Content-location relationships

Tang, Vicente; Painho, Marco

http://hdl.handle.net/10362/154769

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Autores

Tang, Vicente

Painho, Marco

Resumo(s)

The use of social media and location-based networks through GPS-enabled devices provides geospatial data for a plethora of applications in urban studies. However, the extent to which information found in geo-tagged social media activity corresponds to the spatial context is still a topic of debate. In this article, we developed a framework aimed at retrieving the thematic and spatial relationships between content originated from space-based (Twitter) and place-based (Google Places and OSM) sources of geographic user-generated content based on topics identified by the embedding-based BERTopic model. The contribution of the framework lies on the combination of methods that were selected to improve previous works focused on content-location relationships. Using the city of Lisbon (Portugal) to test our methodology, we first applied the embedding-based topic model to aggregated textual data coming from each source. Results of the analysis evidenced the complexity of content-location relationships, which are mostly based on thematic profiles. Nonetheless, the framework can be employed in other cities and extended with other metrics to enrich the research aimed at exploring the correlation between online discourse and geography.

Descrição

Tang, V., & Painho, M. (2023). Content-location relationships: a framework to explore correlations between space-based and place-based user-generated content. International Journal Of Geographical Information Science, 37(8), 1840–1871. https://doi.org/10.1080/13658816.2023.2213869 ---The authors acknowledge the funding from the Portuguese national funding agency for science, research and technology (Fundação para a Ciência e a Tecnologia – FCT) through the CityMe project (EXPL/GES-URB/1429/2021; https://cityme.novaims.unl.pt/) and the project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.

Palavras-chave

Content-location relationships UGC geo-tagged activity topic modeling Information Systems Geography, Planning and Development Library and Information Sciences SDG 11 - Sustainable Cities and Communities