大规模的社交媒体分析揭示了与非医疗处方药使用相关的情绪。

Mohammed Ali Al-Garadi, Yuan-Chi Yang, Yuting Guo, Sangmi Kim, Jennifer S Love, Jeanmarie Perrone, Abeed Sarker
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引用次数: 2

摘要

背景:与非医疗处方药物使用(NMPDU)相关的行为和情绪及其原因并没有通过传统的工具如调查和保险理赔来很好地捕获。社交媒体上公开的nmpdu相关帖子可能会被用来不引人注目地大规模研究这些方面。方法:采用机器学习分类器检测Twitter上NMPDU的自我报告,提取相关用户的所有公开帖子。我们通过自然语言处理分析了来自87,718名Twitter用户的约1.37亿条帖子,包括表达的情绪、情绪、担忧以及NMPDU的可能原因。结果:与对照组(即从未报告过NMPDU的用户)相比,NMPDU组的用户表达了更多的负面情绪和更少的积极情绪,更多地关注家庭、过去和身体,更少地关注工作、休闲、家庭、金钱、宗教、健康和成就。NMPDU的帖子往往高度两极分化,表明潜在的情绪触发因素。性别分析表明,与男性相比,NMPDU组中的女性用户表达的内容更多与积极情绪、期待、悲伤、喜悦、对家人、朋友、家庭、健康和过去的担忧有关,而愤怒的内容较少。这一发现在不同的处方药类别(阿片类药物、苯二氮卓类药物、兴奋剂和多物质)中是一致的。结论:我们对大规模数据的分析表明,在Twitter上自我报告NMPDU的用户和不自我报告NMPDU的用户的帖子文本之间,以及在报告NMPDU的男性和女性之间,存在着实质性的差异。我们的发现可以丰富我们对NMPDU和相关人群的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use.

Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use.

Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use.

Background: The behaviors and emotions associated with and reasons for nonmedical prescription drug use (NMPDU) are not well-captured through traditional instruments such as surveys and insurance claims. Publicly available NMPDU-related posts on social media can potentially be leveraged to study these aspects unobtrusively and at scale.

Methods: We applied a machine learning classifier to detect self-reports of NMPDU on Twitter and extracted all public posts of the associated users. We analyzed approximately 137 million posts from 87,718 Twitter users in terms of expressed emotions, sentiments, concerns, and possible reasons for NMPDU via natural language processing.

Results: Users in the NMPDU group express more negative emotions and less positive emotions, more concerns about family, the past, and body, and less concerns related to work, leisure, home, money, religion, health, and achievement compared to a control group (i.e., users who never reported NMPDU). NMPDU posts tend to be highly polarized, indicating potential emotional triggers. Gender-specific analyses show that female users in the NMPDU group express more content related to positive emotions, anticipation, sadness, joy, concerns about family, friends, home, health, and the past, and less about anger than males. The findings are consistent across distinct prescription drug categories (opioids, benzodiazepines, stimulants, and polysubstance).

Conclusion: Our analyses of large-scale data show that substantial differences exist between the texts of the posts from users who self-report NMPDU on Twitter and those who do not, and between males and females who report NMPDU. Our findings can enrich our understanding of NMPDU and the population involved.

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