Social Construction of Urban Space: Using LLMs to Identify Neighborhood Boundaries From Craigslist Ads
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917186973466624 |
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| author | Visokay, Adam Bagley, Ruth Kennedy, Ian Hess, Chris Crowder, Kyle Voigt, Rob Peskoff, Denis |
| author_facet | Visokay, Adam Bagley, Ruth Kennedy, Ian Hess, Chris Crowder, Kyle Voigt, Rob Peskoff, Denis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00634 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Social Construction of Urban Space: Using LLMs to Identify Neighborhood Boundaries From Craigslist Ads Visokay, Adam Bagley, Ruth Kennedy, Ian Hess, Chris Crowder, Kyle Voigt, Rob Peskoff, Denis Computation and Language 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. |
| title | Social Construction of Urban Space: Using LLMs to Identify Neighborhood Boundaries From Craigslist Ads |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.00634 |