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.
Author	Haunschild, Robin Bornmann, Lutz
Author_xml	– sequence: 1 givenname: Robin orcidid: 0000-0001-7025-7256 surname: Haunschild fullname: Haunschild, Robin email: R.Haunschild@fkf.mpg.de organization: Max Planck Institute for Solid State Research – sequence: 2 givenname: Lutz surname: Bornmann fullname: Bornmann, Lutz organization: Science Policy and Strategy Department, Max Planck Society, Administrative Headquarters
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Keywords	COVID-19 Scientometrics Twitter SARS-CoV-2 Retracted papers Altmetrics
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Snippet	Methodological mistakes, data errors, and scientific misconduct are considered prevalent problems in science that are often difficult to detect. In this study,...
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SubjectTerms	Blogs Case studies Computer mediated communication Computer Science Coronaviruses COVID-19 Documents Early warning systems Emergency communications systems Errors Information Storage and Retrieval Library Science Misconduct Notices Professional misconduct Scientific community Scientific papers Severe acute respiratory syndrome Severe acute respiratory syndrome coronavirus 2 Texts Viral diseases
Title	Can tweets be used to detect problems early with scientific papers? A case study of three retracted COVID-19/SARS-CoV-2 papers
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