Brigham and Women's Hospital Boston MA USA.
Harvard T.H. Chan School of Public Health Boston MA USA.
J Am Heart Assoc. 2024 Apr 2;13(7):e032678. doi: 10.1161/JAHA.123.032678. Epub 2024 Mar 27.
Many individuals eligible for statin therapy decline treatment, often due to fear of adverse effects. Misinformation about statins is common and drives statin reluctance, but its prevalence on social media platforms, such as Twitter (now X) remains unclear. Social media bots are known to proliferate medical misinformation, but their involvement in statin-related discourse is unknown. This study examined temporal trends in volume, author type (bot or human), and sentiment of statin-related Twitter posts (tweets).
We analyzed original tweets with statin-related terms from 2010 to 2022 using a machine learning-derived classifier to determine the author's bot probability, natural language processing to assign each tweet a negative or positive sentiment, and manual qualitative analysis to identify statin skepticism in a random sample of all tweets and in highly influential tweets. We identified 1 155 735 original statin-related tweets. Bots produced 333 689 (28.9%), humans produced 699 876 (60.6%), and intermediate probability accounts produced 104 966 (9.1%). Over time, the proportion of bot tweets decreased from 47.8% to 11.3%, and human tweets increased from 43.6% to 79.8%. The proportion of negative-sentiment tweets increased from 27.8% to 43.4% for bots and 30.9% to 38.4% for humans. Manually coded statin skepticism increased from 8.0% to 19.0% for bots and from 26.0% to 40.0% for humans.
Over the past decade, humans have overtaken bots as generators of statin-related content on Twitter. Negative sentiment and statin skepticism have increased across all user types. Twitter may be an important forum to combat statin-related misinformation.
许多符合他汀类药物治疗条件的人拒绝治疗，这通常是由于担心不良反应。关于他汀类药物的错误信息很常见，导致人们不愿使用他汀类药物，但它在社交媒体平台（如 Twitter[现为 X]）上的流行程度尚不清楚。已知社交媒体机器人会传播医学错误信息，但它们在他汀类药物相关话语中的参与程度尚不清楚。本研究调查了他汀类药物相关推文（推文）的数量、作者类型（机器人或人类）和情绪的时间趋势。
我们使用机器学习衍生的分类器分析了 2010 年至 2022 年与他汀类药物相关的原始推文，以确定作者的机器人概率，使用自然语言处理为每条推文分配负面或正面情绪，并使用手动定性分析识别所有推文中的他汀类药物怀疑论者和高度有影响力的推文。我们确定了 1155735 条原始他汀类药物相关推文。机器人产生了 333689（28.9%），人类产生了 699876（60.6%），中间概率账户产生了 104966（9.1%）。随着时间的推移，机器人推文的比例从 47.8%下降到 11.3%，而人类推文的比例从 43.6%增加到 79.8%。机器人推文的负面情绪比例从 27.8%增加到 43.4%，人类推文的负面情绪比例从 30.9%增加到 38.4%。手动编码的他汀类药物怀疑论从机器人的 8.0%增加到 19.0%，从人类的 26.0%增加到 40.0%。
在过去十年中，人类在 Twitter 上发布他汀类药物相关内容的数量超过了机器人。所有用户类型的负面情绪和他汀类药物怀疑论都有所增加。Twitter 可能是对抗他汀类药物相关错误信息的重要论坛。
