Open Conference Systems, 50th Scientific meeting of the Italian Statistical Society

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Brexit in Italy - Text Mining of Social Media
Francesca Greco, Livia Celardo, Leonardo Salvatore Alaimo

Last modified: 2018-06-07


The aim of the research is to identify how Brexit is discussed about on Twitter through a text mining approach. We collected all the tweets containing the term “Brexit†for 20 days, resulting in a large corpus of hundred thousand tokens to which we applied multivariate techniques in order to identify the contents and the sentiments behind the shared comments.


  1. Alaimo, L. S.: Demographic and socio-economic factors influencing the Brexit vote. Rivista Italiana di Economia, Demografia e Statistica (RIEDS), 72(1), 17–28 (2018).
  2. Celardo, L., Iezzi, D. F., Vichi, M.: Multi-mode partitioning for text clustering to reduce dimensionality and noises. In: Mayaffe, D., Poudat, C., Vanni, L., Magri, V., Follette, P. (eds) JADT 2016: Statistical Analysis of Textual Data. Les Press de Fac Imprimeur, Nizza (2016).
  3. Ceron, A., Curini, L., Iacus, S.M., Porro, G.: Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 16(2), 340-358 (2014).
  4. Ceron, A., Curini, L., Iacus, S.M.: Social Media e Sentiment Analysis. L’evoluzione dei fenomeni sociali attraverso la Rete. Springer, Milano (2013).
  5. Clark, H.D., Goodwin, M., Whiteley, P.: Brexit: Why People Voted to Leave the European Union. Cambridge University Press, Cambridge (2017).
  6. Dhingra, S., Ottaviano, G., Sampson, T., Van Reenen, J.: The consequences of Brexit for UK trade and living standards. Centre for Economic Performance (CEP), London School of Economics and Political Science (LSE) (2016).
  7. Gietel-Bastel, S.: Why Brexit? The Toxic Mix of Immigration and Austerity. Population and Development Review, 42(4), 673–680 (2016)
  8. Goodwin, M. J., Heath, O.: The 2016 Referendum, Brexit and the Left Behind: An Aggregate-level Analysis of the Result. The Political Quarterly, 87(3), 323–332 (2016)
  9. Greco, F., Mascietti, D., Polli, A.: Emotional text mining of social networks: the French pre-electoral sentiment on migration. RIEDS (2018) Available from
  10. Greco, F.: Integrare la disabilità. Una metodologia interdisciplinare per leggere il cambiamento culturale. Franco Angeli, Milano (2016).
  11. Iezzi, D. F.: Centrality measures for text clustering. Communications in Statistics-Theory and Methods, 41(16-17), 3179-3197 (2012).
  12. Lebart, L., Salem, A.: Statistique Textuelle. Dunod, Paris (1994).
  13. Pelagalli, F., Greco, F., De Santis, E.: Social emotional data analysis. The map of Europe. In: Petrucci A., Verde R. (eds) SIS 2017. Statistics and Data Science: new challenges, new generations. Proceedings of the Conference of the Italian Statistical Society, Florence 28-30 June 2017.: Firenze University Press (2017).
  14. Savaresi, S.M., Boley, D.L.: A comparative analysis on the bisecting K-means and the PDDP clustering algorithms. Intelligent Data Analysis, 8(4), 345-362 (2004).
  15. Vichi, M.: Double k-means clustering for simultaneous classification of objects and variables. Advances in classification and data analysis, 43-52 (2001).
  16. Vickers, J. Consequences of Brexit for Competition Law and Policy. Oxford Review of Economic Policy, 33 (2017).

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