The impact of culture on how people express distress in online support communities is increasingly a topic of interest within Computer Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI). In the United States, distinct cultures have emerged from each of the two dominant political parties, forming a primary lens by which people navigate online and offline worlds. We examine whether partisan culture may play a role in how U.S. Republican and Democrat users of online mental health support communities express distress. We present a large-scale observational study of 2,184,356 posts from 8,916 statistically matched Republican, Democrat, and unaffiliated online support community members. We utilize methods from causal inference to statistically match partisan users along covariates that correspond with demographic attributes and platform use, in order to create comparable cohorts for analysis. We then leverage methods from natural language processing to understand how partisan expressions of distress compare between these sets of closely matched opposing partisans, and between closely matched partisans and typical support community members. Our data spans January 2013 to December 2022, a period of both rising political polarization and mental health concerns. We find that partisan culture does play into expressions of distress, underscoring the importance of considering partisan cultural differences in the design of online support community platforms.

People experiencing severe distress increasingly use Large Language Model (LLM) chatbots as mental health support tools. Discussions on social media have described how engagements were lifesaving for some, but evidence suggests that general-purpose LLM chatbots also have notable risks that could endanger the welfare of users if not designed responsibly. In this study, we investigate the lived experiences of people who have used LLM chatbots for mental health support. We build on interviews with 21 individuals from globally diverse backgrounds to analyze how users create unique support roles for their chatbots, fill in gaps in everyday care, and navigate associated cultural limitations when seeking support from chatbots. We ground our analysis in psychotherapy literature around effective support, and introduce the concept of therapeutic alignment, or aligning AI with therapeutic values for mental health contexts. Our study offers recommendations for how designers can approach the ethical and effective use of LLM chatbots and other AI mental health support tools in mental health care.

Psychedelic-assisted therapy has shown significant promise in alleviating treatment-resistant mental illness, prompting excitement among people with lived experience of mental illness. The emerging collective perception of psychedelics as tools for mental health has been dubbed the Pollan Effect. We investigate whether the Pollan Effect carries to online community discussions concerning psilocybin-containing mushrooms (PCMs). Through a matched computational analysis of 676,875 longform Reddit posts describing PCM use spanning a decade, we provide evidence of the Pollan Effect in terms of increased health discourse around PCMs following two inception points—release of a book and subsequent documentary on PCMs. We then introduce the notion of a Pollan shift, which we witness through increased collective sharing of emotional and social experiences following the two inception points. Our findings offer insights into how online discourse could be representative of emerging social movements around new psychiatric treatments, and the role of platforms in sensemaking and research.

The global COVID-19 pandemic has spurred on new collaborations across borders, and emphasized the importance of supporting wellbeing in the workplace, whether that workplace is hybrid, remote, or in-person. Work in CSCW, HCI, and organizational psychology has explored how people come to understand their wellbeing at work, and the role of identity, culture, and organizational factors in that process. In this study, we build on this past research and explore the importance of these factors when designing tools that support worker wellbeing for location-independent teams. We ask the question: how did organizational, cultural, and individual factors influence how workers understood their workplace wellbeing needs during the move to remote work? To investigate this question, we conduct a large scale linguistic analysis of 13,265 diary entries collected between 2020 - 2022, and complement it with in-depth interviews with 26 global employees, exploring intersections between technology, context, and wellbeing needs. We utilize this data to analyze the broader human infrastructure supporting hybrid and remote work, demonstrating how ideas around wellbeing are influenced by the (often technology-mediated) environment around both information and essential workers, and power differentials within it. Building on our findings, we provide recommendations for how technology design can better support more diverse and inclusive forms of worker wellbeing.

In the mental health domain, patient engagement is key to designing human-centered technologies. CSCW and HCI researchers have delved into various facets of collaboration in AI research; however, previous research neglects the individuals who both produce the data and will be most impacted by the resulting technologies, such as patients. This study examines how interdisciplinary researchers and mental health patients who donate their data for AI research collaborate and how we can improve human-centeredness in mental health AI research. We interviewed patient participants, AI researchers, and clinical researchers in a federally funded mental health AI research project. We used the concept of boundary objects to understand stakeholder collaboration. Our findings reveal that the social media data provided by patient participants functioned as boundary objects that facilitated stakeholder collaboration. Although the collaboration appeared to be successful, we argue that building consensus, or understanding each other's perspectives, can improve the human-centeredness of mental health AI research. Based on the findings, we provide suggestions for human-centered mental health AI research, working with data donors as domain experts, making invisible work visible, and privacy implications.

People experiencing mental illness are often forced into a system in which their chances of finding relief are largely determined by institutions that evaluate whether their distress deserves treatment. These governing institutions can be offline, such as the American healthcare system, and can also be online, such as online social platforms. As work in Human-Computer Interaction (HCI) and Computer Supported Cooperative Work (CSCW) frames technology-mediated support as one method to fill structural gaps in care, in this study, we ask the question: how do online and offline institutions influence how people in resource-scarce areas understand and express their distress online? We situate our work in U.S. Mental Health Professional Shortage Areas (MHPSAs), or areas in which there are too few mental health professionals to meet expected needs. We use an analysis of illness narratives to answer this question, conducting a large scale linguistic analysis of social media posts to understand broader trends in expressions of distress online. We then build on these analyses via in-depth interviews with 18 participants with lived experience of mental illness, analyzing the role of online and offline institutions in how participants express distress online. Through our findings, we argue that a consideration of institutions is crucial in designing effective technology-mediated support, and discuss the implications of considering institutions in mental health support for platform designers.

