Personality Traits and Demographics Analysis in Online Mental Health Discourse

Social media provides valuable insights into users' thoughts, behaviors, and emotions, offering opportunities for mental health research. In this work, we explore how personality traits and demographic attributes manifest in the online behavior of individuals suffering from mental disorders. Focusing on the Big-5 personality dimensions, we analyze social media users associated with four mental health disorders (Anorexia, Depression, Gambling, and Self-harm), investigating how these traits differ across groups. Using the PANDORA dataset –which provides annotations for personality traits, age, and gender–, we train models for personality prediction and author profiling. These models are subsequently transferred to various eRisk collections. Besides confirming known trends (e.g., high association between anorexia and certain young female groups, or between gambling and young males), our analysis reveals intriguing personality traits. For example, we found high neuroticism and agreeableness, and low extraversion and conscientiousness shared across most disorders. These trends underscore the relevance of these personality traits for these mental health problems. Finally, we conclude by analyzing demographic biases in risk detection systems and show that alert accuracy differs significantly across demographic groups.