标题： 一种用于识别与房地产相关的事件和主题的社会数据驱动系统 显示英文标题

标题： A Social Data-Driven System for Identifying Estate-related Events and Topics

摘要： 社交媒体平台如Twitter和Facebook已经深深嵌入我们的日常生活中，提供动态的本地新闻和个人经历流。 这些平台的普及使它们成为识别与房地产相关问题的宝贵资源，尤其是在城市人口增长的背景下。 在本工作中，我们提出了一种基于语言模型的系统，用于从社交媒体内容中检测和分类与房地产相关的事件。 我们的系统采用分层分类框架，首先过滤相关帖子，然后将它们分类为可操作的房地产相关主题。 此外，对于缺乏显式地理标签的帖子，我们应用基于Transformer的地理定位模块，在兴趣点级别推断发布位置。 这种综合方法为城市管理、运营响应和态势感知提供了及时的数据驱动见解。 显示英文摘要

摘要： 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.