The field of digital mental health is making strides in the application of technology to broaden access to care. We critically examine how these technology-mediated forms of care might amplify historical injustices, and erase minoritized experiences and expressions of mental distress and illness. We draw on decolonial thought and critiques of identity-based algorithmic bias to analyze the underlying power relations impacting digital mental health technologies today, and envision new pathways towards a decolonial digital mental health. We argue that a decolonial digital mental health is one that centers lived experience over rigid classification, is conscious of structural factors that influence mental wellbeing, and is fundamentally designed to deter the creation of power differentials that prevent people from having agency over their care. Stemming from this vision, we make recommendations for how researchers and designers can support more equitable futures for people experiencing mental distress and illness.

Individuals in distress adopt varied pathways in pursuit of care that aligns with their individual needs. Prior work has established that the first resource an individual leverages can influence later care and recovery, but less is understood about how the design of a point of care might interact with subsequent pathways to care. We investigate how the design of the Indian mental health helpline system interacts with complex sociocultural factors to marginalize caller needs. We draw on interviews with 18 helpline stakeholders, including individuals who have engaged with helplines in the past, shedding light on how they navigate both technological and structural barriers in pursuit of relief. Finally, we use a design justice framework rooted in Amartya Sen’s conceptualization of realization-focused justice to discuss implications and present recommendations towards the design of technology-mediated points of mental health support.

Significant research in HCI and beyond has sought to understand end-user needs in formal and informal technology-mediated mental health support (TMMHS) systems. However, little work has been done to understand the experiences and needs of the individuals who power or support these systems, particularly in the Global South. We present a qualitative study of one of the most accessible forms of mental health care in India — helplines. Through in-depth interviews conducted with 12 helpline volunteers, we research the human infrastructure responsible for the functioning of helplines. We foreground the often invisible labor involved in erecting and maintaining the institutional, interpersonal, and individual boundaries that are critical to realizing the goals of these helplines. Finally, we discuss the implications of our research for future work examining human infrastructures, particularly in mental health settings, and for the design of future TMMHS systems that deliver on-demand care to diverse, underserved, and stigmatized populations.

Messaging is a common mode of communication, with conversations written informally between individuals. Interpreting emotional affect from messaging data can lead to a powerful form of reflection or act as a support for clinical therapy. Existing analysis techniques for social media commonly use LIWC and VADER for automated sentiment estimation. We correlate LIWC, VADER, and ratings from human reviewers with affect scores from 25 participants. We explore differences in how and when each technique is successful. Results show that human review does better than VADER, the best automated technique, when humans are judging positive affect ($r_s=0.45$ correlation when confident, $r_s=0.30$ overall). Surprisingly, human reviewers only do slightly better than VADER when judging negative affect ($r_s=0.38$ correlation when confident, $r_s=0.29$ overall). Compared to prior literature, VADER correlates more closely with PANAS scores for private messaging than public social media. Our results indicate that while any technique that serves as a proxy for PANAS scores has moderate correlation at best, there are some areas to improve the automated techniques by better considering context and timing in conversations.

Clinical psychology literature indicates that reframing ir- rational thoughts can help bring positive cognitive change to those suffering from mental distress. Through data from an online mental health forum, we study how these cognitive processes play out in peer-to-peer conversations. Acknowledging the complexity of measuring cognitive change, we first provide an operational definition of a "moment of change" based on sentiment change in online conversations. Using this definition, we propose a predictive model that can identify whether a conversation thread or a post is associated with a moment of cognitive change. Consistent with psychological literature, we find that markers of language associated with sentiment and and affect are the most predictive. Further, cultural differences play an important role: predictive models trained on one country generalize poorly to others. To understand how a moment of change happens, we build a model that explicitly tracks topic and associated sentiment in a forum thread.

Mental illness is rapidly gaining recognition as a serious global challenge. Recent human-computer interaction (HCI) research has investigated mental health as a domain of concern, but is yet to venture into the Global South, where the problem exhibits a more complex, intersectional nature. In this paper, we review work on mental health in the Global South and present a case for HCI for Development (HCI4D) to look at mental health-both because it is an inarguably important area of concern in itself, and also because it impacts the efficacy of HCI4D interventions in other domains. We consider the role of cultural and resource-based interactions towards accessibility challenges and continuing stigma around mental health. We also identify participants' mental health as a constant consideration for HCI4D and present best practices for measuring and incorporating it. As an example, we demonstrate how both the process and the lens of aspirations-based design, a recently proposed approach for HCI4D research and design, may benefit from the consideration of mental health concerns. Our paper thus recommends a path forward for considering mental health in HCI4D, potentially leading to new research directions in addition to enriching existing ones.

Online mental health forums facilitate supportive relationships between peers that transcend national and cultural boundaries. While past work in medical anthropology indicates a central role of cultural identity in how individuals frame their mental well-being and distress, little research has been done to investigate the role of culture in seeking and providing mental health support on online forums. Using data from two mental health forums, we analyze cross-cultural differences in mental health expression between people from different countries. We characterize these differences along three dimensions--identity, language use, and support behavior. Through comparing usage of the platform by individuals from three Asian countries with their counterparts from primarily Western countries, we find that individuals from these less-represented countries mention their own country more often when expressing distress, use fewer clinical language terms, and are more likely to provide support to people from the same country as them, as expected from past work on mental health in these countries. Contrary to past work, however, we find that the use of clinical mental health language is not affected over time by interacting with others in an international forum. While these findings are useful for understanding the role of culture in mental health support, they also have practical design implications for online forums. We find that the three dimensions of cultural differences we analyze are correlated with receiving effective support, and make design recommendations that can improve quality of support for the people in the minority on these forums.