Geographies of Twitter debates

Emiliano Gobbo (), Lara Fontanella (), Sara Fontanella () and Annalina Sarra ()
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Emiliano Gobbo: G. d’Annunzio University Chieti-Pescara
Lara Fontanella: G. d’Annunzio University Chieti-Pescara
Sara Fontanella: Imperial College of London
Annalina Sarra: G. d’Annunzio University Chieti-Pescara

Journal of Computational Social Science, 2022, vol. 5, issue 1, No 28, 647-663

Abstract: Abstract Over the last years, the prodigious success of online social media sites has marked a shift in the way people connect and share information. Coincident with this trend is the proliferation of location-aware devices and the consequent emergence of user-generated geospatial data. From a social scientific perspective, these location data are of incredible value as it can be mined to provide researchers with useful information about activities and opinions across time and space. However, the utilization of geo-located data is a challenging task, both in terms of data management and in terms of knowledge production, which requires a holistic approach. In this paper, we implement an integrated knowledge discovery in cyberspace framework for retrieving, processing and interpreting Twitter geolocated data for the discovery and classification of the latent opinion in user-generated debates on the internet. Text mining techniques, supervised machine learning algorithms and a cluster spatial detection technique are the building blocks of our research framework. As real-word example, we focus on Twitter conversations about Brexit, posted on Uk during the 13 months before the Brexit day. The experimental results, based on various analysis of Brexit-related tweets, demonstrate that different spatial patterns can be identified, clearly distinguishing pro- and anti-Brexit enclaves and delineating interesting Brexit geographies.

Keywords: Social media; Knowledge discovery in cyberspace; Ensemble learning; Cluster detection and Brexit debate; 62Pxx; 62-07 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s42001-021-00143-7

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