@inproceedings{visokay-etal-2026-social,
title = "Social Construction of Urban Space: Using {LLM}s to Identify Neighborhood Boundaries From {C}raigslist Ads",
author = "Visokay, Adam and
Bagley, Ruth and
Hess, Chris and
Kennedy, Ian and
Crowder, Kyle and
Voigt, Rob and
Peskoff, Denis",
editor = "Card, Dallas and
Field, Anjalie and
Keith, Katherine and
Mendelsohn, Julia",
booktitle = "Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science",
month = jul,
year = "2026",
address = "San Diego",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nlpcss-1.18/",
pages = "307--321",
ISBN = "979-8-89176-426-2",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Social Construction of Urban Space: Using LLMs to Identify Neighborhood Boundaries From Craigslist Ads
%A Visokay, Adam
%A Bagley, Ruth
%A Hess, Chris
%A Kennedy, Ian
%A Crowder, Kyle
%A Voigt, Rob
%A Peskoff, Denis
%Y Card, Dallas
%Y Field, Anjalie
%Y Keith, Katherine
%Y Mendelsohn, Julia
%S Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego
%@ 979-8-89176-426-2
%F visokay-etal-2026-social
%X 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.
%U https://aclanthology.org/2026.nlpcss-1.18/
%P 307-321
Markdown (Informal)
[Social Construction of Urban Space: Using LLMs to Identify Neighborhood Boundaries From Craigslist Ads](https://aclanthology.org/2026.nlpcss-1.18/) (Visokay et al., NLP+CSS 2026)
ACL