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

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.

keywords: Mental health detection, Social media analysis, Machine learning, Word embeddings, Temporal word representations, Text mining