2026-09-10T09:45:03Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:27569 2026-09-10T05:38:43Z ddc:004 open_access

Document Geocoding for Inferring Spatial Familiarity Within Reddit Text (Short Paper) Smith, Paddy Gould, Myles Manley, Ed spatial familiarity document geocoding geoparsing LLMs In this paper, we reframe social media user geolocation as a task of inferring a user’s spatial familiarity. We propose an LLM-based approach that identifies places a user is most familiar, including the strength of familiarity. We evaluate this approach using two Reddit evaluation corpora constructed from subreddit user-flair tags: Reddit-London, where users are associated with London boroughs, and Reddit-USA, where users are associated with US states. We compare these datasets with GeoText benchmark dataset. The results show that LLMs can recover place-associated labels from social media text, achieving the strongest performance on Reddit-USA state prediction and moderate performance on fine-grained London borough prediction. We further show that inferred familiar places exhibit clear spatial dependency. Using polygon-based metrics, familiar places inferred from Reddit-London users have a mean distance to labeled boundary of 3.48 km, while Reddit-USA shows stronger alignment with state labels compared to GeoText, with higher inside-label share, and lower spatial dispersion. These findings suggest spatial familiarity provides a useful alternative framing for social media user geolocation, and that Reddit flair-based labels are more closely aligned with expressed spatial familiarity than home location labels in GeoText benchmark dataset. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Paddy Smith and Myles Gould and Ed Manley 2026 Is Part Of LIPIcs, Volume 393, 17th International Conference on Spatial Information Theory (COSIT 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.COSIT.2026.25 urn:nbn:de:0030-drops-275698 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2026.25 eng https://creativecommons.org/licenses/by/4.0/legalcode