用爬虫广告分析芝加哥社区边界，发现真实认知与官方划分的差异。
Social Construction of Urban Space: Using LLMs to Identify Neighborhood Boundaries From Craigslist Ads
- 结合人工标注与大模型，从租房广告中识别社区归属
- 发现三类空间冲突：边界争议、跨区宣称、远距离蹭名
- 语言特征反映位置差异，中心区强调便利性，外围突出环境
租房广告揭示了城市空间如何通过语言被社会建构。我们分析2018至2024年芝加哥Craigslist上的租房广告，考察房源代理对社区的描述，识别制度边界与社区自我认定之间的不一致。通过人工与大语言模型标注，将非结构化广告按社区分类。地理空间分析揭示三种模式：因竞争性空间定义导致的社区归属冲突、位于边界的房产对相邻社区的有效归属主张，以及将遥远优质社区作为声誉标签的“声誉洗白”现象。主题建模显示，与社区中心距离不同的房源强调不同配套设施：远离中心的房源更关注环境属性，而中心区域则强调便利性。自然语言处理方法揭示了传统方法忽略的城市空间定义之争。
原文摘要 · Abstract (English)
Rental listings offer a window into how urban space is socially constructed through language. We analyze Chicago Craigslist rental advertisements from 2018 to 2024 to examine how listing agents characterize neighborhoods, identifying mismatches between institutional boundaries and neighborhood claims. Through manual and large language model annotation, we classify unstructured listings from Craigslist according to their neighborhood. Further geospatial analysis reveals three distinct patterns: properties with conflicting neighborhood designations due to competing spatial definitions, border properties with valid claims to adjacent neighborhoods, and "reputation laundering" where listings claim association with distant, desirable neighborhoods. Through topic modeling, we identify patterns that correlate with spatial positioning: listings further from neighborhood centers emphasize different amenities than centrally-located units. Natural language processing techniques reveal how definitions of urban spaces are contested in ways that traditional methods overlook.
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