Data Mining Lab, Department of Computer Science and Engineering, University of Louisville, Louisville, KY, United States.
Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY, United States.
J Med Internet Res. 2021 Dec 20;23(12):e27307. doi: 10.2196/27307.
In the absence of official clinical trial information, data from social networks can be used by public health and medical researchers to assess public claims about loosely regulated substances such as cannabidiol (CBD). For example, this can be achieved by comparing the medical conditions targeted by those selling CBD against the medical conditions patients commonly treat with CBD.
The objective of this study was to provide a framework for public health and medical researchers to use for identifying and analyzing the consumption and marketing of unregulated substances. Specifically, we examined CBD, which is a substance that is often presented to the public as medication despite complete evidence of efficacy and safety.
We collected 567,850 tweets by searching Twitter with the Tweepy Python package using the terms "CBD" and "cannabidiol." We trained two binary text classifiers to create two corpora of 167,755 personal use and 143,322 commercial/sales tweets. Using medical, standard, and slang dictionaries, we identified and compared the most frequently occurring medical conditions, symptoms, side effects, body parts, and other substances referenced in both corpora. In addition, to assess popular claims about the efficacy of CBD as a medical treatment circulating on Twitter, we performed sentiment analysis via the VADER (Valence Aware Dictionary for Sentiment Reasoning) model on the personal CBD tweets.
We found references to medically relevant terms that were unique to either personal or commercial CBD tweet classes, as well as medically relevant terms that were common to both classes. When we calculated the average sentiment scores for both personal and commercial CBD tweets referencing at least one of 17 medical conditions/symptoms terms, an overall positive sentiment was observed in both personal and commercial CBD tweets. We observed instances of negative sentiment conveyed in personal CBD tweets referencing autism, whereas CBD was also marketed multiple times as a treatment for autism within commercial tweets.
Our proposed framework provides a tool for public health and medical researchers to analyze the consumption and marketing of unregulated substances on social networks. Our analysis showed that most users of CBD are satisfied with it in regard to the condition that it is being advertised for, with the exception of autism.
在缺乏官方临床试验信息的情况下，公共卫生和医学研究人员可以利用社交网络上的数据来评估公众对大麻二酚 (CBD) 等监管宽松物质的说法。例如，可以通过比较销售 CBD 的人的医疗条件与患者通常用 CBD 治疗的医疗条件来实现。
本研究旨在为公共卫生和医学研究人员提供一个框架，用于识别和分析不受监管物质的消费和营销情况。具体来说，我们研究了 CBD，尽管 CBD 在疗效和安全性方面完全没有证据，但它经常被公众作为药物使用。
我们使用 Tweepy Python 包通过搜索 Twitter 用术语“CBD”和“大麻二酚”收集了 567850 条推文。我们训练了两个二进制文本分类器来创建两个语料库，其中 167755 条是个人使用语料库，143322 条是商业/销售语料库。我们使用医学、标准和俚语词典来识别和比较两个语料库中最常出现的医疗条件、症状、副作用、身体部位和其他物质。此外，为了评估在 Twitter 上流传的 CBD 作为一种医疗治疗方法的有效性的流行说法，我们通过 VADER（用于情感推理的感知字典）模型对个人 CBD 推文进行了情感分析。
我们发现了在个人或商业 CBD 推文类别中独特的与医学相关的术语，以及在两个类别中都常见的与医学相关的术语。当我们计算至少引用 17 种医疗条件/症状术语的个人和商业 CBD 推文的平均情感得分时，我们观察到个人和商业 CBD 推文中都存在整体积极的情绪。我们观察到个人 CBD 推文中引用自闭症时传达了负面情绪的例子，而在商业推文中 CBD 也被多次宣传为自闭症的治疗方法。
我们提出的框架为公共卫生和医学研究人员提供了一个分析社交网络上不受监管物质消费和营销的工具。我们的分析表明，CBD 的大多数使用者对其宣传的适应症都很满意，除了自闭症。
