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首页> 外文期刊>JMIR public health and surveillance. >Identifying Adverse Effects of HIV Drug Treatment and Associated Sentiments Using Twitter
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Identifying Adverse Effects of HIV Drug Treatment and Associated Sentiments Using Twitter

机译：使用Twitter识别HIV药物治疗和相关情绪的不良影响

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Background: Social media platforms are increasingly seen as a source of data on a wide range of health issues. Twitter is of particular interest for public health surveillance because of its public nature. However, the very public nature of social media platforms such as Twitter may act as a barrier to public health surveillance, as people may be reluctant to publicly disclose information about their health. This is of particular concern in the context of diseases that are associated with a certain degree of stigma, such as HIV/AIDS. Objective: The objective of the study is to assess whether adverse effects of HIV drug treatment and associated sentiments can be determined using publicly available data from social media. Methods: We describe a combined approach of machine learning and crowdsourced human assessment to identify adverse effects of HIV drug treatment solely on individual reports posted publicly on Twitter. Starting from a large dataset of 40 million tweets collected over three years, we identify a very small subset (1642; 0.004%) of individual reports describing personal experiences with HIV drug treatment. Results: Despite the small size of the extracted final dataset, the summary representation of adverse effects attributed to specific drugs, or drug combinations, accurately captures well-recognized toxicities. In addition, the data allowed us to discriminate across specific drug compounds, to identify preferred drugs over time, and to capture novel events such as the availability of preexposure prophylaxis. Conclusions: The effect of limited data sharing due to the public nature of the data can be partially offset by the large number of people sharing data in the first place, an observation that may play a key role in digital epidemiology in general.
机译：背景：社交媒体平台越来越被视为有关各种健康问题的数据来源。 Twitter具有公共性质，因此对于公共卫生监视特别感兴趣。但是，社交媒体平台（如Twitter）的非常公共性可能会成为公共健康监控的障碍，因为人们可能不愿公开披露有关其健康的信息。在与一定程度的污名相关的疾病（例如艾滋病毒/艾滋病）的情况下，这尤其令人担忧。目的：该研究的目的是评估是否可以使用来自社交媒体的公开数据确定艾滋病毒药物治疗和相关情绪的不良影响。方法：我们描述了一种机器学习和众包人类评估相结合的方法，以仅根据在Twitter上公开发布的个人报告来确定HIV药物治疗的不良影响。从过去三年收集的4000万条推文的大型数据集开始，我们确定了描述艾滋病毒药物治疗个人经历的个人报告中的一小部分（1642; 0.004％）。结果：尽管提取的最终数据集很小，但归因于特定药物或药物组合的不良反应的摘要表示，准确地捕获了公认的毒性。此外，这些数据使我们能够区分特定的药物化合物，随着时间的推移识别出首选药物，并捕获新的事件，例如进行暴露前预防的可能性。结论：由于数据的公共性，有限的数据共享的影响可以首先被大​​量人共享数据所部分抵消，这一发现通常在数字流行病学中可能起关键作用。

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