Swetland Sarah B, Rothrock Ava N, Andris Halle, Davis Bennett, Nguyen Linh, Davis Phil, Rothrock Steven G
University of Florida Gainesville Florida USA.
Duke University Durham North Carolina USA.
World Med Health Policy. 2021 Sep;13(3):503-517. doi: 10.1002/wmh3.468. Epub 2021 Jul 29.
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 analysis and Mann-Whitney . 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.
本研究旨在分析2019年冠状病毒病(COVID-19)大流行期间推特上与健康相关信息的准确性。作者在三个日期查询推特,获取有关COVID-19的信息以及五个术语(治愈、急诊或急诊室、预防、治疗、维生素或补充剂),评估前25条与健康相关的结果。如果推文由政府、医院或医生撰写,则具有权威性。两名医生评估每条推文的准确性。使用分析和曼-惠特尼检验比较准确和不准确推文之间的指标。共有25.4%的推文不准确。准确的推文更有可能由推特认证作者撰写(49.8%对20.9%,差异28.9%,95%置信区间[CI]:17.7 - 38.2),准确推文的作者比不准确推文的作者拥有更多粉丝(19491对7346;差异3446,95%CI:234 - 14054)。准确和不准确推文在点赞数、转发数、推文长度、机器人检测分数、写作年级水平和排名顺序方面没有差异。我们发现四分之一与COVID-19健康相关的推文不准确,这表明公众不应依赖推特上撰写的COVID-19健康信息。理想情况下,需要加强政府监管机构、公共/私营行业监督、独立事实核查和人工智能算法,以确保推特上的不准确信息被删除。