Toward a Critical Toponymy Framework for Named Entity Recognition: A Case Study of Airbnb in New York City
This work addresses the gap in critical toponymy by examining everyday place references, with implications for research on neighborhood status and gentrification, though it is incremental in applying computational methods to a specific domain.
The study tackled the problem of how cultural and economic capital influence place references by analyzing 47,440 Airbnb listings in New York City, resulting in a new named entity recognition model that identifies discourse categories relevant to place characterization.
Critical toponymy examines the dynamics of power, capital, and resistance through place names and the sites to which they refer. Studies here have traditionally focused on the semantic content of toponyms and the top-down institutional processes that produce them. However, they have generally ignored the ways in which toponyms are used by ordinary people in everyday discourse, as well as the other strategies of geospatial description that accompany and contextualize toponymic reference. Here, we develop computational methods to measure how cultural and economic capital shape the ways in which people refer to places, through a novel annotated dataset of 47,440 New York City Airbnb listings from the 2010s. Building on this dataset, we introduce a new named entity recognition (NER) model able to identify important discourse categories integral to the characterization of place. Our findings point toward new directions for critical toponymy and to a range of previously understudied linguistic signals relevant to research on neighborhood status, housing and tourism markets, and gentrification.