Ong Shaun, Koo John, Johannson Kerri A, Ryerson Christopher J, Goobie Gillian C
Division of Respirology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
Division of Respirology, Critical Care, and Sleep Medicine, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
ATS Sch. 2022 Nov 15;3(4):576-587. doi: 10.34197/ats-scholar.2022-0054OC. eCollection 2022 Dec.
Information regarding idiopathic pulmonary fibrosis (IPF) on the internet is often outdated, inaccurate, and potentially harmful. Twitter is a social media platform that allows users to post content in the form of "tweets".
We sought to assess the prevalence of inaccurate information regarding IPF on Twitter. We hypothesized that foundations and medical organizations would be the least likely to post inaccurate information and that inaccurate tweets would have higher user engagement.
All tweets posted between 2011 and 2019 were gathered using "snscrape" on Python 3.8 while searching for the phrase "idiopathic pulmonary fibrosis". Quantitative analysis was performed to describe trends in IPF-related tweet frequency over time. A subset of tweets made between 2018 and 2019 was screened for verifiable medical statements, which were then analyzed for accuracy compared with contemporary clinical practice guidelines, with descriptive statistics reported. Logistic regression was used to compare tweet accuracy and recommendation of nonindicated therapies across sources, with adjustment for tweet age and character count. Wilcoxon rank-sum tests were used to determine if user engagement (favorites, retweets, and replies) differed between accurate and inaccurate tweets.
A total of 16,787 tweets were identified between 2011 and 2019. Between 2018 and 2019, 4,861 tweets were included, of which 1,612 (33%) contained verifiable medical statements. Tweets from sources other than foundations or medical organizations were more likely to contain inaccurate information and to recommend nonindicated therapies in both unadjusted and adjusted analyses. News and media sources had the highest odds of communicating potentially harmful information in both adjusted (odds ratio [OR], 12.00; 95% confidence interval [CI], 5.87-27.16) and unadjusted (OR, 11.62; 95% CI, 5.70-26.21) analyses when compared with foundations and medical organizations. Tweets containing inaccurate information had significantly lower numbers of favorites and retweets ( < 0.001 for both).
Misinformation regarding IPF is present on Twitter and is more often presented by news and media sources. Medically inaccurate tweets displayed less user engagement than accurate tweets. This differs from findings on IPF-related information on YouTube and Facebook, which may reflect differences in both author and consumer qualities across social media platforms.
互联网上有关特发性肺纤维化(IPF)的信息往往过时、不准确且可能有害。推特是一个社交媒体平台,允许用户以“推文”的形式发布内容。
我们试图评估推特上有关IPF的不准确信息的流行程度。我们假设基金会和医学组织发布不准确信息的可能性最小,且不准确的推文会有更高的用户参与度。
使用Python 3.8上的“snscrape”收集2011年至2019年间发布的所有推文,同时搜索短语“特发性肺纤维化”。进行定量分析以描述IPF相关推文频率随时间的趋势。对2018年至2019年间发布的一部分推文进行可验证医学陈述筛选,然后将其与当代临床实践指南进行准确性分析,并报告描述性统计数据。使用逻辑回归比较不同来源推文的准确性和未指明疗法的推荐情况,并对推文年龄和字符数进行调整。使用Wilcoxon秩和检验确定准确和不准确推文之间的用户参与度(点赞、转发和回复)是否存在差异。
2011年至2019年间共识别出16787条推文。2018年至2019年间,纳入了4861条推文,其中1612条(33%)包含可验证的医学陈述。在未调整和调整分析中,来自基金会或医学组织以外来源的推文更有可能包含不准确信息并推荐未指明的疗法。与基金会和医学组织相比,新闻和媒体来源在调整分析(优势比[OR],12.00;95%置信区间[CI],5.87 - 27.16)和未调整分析(OR,11.62;95% CI,5.70 - 26.21)中传播潜在有害信息的几率最高。包含不准确信息的推文点赞和转发数量显著更低(两者均<0.001)。
推特上存在有关IPF的错误信息,且更多由新闻和媒体来源发布。医学上不准确的推文显示出的用户参与度低于准确的推文。这与YouTube和Facebook上有关IPF相关信息的研究结果不同,这可能反映了不同社交媒体平台上作者和用户特质的差异。