用社交媒体数据自动识别房产相关事件与话题，助力城市治理。
A Social Data-Driven System for Identifying Estate-related Events and Topics
- 基于语言模型的分层分类框架，筛选并归类房产事件
- 对无地理标签内容，用Transformer模型推断至兴趣点级位置
- 适合城市管理者和应急响应团队快速获取实时信息
Twitter、Facebook等社交平台已成为日常生活中不可或缺的部分，提供丰富的本地化新闻与个人经历。随着城市人口增长，这些平台成为识别房产相关问题的重要资源。本文提出一种基于语言模型的系统，从社交媒体内容中检测并分类房产相关事件。系统采用分层分类框架，先筛选相关帖子，再将其归入可操作的房产主题类别。对于缺乏显式地理标签的帖子，引入基于Transformer的定位模块，推断其发布位置至兴趣点级别。该集成方法为城市管理、应急响应与态势感知提供了及时、数据驱动的洞察。
原文摘要 · Abstract (English)
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.
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