RESUMEN
BACKGROUND: Online communities provide support for individuals looking for help with suicidal ideation and crisis. As community data are increasingly used to devise machine learning models to infer who might be at risk, there have been limited efforts to identify both risk and protective factors in web-based posts. These annotations can enrich and augment computational assessment approaches to identify appropriate intervention points, which are useful to public health professionals and suicide prevention researchers. OBJECTIVE: This qualitative study aims to develop a valid and reliable annotation scheme for evaluating risk and protective factors for suicidal ideation in posts in suicide crisis forums. METHODS: We designed a valid, reliable, and clinically grounded process for identifying risk and protective markers in social media data. This scheme draws on prior work on construct validity and the social sciences of measurement. We then applied the scheme to annotate 200 posts from r/SuicideWatch-a Reddit community focused on suicide crisis. RESULTS: We documented our results on producing an annotation scheme that is consistent with leading public health information coding schemes for suicide and advances attention to protective factors. Our study showed high internal validity, and we have presented results that indicate that our approach is consistent with findings from prior work. CONCLUSIONS: Our work formalizes a framework that incorporates construct validity into the development of annotation schemes for suicide risk on social media. This study furthers the understanding of risk and protective factors expressed in social media data. This may help public health programming to prevent suicide and computational social science research and investigations that rely on the quality of labels for downstream machine learning tasks.
RESUMEN
Social media is now being used to model mental well-being, and for understanding health outcomes. Computer scientists are now using quantitative techniques to predict the presence of specific mental disorders and symptomatology, such as depression, suicidality, and anxiety. This research promises great benefits to monitoring efforts, diagnostics, and intervention design for these mental health statuses. Yet, there is no standardized process for evaluating the validity of this research and the methods adopted in the design of these studies. We conduct a systematic literature review of the state-of-the-art in predicting mental health status using social media data, focusing on characteristics of the study design, methods, and research design. We find 75 studies in this area published between 2013 and 2018. Our results outline the methods of data annotation for mental health status, data collection and quality management, pre-processing and feature selection, and model selection and verification. Despite growing interest in this field, we identify concerning trends around construct validity, and a lack of reflection in the methods used to operationalize and identify mental health status. We provide some recommendations to address these challenges, including a list of proposed reporting standards for publications and collaboration opportunities in this interdisciplinary space.
RESUMEN
Online communities can promote illness recovery and improve well-being in the cases of many kinds of illnesses. However, for challenging mental health condition like anorexia, social media harbor both recovery communities as well as those that encourage dangerous behaviors. The effectiveness of such platforms in promoting recovery despite housing both communities is underexplored. Our work begins to fill this gap by developing a statistical framework using survival analysis and situating our results within the cognitive behavioral theory of anorexia. This model identifies content and participation measures that predict the likelihood of recovery. From our dataset of over 68M posts and 10K users that self-identify with anorexia, we find that recovery on Tumblr is protracted - only half of the population is estimated to exhibit signs of recovery after four years. We discuss the effectiveness of social media in improving well-being around anorexia, a unique health challenge, and emergent questions from this line of work.