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Feeling anxious? Perceiving anxiety in tweets using machine learning
| Content Provider | PsyArXiv |
|---|---|
| Author | Gruda, Dritjon Hasan, Souleiman |
| Description | 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. |
| DOI | 10.31234/osf.io/xfmqd |
| Language | English |
| Publisher Date | 2019-10-01 |
| Access Restriction | Open |
| Rights License | CC-By Attribution 4.0 International |
| Subject Keyword | Social and Behavioral Sciences;Social and Personality Psychology;Affect and Emotion Regulation;Industrial and Organizational Psychology;Occupational Health;Quantitative Methods;Computational Modeling;Psychometrics;Health Psychology;Mental Health;Health-related Behavior Anxiety Health Machine Learning Micro-blogs Twitter |
| Content Type | Text |
| Resource Type | Preprint |
| Subject | Social Sciences Social Psychology Psychology Mathematics Psychiatry and Mental Health Neuropsychology and Physiological Psychology |
