Peer Reviewed

Online mental health support communities, in which volunteer counselors provide accessible mental and emotional health support, have grown in recent years. Despite millions of people using these platforms, the clinical effectiveness of these communities on mental health symptoms remains unknown. Although volunteers receive some training on the therapeutic skills proven effective in face-to-face environments, such as active listening and motivational interviewing, it is unclear how the usage of these skills in an online context affects people's mental health. In our work, we collaborate with one of the largest online peer support platforms and use both natural language processing and machine learning techniques to examine how one-on-one support chats on the platform affect clients' depression and anxiety symptoms. We measure how characteristics of support-providers, such as their experience on the platform and use of therapeutic skills (e.g. affirmation, showing empathy), affect support-seekers' mental health changes. Based on a propensity-score matching analysis to approximate a random-assignment experiment, results shows that online peer support chats improve both depression and anxiety symptoms with a statistically significant but relatively small effect size. Additionally, support providers' techniques such as emphasizing the autonomy of the client lead to better mental health outcomes. However, we also found that the use of some behaviors, such as persuading and providing information, are associated with worsening of mental health symptoms. Our work provides key understanding for mental health care in the online setting and designing training systems for online support providers.

Media coverage has historically played an influential and often stigmatizing role in the public’s understanding of mental illness through harmful language and inaccurate portrayals of those with mental health issues. However, it is unknown how and to what extent media events may affect stigma in online discourse regarding mental health. In this study, we examine a highly publicized event – the celebrity defamation trial between Johnny Depp and Amber Heard – to uncover how stigmatizing and destigmatizing language on Twitter changed during and after the course of the trial. Using causal impact and language analysis methods, we provided a first look at how external events can lead to significantly greater levels of stigmatization and lower levels of destigmatization on Twitter towards not only particular disorders targeted in the coverage of external events but also general mental health discourse.

Online mental health communities (OMHCs) have emerged in recent years as an effective and accessible way to obtain peer support, filling crucial gaps of traditional mental health resources. However, the mechanisms for users to find relationships that fulfill their needs and capabilities in these communities are highly underdeveloped. Using a mixed-methods approach of user interviews and behavioral log analysis on 7Cups.com, we explore central challenges in finding adequate peer relationships in online support platforms and how algorithmic matching can alleviate many of these issues. We measure the impact of using qualities like gender and age in purposeful matching to improve member experiences, with especially salient results for users belonging to vulnerable populations. Lastly, we note key considerations for designing matching systems in the online mental health context, such as the necessity for better moderation to avoid potential harassment behaviors exacerbated by algorithmic matching. Our findings yield key insights into current user experiences in OMHCs as well as design implications for building matching systems in the future for OMHCs.