Saha Koustuv, Torous John, Kiciman Emre, De Choudhury Munmun
Georgia Institute of Technology, Atlanta, GA, United States.
Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.
JMIR Ment Health. 2021 Mar 19;8(3):e26589. doi: 10.2196/26589.
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.
We aim to understand the side effects of antidepressants from naturalistic expressions of individuals on social media.
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.
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.
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.
已知抗抑郁药在个体和不同病情中会表现出异质性效果,这给理解其在心理健康治疗中的疗效带来了挑战。社交媒体平台使个体能够与他人分享日常关切,从而可以作为研究服用抗抑郁药个体纵向行为的不引人注意、大规模且自然主义的数据源。
我们旨在从个体在社交媒体上的自然表达中了解抗抑郁药的副作用。
在一个自我报告使用抗抑郁药的个体的大规模推特数据集上,进行了一项使用无监督语言分析的准实验研究,以提取区分使用抗抑郁药后病情改善和未改善个体的关键词。该净数据集由2014年1月1日至2018年2月15日这4年期间30多万用户发布的800多万条推特帖子组成。
研究了抗抑郁药的五种主要副作用:睡眠、体重、饮食、疼痛和性方面的问题。社交媒体语言揭示了与这些副作用相关的关键词。特别是,发现抗抑郁药在这些副作用中的每一种上都显示出从减少到增加的一系列效果。
这项工作通过在个体的纵向社交媒体数据中识别出显示自我报告服用抗抑郁药后改善最多和最少的个体的不同语言标记,增强了对抗抑郁药副作用的理解。这项工作的一个意义在于社交媒体数据作为支持数字药物警戒和数字治疗的有效手段的潜力。这些结果可以为临床医生调整他们对副作用的讨论和评估提供信息,并告知患者抗抑郁药可能出现的预期情况以及哪些可能或不可能在正常后遗症范围内。