Il dibattito sulla migrazione in campagna elettorale: confronto tra il caso francese e italiano
The Debate on Migration in the Electoral Campaign: Comparison between the French and Italian Case
Migration has actually gained considerable relevance both in the public and political debate, which in some cases has been associated with a strengthening of nationalist sentiments calling into question the European Union membership. Migration was one of the most relevant topics of the political debate during the French and Italian electoral campaigns, also thanks to the role played by the social media, which has solicited the electoral consensus on the idea that migration may be a risk factor for citizens. In order to analyze the representation and the sentiment on migration characterizing the political debate during the electoral campaign in France and Italy, we collected two samples of messages, in both languages, containing the word migrant produced in the period preceding the vote from the Twitter repository. The messages were collected in two large size corpora, which underwent the Emotional Text Mining. The results comparison shows that Italian and French messages are similar concerning some issues (work, security, exploitation, solidarity) but differ in the sentiment intensity, which is mainly negative in Italy. Finally, if it seems possible to identify a close connection between the election result and the issues debated in France, this association seems less evident in Italy.
Binotto, M., Bruno, M. & Lai, V. (2016). Tracciare confini. L'immigrazione nei media ita-liani. Milano: FrancoAngeli.
Castells, M. (2007).Communication, Power and Counter-power in the Network Society. International Journal of Communication, 1, pp. 238-266
Carli, R. (1990). Il processo di collusione nelle rappresentazioni sociali. Rivista di Psicolo-gia Clinica, 4, pp. 282-296.
Carli, R. & Paniccia, R.M. (2002). Analisi emozionale del testo. Milano: FrancoAngeli.
Carzo, D. (ed.) (2011). Narrare l’Altro. Pratiche discorsive sull’immigrazione. Roma: A-racne.
Cepernich, C. & Novelli, E. (2018). Sfumature del razionale. La comunicazione politica e-mozionale nell’ecosistema ibrido dei media. Comunicazione politica, 1, pp. 13-30.
Ceron, A., Curini, L. & Iacus, S. (2013). Social Media e Sentiment Analysis. L’evoluzione dei fenomeni sociali attraverso la Rete. Milano: Springer-Verlag Italia.
Ceron, A., Curini, L. & Iacus, S.M. (2016). iSA: a fast, scalable and accurate algorithm for sentiment analysis of social media content. Information Sciences, 367, pp. 105-124.
Ceron, A., Curini, L., Iacus, S.M. & Porro, G. (2014). 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), pp. 340-358.
Cordella, B., Greco, F. & Raso, A. (2014). Lavorare con corpus di piccole dimensioni in psicologia clinica: una proposta per la preparazione e l’analisi dei dati. In Nee E., Daube M., Valette M. & Fleury S. (Eds), Actes JADT 2014, 12es Journées internationales d’Analyse Statistque des Données Textuelles, Paris, France, Juin 3-6, 2014 (pp. 173-184). Paris: Lexicometrica.
Fornari, F. (1976). Simbolo e codice: Dal processo psicoanalitico all’analisi istituzionale. Milano: Feltrinelli.
Gentry, J. (2016). R Based Twitter Client. R package version 1.1.9.
Greco, F. (2016). Integrare la disabilità: Una metodologia interdisciplinare per leggere il cambiamento culturale. Milano: FrancoAngeli.
Greco, F., Maschietti, D. & Polli, A. (2017). Emotional text mining of social networks: The French pre-electoral sentiment on migration. RIEDS, 71(2), pp. 125-136.
Greco, F., Alaimo, L. & Celardo, L. (2018). Brexit and Twitter: The voice of people. In D.F. Iezzi, L. Celardo & M. Misuraca (Eds.), JADT’ 18, Proceedings of the 14th Inter-national Conference on statistical analysis of textual data, Rome, 12-15 June, 2018, Vol. I, (pp. 327-334). Roma: Universitalia.
Hopkins, D. & King, G. (2010). A method of automated nonparametric content analysis for social science. American J. Pol. Sci., 54(1), pp. 229-247.
Lancia, F. (2017). User’s Manual: Tools for text analysis. T-Lab version Plus 2017.
Lebart, L., Salem, A. & Berry, L. (1997). Exploring textual data. Vol. 4. New York: Sprin-ger Science & Business Media.
Matte Blanco, I. (1981). L’inconscio come insiemi infiniti: Saggio sulla bi-logica. Torino: Einaudi.
Moscovici, S. (2005). Le rappresentazioni sociali. Bologna: il Mulino.
Pelagalli,F., Greco, F. & De Santis, E. (2017). Social emotional data analysis. The map of Europe. In A. Petrucci & R. Verde (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, (pp. 779-784). Firenze: Firenze University Press.
Salvatore, S. & Freda, M.F. (2011). Affect, unconscious and sensemaking: A psychody-namic, semiotic and dialogic model. New Ideas in Psychology, 29(2), pp. 119–135.
Savaresi, S.M., Boley, D. L. 2004. A comparative analysis on the bisecting k-means and the PDDP clustering algorithms. Intelligent Data Analysis, 8(4), pp. 345-362.
Schoen,H., Gayo-Avello, D., Metaxas, P., Mustafaraj, E., Strohmaier, M. & Gloor, P. (2013). The power of prediction with social media. Internet Res, 23(5), pp. 528-543.