Background: Antidepressants are known to show heterogeneous effects across individuals and conditions, posing challenges to understanding their efficacy in mental health treatment. Social media platforms enable individuals to share their day-to-day concerns with others and thereby can function as unobtrusive, large-scale, and naturalistic data sources to study the longitudinal behavior of individuals taking antidepressants. Objective: We aim to understand the side effects of antidepressants from naturalistic expressions of individuals on social media. Methods: On a large-scale Twitter data set of individuals who self-reported using antidepressants, a quasi-experimental study using unsupervised language analysis was conducted to extract keywords that distinguish individuals who improved and who did not improve following the use of antidepressants. The net data set consists of over 8 million Twitter posts made by over 300,000 users in a 4-year period between January 1, 2014, and February 15, 2018. Results: Five major side effects of antidepressants were studied: sleep, weight, eating, pain, and sexual issues. Social media language revealed keywords related to these side effects. In particular, antidepressants were found to show a spectrum of effects from decrease to increase in each of these side effects. Conclusions: This work enhances the understanding of the side effects of antidepressants by identifying distinct linguistic markers in the longitudinal social media data of individuals showing the most and least improvement following the self-reported intake of antidepressants. One implication of this work concerns the potential of social media data as an effective means to support digital pharmacovigilance and digital therapeutics. These results can inform clinicians in tailoring their discussion and assessment of side effects and inform patients about what to potentially expect and what may or may not be within the realm of normal aftereffects of antidepressants.

中文翻译：

---

了解抗抑郁药的副作用：社交媒体数据的大规模纵向研究

背景：已知抗抑郁药在个体和状况之间表现出不同的作用，对理解其在心理健康治疗中的功效提出了挑战。社交媒体平台使个人可以与他人分享他们的日常关注，从而可以充当不引人注目的，大规模且自然主义的数据源，以研究服用抗抑郁药的个人的纵向行为。目的：我们旨在从社交媒体上的个人自然主义表达中了解抗抑郁药的副作用。方法：在使用抗抑郁药自我报告的大型Twitter数据集上，进行了使用无监督语言分析的准实验研究，以提取关键字，以区分使用抗抑郁药后有改善和无改善的个人。净数据集包括在2014年1月1日至2018年2月15日的4年中，超过300,000位用户发布的800万条Twitter帖子。结果：研究了抗抑郁药的五个主要副作用：睡眠，体重，饮食，疼痛和性问题。社交媒体语言揭示了与这些副作用相关的关键字。特别地，发现抗抑郁药在这些副作用的每一个中显示出从减少到增加的一系列作用。结论：这项工作通过在个体自我报告服用抗抑郁药后显示出最大和最小改善的个体的纵向社交媒体数据中识别出不同的语言标记，从而增强了对抗抑郁药副作用的认识。这项工作的一个含义涉及社交媒体数据作为支持数字药物警戒和数字疗法的有效手段的潜力。这些结果可以指导临床医生进行有关副作用的讨论和评估，并使患者了解抗抑郁药正常后效范围内可能预期的情况以及可能或可能不存在的情况。