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Many researchers studying online communities seek to make them better. However, beyond a small set of widely-held values, such as combating misinformation and abuse, determining what `better’ means can be challenging, as community members may disagree, values may be in conflict, and different communities may have differing preferences as a whole.In this work, we present the first study that elicits values directly from members across a diverse set of communities.We survey 212 members of 627 unique subreddits and ask them to describe their values for their communities in their own words. Through iterative categorization of 1,481 responses, we develop and validate a comprehensive taxonomy of community values, consisting of 29 subcategories within nine top-level categories enabling principled, quantitative study of community values by researchers. Using our taxonomy, we reframe existing research problems, such as managing influxes of new members, as tensions between different values, and we identify understudied values, such as those regarding content quality and community size. We call for greater attention to vulnerable community members' values, and we make our codebook public for use in future research. 

This paper presents a mixed-methods study of app-based motorcycle taxis in Thailand to explore the social dynamics of rideshare drivers and their exercised autonomy both through social pressure and a hostile work environment. As motorcycle taxis are open-air vehicles, drivers can be exposed to prolonged air pollution and other weather events, potentially impacting their health. In an initial quantitative study of server-side rideshare logs, we unexpectedly found that drivers do not exercise the autonomy provided by their rideshare platform to avoid air pollution events. This prompted a follow-on investigation through semi-structured interviews of both drivers and passengers in three provinces to explore why these drivers fail to experience the autonomy promised by gig-work in this context and elucidated further examples this lack of autonomy experienced by drivers. Our study sheds light on the social context that may constrain a driver's agency, including financial pressures, weather conditions, conflicts with local taxi organizations, and a false perception that drivers need to work around the ride assignment algorithm to avoid being blacklisted. We find that when leveraging app-based rideshare opportunities, drivers simultaneously perceive increased flexibility in their work hours and a lack of agency to prioritize their health and safety. We conclude with a discussion on potential interventions aimed at mitigating the forces preventing drivers from exercising their autonomy. 

Abstract Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack of objective outcomes and fidelity metrics. AI technologies and specifically Natural Language Processing (NLP) have emerged as tools to study mental health interventions (MHI) at the level of their constituent conversations. However, NLP’s potential to address clinical and research challenges remains unclear. We therefore conducted a pre-registered systematic review of NLP-MHI studies using PRISMA guidelines (osf.io/s52jh) to evaluate their models, clinical applications, and to identify biases and gaps. Candidate studies (n = 19,756), including peer-reviewed AI conference manuscripts, were collected up to January 2023 through PubMed, PsycINFO, Scopus, Google Scholar, and ArXiv. A total of 102 articles were included to investigate their computational characteristics (NLP algorithms, audio features, machine learning pipelines, outcome metrics), clinical characteristics (clinical ground truths, study samples, clinical focus), and limitations. Results indicate a rapid growth of NLP MHI studies since 2019, characterized by increased sample sizes and use of large language models. Digital health platforms were the largest providers of MHI data. Ground truth for supervised learning models was based on clinician ratings (n = 31), patient self-report (n = 29) and annotations by raters (n = 26). Text-based features contributed more to model accuracy than audio markers. Patients’ clinical presentation (n = 34), response to intervention (n = 11), intervention monitoring (n = 20), providers’ characteristics (n = 12), relational dynamics (n = 14), and data preparation (n = 4) were commonly investigated clinical categories. Limitations of reviewed studies included lack of linguistic diversity, limited reproducibility, and population bias. A research framework is developed and validated (NLPxMHI) to assist computational and clinical researchers in addressing the remaining gaps in applying NLP to MHI, with the goal of improving clinical utility, data access, and fairness. 

Multiverse analysis—a paradigm for statistical analysis that considers all combinations of reasonable analysis choices in parallel—promises to improve transparency and reproducibility. Although recent tools help analysts specify multiverse analyses, they remain difficult to use in practice. In this work, we identify debugging as a key barrier due to the latency from running analyses to detecting bugs and the scale of metadata processing needed to diagnose a bug. To address these challenges, we prototype a command-line interface tool, Multiverse Debugger, which helps diagnose bugs in the multiverse and propagate fixes. In a qualitative lab study (n=13), we use Multiverse Debugger as a probe to develop a model of debugging workflows and identify specific challenges, including difficulty in understanding the multiverse’s composition. We conclude with design implications for future multiverse analysis authoring systems. 

Abstract An unhealthy diet is a major risk factor for chronic diseases including cardiovascular disease, type 2 diabetes, and cancer 1–4 . Limited access to healthy food options may contribute to unhealthy diets 5,6 . Studying diets is challenging, typically restricted to small sample sizes, single locations, and non-uniform design across studies, and has led to mixed results on the impact of the food environment 7–23 . Here we leverage smartphones to track diet health, operationalized through the self-reported consumption of fresh fruits and vegetables, fast food and soda, as well as body-mass index status in a country-wide observational study of 1,164,926 U.S. participants (MyFitnessPal app users) and 2.3 billion food entries to study the independent contributions of fast food and grocery store access, income and education to diet health outcomes. This study constitutes the largest nationwide study examining the relationship between the food environment and diet to date. We find that higher access to grocery stores, lower access to fast food, higher income and college education are independently associated with higher consumption of fresh fruits and vegetables, lower consumption of fast food and soda, and lower likelihood of being affected by overweight and obesity. However, these associations vary significantly across zip codes with predominantly Black, Hispanic or white populations. For instance, high grocery store access has a significantly larger association with higher fruit and vegetable consumption in zip codes with predominantly Hispanic populations (7.4% difference) and Black populations (10.2% difference) in contrast to zip codes with predominantly white populations (1.7% difference). Policy targeted at improving food access, income and education may increase healthy eating, but intervention allocation may need to be optimized for specific subpopulations and locations. 

Abstract The COVID-19 pandemic has stimulated important changes in online information access as digital engagement became necessary to meet the demand for health, economic, and educational resources. Our analysis of 55 billion everyday web search interactions during the pandemic across 25,150 US ZIP codes reveals that the extent to which different communities of internet users enlist digital resources varies based on socioeconomic and environmental factors. For example, we find that ZIP codes with lower income intensified their access to health information to a smaller extent than ZIP codes with higher income. We show that ZIP codes with higher proportions of Black or Hispanic residents intensified their access to unemployment resources to a greater extent, while revealing patterns of unemployment site visits unseen by the claims data. Such differences frame important questions on the relationship between differential information search behaviors and the downstream real-world implications on more and less advantaged populations.