{"ID":2891847,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.03711","arxiv_id":"2508.03711","title":"A Social Data-Driven System for Identifying Estate-related Events and Topics","abstract":"Social media platforms such as Twitter and Facebook have become deeply embedded in our everyday life, offering a dynamic stream of localized news and personal experiences. The ubiquity of these platforms position them as valuable resources for identifying estate-related issues, especially in the context of growing urban populations. In this work, we present a language model-based system for the detection and classification of estate-related events from social media content. Our system employs a hierarchical classification framework to first filter relevant posts and then categorize them into actionable estate-related topics. Additionally, for posts lacking explicit geotags, we apply a transformer-based geolocation module to infer posting locations at the point-of-interest level. This integrated approach supports timely, data-driven insights for urban management, operational response and situational awareness.","short_abstract":"Social media platforms such as Twitter and Facebook have become deeply embedded in our everyday life, offering a dynamic stream of localized news and personal experiences. The ubiquity of these platforms position them as valuable resources for identifying estate-related issues, especially in the context of growing urba...","url_abs":"https://arxiv.org/abs/2508.03711","url_pdf":"https://arxiv.org/pdf/2508.03711v1","authors":"[\"Wenchuan Mu\",\"Menglin Li\",\"Kwan Hui Lim\"]","published":"2025-07-22T14:48:42Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.AI\",\"cs.CL\",\"cs.LG\",\"cs.SI\"]","methods":"[\"Transformer\",\"Language Model\"]","has_code":false}