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Geographies of Twitter debates

Author

Listed:
  • 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)

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.

Suggested Citation

  • Emiliano Gobbo & Lara Fontanella & Sara Fontanella & Annalina Sarra, 2022. "Geographies of Twitter debates," Journal of Computational Social Science, Springer, vol. 5(1), pages 647-663, May.
    • Handle: RePEc:spr:jcsosc:v:5:y:2022:i:1:d:10.1007_s42001-021-00143-7
    DOI: 10.1007/s42001-021-00143-7
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    References listed on IDEAS

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    1. S. Bersimis & C. Chalkias & T. Anthopoulou, 2014. "Detecting and interpreting clusters of economic activity in rural areas using scan statistic and LISA under a unified framework," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 30(5), pages 573-587, September.
    2. Dilek Yildiz & Jo Munson & Agnese Vitali & Ramine Tinati & Jennifer A. Holland, 2017. "Using Twitter data for demographic research," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(46), pages 1477-1514.
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