Importance-aware Topic Modeling for Discovering Public Transit Risk from Noisy Social Media

arXiv — cs.LG•Tuesday, December 9, 2025 at 5:00:00 AM

NeutralArtificial Intelligence

Urban transit agencies are increasingly utilizing social media to identify emerging service risks such as crowding, delays, and safety incidents. A new framework has been developed that employs an influence-weighted keyword co-occurrence graph and Poisson Deconvolution Factorization to analyze social media posts, allowing for the extraction of interpretable topics and their importance scores.
This development is significant as it enhances the ability of transit agencies to monitor and respond to public concerns in real-time, potentially improving service reliability and passenger safety by addressing issues before they escalate.
The integration of advanced topic modeling techniques in analyzing social media reflects a broader trend in leveraging digital platforms for real-time insights across various sectors, including political discourse and mental health, highlighting the growing importance of understanding public sentiment through social media analytics.

— via World Pulse Now AI Editorial System

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