This study investigates the types of misinformation spread on Twitter that evokes scientific authority or evidence when making false claims about the antimalarial drug hydroxychloroquine as a treatment for COVID-19. Specifically, we examined tweets generated after former U.S. President Donald Trump retweeted misinformation about the drug using an unsupervised machine learning approach called the biterm topic model that is used to cluster tweets into misinformation topics based on textual similarity. The top 10 tweets from each topic cluster were content coded for three types of misinformation categories related to scientific authority: medical endorsements of hydroxychloroquine, scientific information used to support hydroxychloroquine’s use, and a comparison group that included scientific evidence opposing hydroxychloroquine’s use. Results show a much higher volume of tweets featuring medical endorsements and use of supportive scientific information compared to accurate and updated scientific evidence, that misinformation-related tweets propagated for a longer time frame, and the majority of hydroxychloroquine Twitter discourse expressed positive views about the drug. Metadata from Twitter accounts found that prominent users within misinformation discourse were more likely to have media or political affiliation and explicitly expressed support for President Trump. Conversely, prominent accounts within the scientific opposition discourse primarily consisted of medical doctors or scientists but had far less influence in the Twitter discourse. Implications of these findings and connections to related social media research are discussed, as well as cognitive mechanisms for understanding susceptibility to misinformation and strategies to combat misinformation spread via online platforms.

这项研究调查了在Twitter上传播的错误信息的类型，这些错误信息在对抗疟药羟氯喹作为COVID-19的治疗提出错误主张时引起了科学权威或证据。具体来说，我们研究了前美国总统唐纳德·特朗普（Donald Trump）使用称为双项主题模型的无监督机器学习方法转发有关该药物的错误信息后产生的推文，该方法用于根据文本相似性将推文聚类为错误信息主题。每个主题组的前10条推文均针对与科学权威有关的三种错误信息类别进行了编码：羟乙基喹啉的医学认可，用于支持羟氯喹使用的科学信息以及一个比较组，其中包括反对使用羟氯喹的科学证据。结果显示，与准确和最新的科学证据相比，带有医学认可和支持科学信息的推文数量要多得多，与错误信息有关的推文传播的时间更长，并且大多数羟氯喹Twitter言论都对该药物持积极态度。 。来自Twitter帐户的元数据发现，误导性言论中的重要用户更有可能具有媒体或政治背景，并明确表示支持特朗普总统。相反，科学反对派话语中的突出叙述主要由医生或科学家组成，但在Twitter话语中的影响要小得多。讨论了这些发现的含义以及与相关社交媒体研究的联系，