From Posts to Patterns: Early Detection of Anorexia on Reddit

Rates of mental health concerns are rising, and an increasing number of individuals openly share their experiences on social media platforms. 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 85.2%. 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.