Methods in predictive techniques for mental health status on social media: a critical review

By Stevie Chancellor, M. de Choudhury. npj Digital Medicine, 2020

533 citations recorded by the source corpus.

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.

Subfields: Applied Psychology, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/73519905370d043898b9ef962a3cf1d5057d4262

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Citations in the corpus

Gendered Mental Health Stigma in Masked Language Models (2022, background): “Some NLP models developed to address mental health issues may have limited utility due to a lack of grounding in psychology research (Chancellor and De Choudhury, 2020).”
Leveraging Large Language Models for Mental Health Prediction via Online Text Data (2023, background): “Online platforms, especially social media platforms, have been acknowledged as a promising lens that is capable of revealing insights into the psychological states, health, and well-being of both individuals and populations [14, 22, 25, 36, 63].”
Artificial Intelligence for Mental Healthcare: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom (2021, background): “…data have been leveraged to predict diagnoses and relapses (51,72,76,77), with accuracies comparable to clinician assessments and screening surveys (78). f However, sensor data do not yet have clear usability in clinical mental health settings (79) due to lacking clinical validity and…”