{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:52:58Z","timestamp":1760151178719,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The popularity of mobile devices with GPS capabilities, along with the worldwide adoption of social media, have created a rich source of text data combined with spatio-temporal information. Text data collected from location-based social networks can be used to gain space\u2013time insights into human behavior and provide a view of time and space from the social media lens. From a data modeling perspective, text, time, and space have different scales and representation approaches; hence, it is not trivial to jointly represent them in a unified model. Existing approaches do not capture the sequential structure present in texts or the patterns that drive how text is generated considering the spatio-temporal context at different levels of granularity. In this work, we present a neural language model architecture that allows us to represent time and space as context for text generation at different granularities. We define the task of modeling text, timestamps, and geo-coordinates as a spatio-temporal conditioned language model task. This task definition allows us to employ the same evaluation methodology used in language modeling, which is a traditional natural language processing task that considers the sequential structure of texts. We conduct experiments over two datasets collected from location-based social networks, Twitter and Foursquare. Our experimental results show that each dataset has particular patterns for language generation under spatio-temporal conditions at different granularities. In addition, we present qualitative analyses to show how the proposed model can be used to characterize urban places.<\/jats:p>","DOI":"10.3390\/ijgi11020147","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:18:05Z","timestamp":1645431485000},"page":"147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Language Modeling on Location-Based Social Networks"],"prefix":"10.3390","volume":"11","author":[{"given":"Juglar","family":"Diaz","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Chile & IMFD, Santiago 8370456, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2153-4306","authenticated-orcid":false,"given":"Felipe","family":"Bravo-Marquez","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Chile & IMFD, Santiago 8370456, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7669-645X","authenticated-orcid":false,"given":"Barbara","family":"Poblete","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Chile & IMFD, Santiago 8370456, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhang, K., Yuan, Q., Zhang, L., Hanratty, T., and Han, J. 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