Applying Topic Modelling to Party Discourse: An Exploration of the Italian Case in 2013–2019


  • Andrea Pareschi University of Bologna



Italian politics, party discourse, content anlysis, text analysis, topic modelling, social media, Facebook


During the last few years, external crises and endogenous weaknesses have combined to plunge the Italian political system into generalised instability. In particular, the major political parties have experienced rapidly turning tides in a context of intensified electoral volatility. This explorative article sets out to get an insight into the discursive struggles that have pitted these parties one against another, undergirding the ebb and flow in their respective mass support and revolving around the ways of communicating political change. To that end, I collect data from the official Facebook pages of the four main Italian parties, downloading posts they published in the period 2013–2019 via the Netvizz application, and I analyse the four corresponding textual corpora through the technique of Topic Modelling. On such bases, the article finds the overall configuration of the political discourse of Italian parties to be aptly described by a model comprising 16 topics, equally divided into ‘partisan’ and ‘cross-cutting’ ones, with the former having a slight edge in terms of diffusion. The four parties differ among themselves by the topics they focus on and by the quantity of topics they choose to include sizably in their streams of communication.


Behar Villegas, E. (2016), “Facebook and its Disappearing Posts: Data Collection Approaches on Fan-Pages for Social Scientists”, The Journal of Social Media in Society, 5 (1): 160–188.

Blei, D.M., Ng, A.Y., and Jordan, M.I. (2003), “Latent Dirichlet Allocation”, Journal of Machine Learning Research, 3 (1): 993–1022.

Bottos, G., Desiata, E., and Pareschi, A. (2020), “Introduction: On Discourse Categories and Their Political Use: Conceptual and Methodological Foundations of the Study”, in G. Bottos, E. Desiata and A. Pareschi (eds), Changing Political Discourse in the Aftermath of the 2008 Crisis: The Case of Italy, 11–48, Brussels/Rome: FEPS and Fondazione Gramsci.

Budge, I., and Farlie, D. (1983), Explaining and Predicting Elections: Issue Effects and Party Strategies in Twenty-Three Democracies, London: Allen and Unwin.

Chong, D., and Druckman, J. (2007), “Framing Theory”, Annual Review of Political Science, 10: 103–126.

Dalton, R. (1985), “Political Parties and Political Representation: Party Supporters and Party Elites in Nine Nations”, Comparative Political Studies, 18 (3): 267–299.

DiMaggio, P., Nag, M., and Blei, D. (2013), “Exploiting Affinities between Topic Modeling and the Sociological Perspective on Culture: Application to Newspaper Coverage of U.S. Government Arts Funding”, Poetics, 41 (6): 570–606.

Disch, L. (2011), “Towards a Mobilization Conception of Democratic Representation”, The American Political Science Review, 105 (1): 100–114.

Downs, A. (1957), An Economic Theory of Democracy, New York, NY: HarperCollins.

Ernst, N., Engesser, S., Büchel, F., Blassnig, S., and Esser, F. (2017), “Extreme Parties and Populism: An Analysis of Facebook and Twitter across Six Countries”, Information, Communication & Society, 20 (9): 1347–1364.

Ferri, P., Lusiani, M., and Pareschi, L. (2018) “Accounting for Accounting History: A topic modeling approach (1996–2015)”, Accounting History, 23 (1-2): 173–205.

Gaxie, D., and Rowell, J. (2011), “Methodology of the Project”, in D. Gaxie, N. Hubé and J. Rowell (eds), Perceptions of Europe: A Comparative Sociology of European Attitudes, 37–49, Colchester: ECPR Press.

Gurevitch, M., and Levy, M.R. (eds, 1985), Mass Communication Review Yearbook (Vol. 5), Beverly Hills, CA: SAGE.

Hoeglinger, D. (2016), “The Politicisation of European Integration in Domestic Election Campaigns”, West European Politics, 39 (1): 44–63.

Hannigan, T., Haans, R.F.J., Vakili, K., Tchalian, H., Glaser, V., Wang, M., Kaplan, S., and Devereaux Jennings, P. (2019), “Topic Modeling in Management Research: Rendering New Theory from Textual Data”, Academy of Management Annals, 13 (2): 586–632.

McCallum, A.K. (2002), “MALLET: A Machine Learning for Language Toolkit”. Available online at (last accessed: February 26, 2020).

Mohr, J.W., and Bogdanov, P. (2013), “Introduction: Topic Models: What They Are and Why They Matter”, Poetics, 41 (6): 545–569.

Mosca, L., and Tronconi, F. (2019), “Beyond Left and Right: The Eclectic Populism of the Five Star Movement”, West European Politics, 42 (6): 1258–1283.

Passarelli, G., and Tuorto, D. (2018), La Lega di Salvini: Estrema Destra di Governo, Bologna: Il Mulino.

Petrocik, J.R. (1996), “Issue Ownership in Presidential Elections, with a 1980 Case Study”, American

Journal of Political Science, 40 (3): 825–850.

Rieder, B. (2013), “Studying Facebook via Data Extraction: The Netvizz Application”, in H. Davis et al. (eds), WebSci ’13: Proceedings of the 5th Annual ACM Web Science Conference, 346–355, New York, NY: Association for Computing Machinery. Available online at (last accessed: February 26, 2020).

Rohrschneider, R., and Whitefield, S. (2012), The Strain of Representation: How Parties Represent Diverse Voters in Western and Eastern Europe, Oxford: Oxford University Press.

Rovny, J. (2012), “Who Emphasizes and Who Blurs? Party Strategies in Multidimensional Competition”, European Union Politics, 13 (2): 269–292.

Sartori, G. (1976), Parties and Party Systems, Cambridge: Cambridge University Press.

Stokes, D.E. (1963), “Spatial Models of Party Competition”, The American Political Science Review, 57 (2): 368–377.




How to Cite

Pareschi, A. (2020). Applying Topic Modelling to Party Discourse: An Exploration of the Italian Case in 2013–2019. PuntOorg International Journal, 5(2), 150–171.