A Social Data-Driven System for Identifying Estate-related Events and Topics
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918115666821120 |
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| author | Mu, Wenchuan Li, Menglin Lim, Kwan Hui |
| author_facet | Mu, Wenchuan Li, Menglin Lim, Kwan Hui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03711 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Social Data-Driven System for Identifying Estate-related Events and Topics Mu, Wenchuan Li, Menglin Lim, Kwan Hui Information Retrieval Artificial Intelligence Computation and Language Machine Learning Social and Information Networks 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. |
| title | A Social Data-Driven System for Identifying Estate-related Events and Topics |
| topic | Information Retrieval Artificial Intelligence Computation and Language Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2508.03711 |