Don’t quote me: reverse identification of research participants in social media studies

• Published in: NPJ digital medicine 2018-08, Vol.1 (1), p.30-30, Article 30
• Main Authors: Ayers, John W., Caputi, Theodore L., Nebeker, Camille, Dredze, Mark
• Format: Article
• Language: English
• Subjects: 692/308/174; 692/308/575; Biomedicine; Biotechnology; Brief Communication; Digital technology; Health informatics; Medicine; Medicine & Public Health; Social networks
• ISSN: 2398-6352; 2398-6352
• DOI: 10.1038/s41746-018-0036-2

We investigated if participants in social media surveillance studies could be reverse identified by reviewing all articles published on PubMed in 2015 or 2016 with the words “Twitter” and either “read,” “coded,” or “content” in the title or abstract. Seventy-two percent (95% CI: 63–80) of articles quoted at least one participant’s tweet and searching for the quoted content led to the participant 84% (95% CI: 74–91) of the time. Twenty-one percent (95% CI: 13–29) of articles disclosed a participant’s Twitter username thereby making the participant immediately identifiable. Only one article reported obtaining consent to disclose identifying information and institutional review board (IRB) involvement was mentioned in only 40% (95% CI: 31–50) of articles, of which 17% (95% CI: 10–25) received IRB-approval and 23% (95% CI:16–32) were deemed exempt. Biomedical publications are routinely including identifiable information by quoting tweets or revealing usernames which, in turn, violates ICMJE ethical standards governing scientific ethics, even though said content is scientifically unnecessary. We propose that authors convey aggregate findings without revealing participants’ identities, editors refuse to publish reports that reveal a participant’s identity, and IRBs attend to these privacy issues when reviewing studies involving social media data. These strategies together will ensure participants are protected going forward.