Detecting Tweets Containing Cannabidiol-Related COVID-19 Misinformation Using Transformer Language Models and Warning Letters From Food and Drug Administration: Content Analysis and Identification.

IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2023-01-01 DOI:10.2196/38390
Jason Turner, Mehmed Kantardzic, Rachel Vickers-Smith, Andrew G Brown
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引用次数: 0

Abstract

Background: COVID-19 has introduced yet another opportunity to web-based sellers of loosely regulated substances, such as cannabidiol (CBD), to promote sales under false pretenses of curing the disease. Therefore, it has become necessary to innovate ways to identify such instances of misinformation.

Objective: We sought to identify COVID-19 misinformation as it relates to the sales or promotion of CBD and used transformer-based language models to identify tweets semantically similar to quotes taken from known instances of misinformation. In this case, the known misinformation was the publicly available Warning Letters from Food and Drug Administration (FDA).

Methods: We collected tweets using CBD- and COVID-19-related terms. Using a previously trained model, we extracted the tweets indicating commercialization and sales of CBD and annotated those containing COVID-19 misinformation according to the FDA definitions. We encoded the collection of tweets and misinformation quotes into sentence vectors and then calculated the cosine similarity between each quote and each tweet. This allowed us to establish a threshold to identify tweets that were making false claims regarding CBD and COVID-19 while minimizing the instances of false positives.

Results: We demonstrated that by using quotes taken from Warning Letters issued by FDA to perpetrators of similar misinformation, we can identify semantically similar tweets that also contain misinformation. This was accomplished by identifying a cosine distance threshold between the sentence vectors of the Warning Letters and tweets.

Conclusions: This research shows that commercial CBD or COVID-19 misinformation can potentially be identified and curbed using transformer-based language models and known prior instances of misinformation. Our approach functions without the need for labeled data, potentially reducing the time at which misinformation can be identified. Our approach shows promise in that it is easily adapted to identify other forms of misinformation related to loosely regulated substances.

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使用转换语言模型和食品和药物管理局的警告信检测含有大麻二酚相关COVID-19错误信息的推文:内容分析和识别。
背景:COVID-19为大麻二酚(CBD)等监管宽松物质的网络卖家提供了另一个机会,以治疗疾病为幌子促进销售。因此,有必要创新识别此类错误信息的方法。目的:我们试图识别与CBD销售或推广相关的COVID-19错误信息,并使用基于转换器的语言模型来识别语义上类似于已知错误信息实例引用的推文。在这种情况下,已知的错误信息是食品和药物管理局(FDA)公开发布的警告信。方法:我们收集使用CBD和covid -19相关术语的推文。使用先前训练过的模型,我们提取了表明CBD商业化和销售的推文,并根据FDA的定义注释了那些包含COVID-19错误信息的推文。我们将推文和错误信息引用的集合编码成句子向量,然后计算每条引用和每条推文之间的余弦相似度。这使我们能够建立一个阈值,以识别关于CBD和COVID-19的虚假声明的推文,同时最大限度地减少误报的情况。结果:我们证明,通过使用FDA向类似错误信息的肇事者发出的警告信中的引用,我们可以识别语义上相似的推文,也包含错误信息。这是通过识别警告信和推文的句子向量之间的余弦距离阈值来实现的。结论:本研究表明,使用基于转换器的语言模型和已知的先前错误信息实例,可以识别和遏制商业CBD或COVID-19错误信息。我们的方法在不需要标记数据的情况下发挥作用,潜在地减少了识别错误信息的时间。我们的方法显示出希望,因为它很容易适应于识别与松散管制物质相关的其他形式的错误信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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