Early detection of anorexia from reddit posts using time series based transformer model

Abstract

Rates of mental health concerns are rising, and an increasing number of individuals openly share their experiences on social media platforms (Hasell and Nabi, in: Emotions in the digital world: exploring affective experience and expression in online interactions, Oxford University Press, 2023). This openness creates an opportunity to study, detect, and ultimately support those at risk using data-driven methods. We focus on Anorexia Nervosa, an eating disorder characterized by persistent restriction and an intense fear of weight gain. Early, automatic identification can enable timelier assessment and intervention. We propose a transformer-based time-series model that analyzes longitudinal Reddit activity to estimate an individual’s likelihood of Anorexia. The model jointly captures temporal dynamics (how signals evolve over time) and semantic content (what the posts mean), yielding an accuracy of. In our experiments, this approach outperforms baselines that rely solely on semantic features, underscoring the value of modeling user trajectories rather than treating posts in isolation. We further conduct post-hoc explanation analyses to highlight the features most responsible for the model’s predictions, and we show that these attributions align with human intuition. Code for our approach is available in the repository here (https://anonymous.4open.science/r/From-Posts-to-Patterns-Detecting-Anorexia-on-Reddit-6CB7/).