Feeling Anxious? Perceiving Anxiety in Tweets using Machine Learning
This addresses the problem of non-intrusively monitoring mental health indicators in social media for individuals, organizations, and society, though it is incremental as it applies existing methods to new data.
The study developed a machine learning tool to measure perceived anxiety in tweets over time, finding it captures fluctuations in state-anxiety and trait anxiety, and identified a reverse relationship between anxiety and social engagement/popularity.
This study provides a predictive measurement tool to examine perceived anxiety from a longitudinal perspective, using a non-intrusive machine learning approach to scale human rating of anxiety in microblogs. Results suggest that our chosen machine learning approach depicts perceived user state-anxiety fluctuations over time, as well as mean trait anxiety. We further find a reverse relationship between perceived anxiety and outcomes such as social engagement and popularity. Implications on the individual, organizational, and societal levels are discussed.