Feeling anxious? Perceiving anxiety in tweets using machine learning

an article by Dritjon Gruda and  Souleiman Hasan (National University of Ireland Maynooth) published in Computers in Human Behavior Volume 98 (September 2019)

Highlights

• Predictive measurement tool to examine anxiety in tweets using a machine learning approach.
• Machine learning approach depicts perceived user state-anxiety fluctuations and trait anxiety.
• Perceived anxiety relates negatively to social engagement and popularity.
• Implications include automatically assessing workers' wellbeing to reduce anxiety.

Abstract

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.

---