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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.<\/jats:p>","DOI":"10.1007\/s11192-021-03962-7","type":"journal-article","created":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T07:02:49Z","timestamp":1619420569000},"page":"5181-5199","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Can tweets be used to detect problems early with scientific papers? 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