  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.
