Background: Social media networks provide an abundance of diverse information that can be leveraged for data-driven applications across various social and physical sciences. One opportunity to utilize such data exists in the public health domain, where data collection is often constrained by organizational funding and limited user adoption. Furthermore, the efficacy of health interventions is often based on self-reported data, which are not always reliable. Health-promotion strategies for communities facing multiple vulnerabilities, such as men who have sex with men, can benefit from an automated system that not only determines health behavior risk but also suggests appropriate intervention targets. Objective: This study aims to determine the value of leveraging social media messages to identify health risk behavior for men who have sex with men. Methods: The Gay Social Networking Analysis Program was created as a preliminary framework for intelligent web-based health-promotion intervention. The program consisted of a data collection system that automatically gathered social media data, health questionnaires, and clinical results for sexually transmitted diseases and drug tests across 51 participants over 3 months. Machine learning techniques were utilized to assess the relationship between social media messages and participants' offline sexual health and substance use biological outcomes. The F1 score, a weighted average of precision and recall, was used to evaluate each algorithm. Natural language processing techniques were employed to create health behavior risk scores from participant messages. Results: Offline HIV, amphetamine, and methamphetamine use were correctly identified using only social media data, with machine learning models obtaining F1 scores of 82.6%, 85.9%, and 85.3%, respectively. Additionally, constructed risk scores were found to be reasonably comparable to risk scores adapted from the Center for Disease Control. Conclusions: To our knowledge, our study is the first empirical evaluation of a social media–based public health intervention framework for men who have sex with men. We found that social media data were correlated with offline sexual health and substance use, verified through biological testing. The proof of concept and initial results validate that public health interventions can indeed use social media–based systems to successfully determine offline health risk behaviors. The findings demonstrate the promise of deploying a social media–based just-in-time adaptive intervention to target substance use and HIV risk behavior.

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中文翻译：

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利用社交媒体活动和机器学习进行 HIV 和药物滥用风险评估：开发和验证研究

背景：社交媒体网络提供了丰富多样的信息，可用于跨各种社会和物理科学的数据驱动应用程序。利用此类数据的一个机会存在于公共卫生领域，其中数据收集通常受到组织资金和有限用户采用的限制。此外，健康干预的有效性通常基于自我报告的数据，这些数据并不总是可靠的。面向面临多重脆弱性的社区（例如男男性行为者）的健康促进策略可以受益于自动化系统，该系统不仅可以确定健康行为风险，还可以提出适当的干预目标。目的：本研究旨在确定利用社交媒体信息识别男男性行为者健康风险行为的价值。方法：同性恋社交网络分析计划是作为基于网络的智能健康促进干预的初步框架而创建的。该计划由一个数据收集系统组成，该系统在 3 个月内自动收集了 51 名参与者的社交媒体数据、健康问卷以及性传播疾病和药物测试的临床结果。机器学习技术被用来评估社交媒体信息与参与者线下性健康和物质使用生物学结果之间的关系。F1 分数是精确率和召回率的加权平均值，用于评估每个算法。使用自然语言处理技术从参与者消息中创建健康行为风险评分。结果：离线 HIV、苯丙胺、仅使用社交媒体数据就可以正确识别和甲基苯丙胺的使用，机器学习模型获得的 F1 分数分别为 82.6%、85.9% 和 85.3%。此外，发现构建的风险评分与根据疾病控制中心改编的风险评分相当。结论：据我们所知，我们的研究是针对男男性行为者的基于社交媒体的公共卫生干预框架的首次实证评估。我们发现社交媒体数据与线下性健康和物质使用相关，并通过生物测试进行了验证。概念验证和初步结果证实，公共卫生干预确实可以使用基于社交媒体的系统来成功确定离线健康风险行为。

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