Temporal Word Embeddings for Early Detection of Psychological Disorders on Social Media

Abstract

[Abstract]: Mental health disorders represent a public health challenge, where early detection is critical to mitigating adverse outcomes for individuals and society. The study of language and behavior is a pivotal component in mental health research, and the content from social media platforms serves as a valuable tool for identifying signs of mental health risks. This paper presents a novel framework leveraging temporal word embeddings to capture linguistic changes over time. We specifically aim at at identifying emerging psychological concerns on social media. By adapting temporal word representations, our approach quantifies shifts in language use that may signal mental health risks. To that end, we implement two alternative temporal word embedding models to detect linguistic variations and exploit these variations to train early detection classifiers. Our experiments, conducted on 18 datasets from the eRisk initiative (covering signs of conditions such as depression, anorexia, and self-harm), show that simple models focusing exclusively on temporal word usage patterns achieve competitive performance compared to state-of-the-art systems. Additionally, we perform a word-level analysis to understand the evolution of key terms among positive and control users. These findings underscore the potential of time-sensitive word models in this domain, being a promising avenue for future research in mental health surveillance.The second and third authors thank the financial support supplied from projects: PLEC2021-007662 (MCIN/AEI/10.13039/501100011033 Ministerio de Ciencia e Innovación, European Union NextGenerationEU/PRTR) and PID2022-137061OB-C21 (MCIN/AEI/10.13039/501100011033/, Ministerio de Ciencia e Innovación, ERDF A way of making Europe, by the European Union); Consellería de Educación, Universidade e Formación Profesional, Spain (accreditations 2019-2022 ED431G/01 and GPC ED431B 2022/33) and the European Regional Development Fund, which acknowledges the CITIC Research Center. The first and fourth authors thank the financial support supplied by the Agencia Estatal de Investigación (Spain) (PID2022-137061OB-C22; PLEC2021-007662 MCIN/AEI/10.13039/501100011033, Plan de Recuperación, Transformación y Resiliencia, Unión Europea-Next Generation EU), Consellería de Cultura, Educación, Formación Profesional e Universidades (Centro de investigación de Galicia accreditation 2024-2027 ED431G-2023/04 and Reference Competitive Group accreditation 2022-2025, ED431C 2022/19) and the European Union (European Regional Development Fund - ERDF). The fourth author thanks the support obtained from project SUBV23/00002 (Ministerio de Consumo, Subdirección General de Regulación del Juego). Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431B 2022/33Xunta de Galicia; ED431G-2023/04Xunta de Galicia; ED431C 2022/1