Park Albert, Conway Mike, Chen Annie T
Department of Biomedical Informatics, School of Medicine University of Utah 421 Wakara Way Ste 140, Salt Lake City, UT 84108-3514, USA.
Department of Biomedical Informatics and Medical Education, School of Medicine University of Washington Box SLU-BIME 358047, 850 Republican St, Building C, Seattle, WA 98109-4714, USA.
Comput Human Behav. 2018 Jan;78:98-112. doi: 10.1016/j.chb.2017.09.001. Epub 2017 Sep 6.
Social media, including online health communities, have become popular platforms for individuals to discuss health challenges and exchange social support with others. These platforms can provide support for individuals who are concerned about social stigma and discrimination associated with their illness. Although mental health conditions can share similar symptoms and even co-occur, the extent to which discussion topics in online mental health communities are similar, different, or overlapping is unknown. Discovering the topical similarities and differences could potentially inform the design of related mental health communities and patient education programs. This study employs text mining, qualitative analysis, and visualization techniques to compare discussion topics in publicly accessible online mental health communities for three conditions: Anxiety, Depression and Post-Traumatic Stress Disorder.
First, online discussion content for the three conditions was collected from three Reddit communities (r/Anxiety, r/Depression, and r/PTSD). Second, content was pre-processed, and then clustered using the -means algorithm to identify themes that were commonly discussed by members. Third, we qualitatively examined the common themes to better understand them, as well as their similarities and differences. Fourth, we employed multiple visualization techniques to form a deeper understanding of the relationships among the identified themes for the three mental health conditions.
The three mental health communities shared four themes: sharing of positive emotion, gratitude for receiving emotional support, and sleep- and work-related issues. Depression clusters tended to focus on self-expressed contextual aspects of depression, whereas the Anxiety Disorders and Post-Traumatic Stress Disorder clusters addressed more treatment- and medication-related issues. Visualizations showed that discussion topics from the Anxiety Disorders and Post-Traumatic Stress Disorder subreddits shared more similarities to one another than to the depression subreddit.
We observed that the members of the three communities shared several overlapping concerns (i.e., sleep- and work-related problems) and discussion patterns (i.e., sharing of positive emotion and showing gratitude for receiving emotional support). We also highlighted that the discussions from the r/Anxiety and r/PTSD communities were more similar to one another than to discussions from the r/Depression community. The r/Anxiety and r/PTSD subreddit members are more likely to be individuals whose experiences with a condition are long-term, and who are interested in treatments and medications. The r/Depression subreddit members may be a comparatively diffuse group, many of whom are dealing with transient issues that cause depressed mood. The findings from this study could be used to inform the design of online mental health communities and patient education programs for these conditions. Moreover, we suggest that researchers employ multiple methods to fully understand the subtle differences when comparing similar discussions from online health communities.
社交媒体,包括在线健康社区,已成为个人讨论健康挑战并与他人交流社会支持的流行平台。这些平台可为那些担心与自身疾病相关的社会耻辱和歧视的个人提供支持。尽管心理健康状况可能有相似症状甚至同时出现,但在线心理健康社区中的讨论话题在多大程度上相似、不同或重叠尚不清楚。发现话题的异同可能会为相关心理健康社区和患者教育项目的设计提供参考。本研究采用文本挖掘、定性分析和可视化技术,比较三个心理健康状况(焦虑症、抑郁症和创伤后应激障碍)在公开可访问的在线心理健康社区中的讨论话题。
首先,从三个Reddit社区(r/焦虑症、r/抑郁症和r/创伤后应激障碍)收集这三种状况的在线讨论内容。其次,对内容进行预处理,然后使用K均值算法进行聚类,以识别成员们共同讨论的主题。第三,我们对这些共同主题进行定性研究,以更好地理解它们以及它们的异同。第四,我们采用多种可视化技术,以更深入地理解为这三种心理健康状况所识别出的主题之间的关系。
这三个心理健康社区共享四个主题:积极情绪的分享、对获得情感支持的感激以及与睡眠和工作相关的问题。抑郁症聚类倾向于关注抑郁症自我表达的情境方面,而焦虑症和创伤后应激障碍聚类则更多地涉及治疗和药物相关问题。可视化显示,焦虑症和创伤后应激障碍子版块的讨论话题彼此之间的相似性比与抑郁症子版块的相似性更高。
我们观察到这三个社区的成员有几个重叠的关注点(即与睡眠和工作相关的问题)和讨论模式(即积极情绪的分享以及对获得情感支持表示感激)。我们还强调,r/焦虑症和r/创伤后应激障碍社区的讨论彼此之间比与r/抑郁症社区的讨论更相似。r/焦虑症和r/创伤后应激障碍子版块的成员更可能是那些长期患有某种疾病且对治疗和药物感兴趣的个体。r/抑郁症子版块的成员可能是一个相对分散的群体,其中许多人正在处理导致情绪低落的短暂问题。本研究的结果可用于为这些状况的在线心理健康社区和患者教育项目的设计提供参考。此外,我们建议研究人员在比较在线健康社区的类似讨论时采用多种方法,以充分理解细微差异。