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Accuracy of health‐related information regarding COVID‐19 on Twitter during a global pandemic
Author(s) -
Swetland Sarah B.,
Rothrock Ava N.,
Andris Halle,
Davis Bennett,
Nguyen Linh,
Davis Phil,
Rothrock Steven G.
Publication year - 2021
Publication title -
world medical and health policy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.326
H-Index - 11
ISSN - 1948-4682
DOI - 10.1002/wmh3.468
Subject(s) - pandemic , government (linguistics) , covid-19 , social media , confidence interval , public health , medicine , computer science , disease , world wide web , nursing , pathology , linguistics , philosophy , infectious disease (medical specialty)
This study was performed to analyze the accuracy of health‐related information on Twitter during the coronavirus disease 2019 (COVID‐19) pandemic. Authors queried Twitter on three dates for information regarding COVID‐19 and five terms (cure, emergency or emergency room, prevent or prevention, treat or treatments, vitamins or supplements) assessing the first 25 results with health‐related information. Tweets were authoritative if written by governments, hospitals, or physicians. Two physicians assessed each tweet for accuracy. Metrics were compared between accurate and inaccurate tweets using χ 2 analysis and Mann–Whitney U . A total of 25.4% of tweets were inaccurate. Accurate tweets were more likely written by Twitter authenticated authors (49.8% vs. 20.9%, 28.9% difference, 95% confidence interval [CI]: 17.7–38.2) with accurate tweet authors having more followers (19,491 vs. 7346; 3446 difference, 95% CI: 234–14,054) versus inaccurate tweet authors. Likes, retweets, tweet length, botometer scores, writing grade level, and rank order did not differ between accurate and inaccurate tweets. We found 1/4 of health‐related COVID‐19 tweets inaccurate indicating that the public should not rely on COVID‐19 health information written on Twitter. Ideally, improved government regulatory authority, public/private industry oversight, independent fact‐checking, and artificial intelligence algorithms are needed to ensure inaccurate information on Twitter is removed.