aceLeveraging social media footprints for predicting college student anxiety: a machine learning approach
Background Anxiety is one of the most prevalent mental health concerns among college students worldwide, yet traditional assessment methods relying on self-report questionnaires are time-consuming, susceptible to response bias, and difficult to scale. Social media platforms, which students use extensively, generate rich behavioral and linguistic data that may reflect underlying psychological states. This study investigates whether passively collected social media footprints are associated with anxiety scores amo…
A Random Forest model trained on linguistic, emotional, cognitive, behavioral and temporal features from Weibo posts predicted Self-Rating Anxiety Scale scores among consenting Chinese college students with R2 0.77 on the test set.
Findings are cross-sectional, limited to consenting active Weibo users in the sample, and show association and score prediction within study context, not clinical diagnosis or longitudinal forecasting.
Evidence
- Peer-reviewedBMC Psychology2026-07-11
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Truvace Impact Record TRV-2026-0121, v1: “Leveraging social media footprints for predicting college student anxiety: a machine learning approach.” Truvace, 2026-07-13. /record/TRV-2026-0121 (accessed at citation time). sha256 c460be4b1d3db9b0…
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