Mini Online Movement and Spain’s Revived Nationalism: the Case of #Spexit

Abstract The Catalonia independence problem has long been Spain’s political Achilles’ hell. On December 19th, 2019, the European Court of Justice ruled imprisoned Catalan pro-independence politician Oriol Junqueras’ parliamentary immunity, which has triggered the hashtag #Spexit being one of the hottest trendings on Twitter for several weeks. Similar to #Brexit, #Spexit demanded Spain to quit European Union. Scholars believe Twitter an important source of information about political issues, reflecting and influencing public mood (Grčar, Cherepnalkoski, Mozetič, & Kralj Novak, 2017), Bastos and Mercea (2018) found a significant incidence of nationalist sentiments and economic views expressed on Twitter through #Brexit. Considering of the results of November 2019 Spanish general elections, at the level of the state, far-right party Vox became the third largest force in the lower house with 52 seats, we suppose the online discourse of #Spexit may also contain nationalist moods. But as the hashtag was triggered by Catalan independence problem and European Union’s justice, given that in Catalonia left wing parties won the majority in the last elections, the reaction of Catalan people and international community to #Spexit may be different. Hence, this paper aims to analyze the online discourse of #Spexit, identifying the difference of the main discussion topics between Spanish speaking communities, Catalan speaking communities and English speaking communities. The proposed research methods are mainly based on Latent Dirichlet Allocation (LDA) (Blei, 2012; Blei, Ng, & Jordan, 2003) topic modeling method, which allows us to discover and illustrate latent topics from selected text corpora. Reference Bastos, M., & Mercea, D. (2018). Parametrizing Brexit: mapping Twitter political space to parliamentary constituencies. Information, Communication & Society, 21(7), 921–939. https://doi.org/10.1080/1369118X.2018.1433224 Blei, D. M. (2012). Probabilistic topic models. In Communications of the ACM (Vol. 55, pp. 77–84). https://doi.org/10.1145/2133806.2133826 Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(Jan), 993–1022. Retrieved from http://www.jmlr.org/papers/v3/blei03a.html Grčar, M., Cherepnalkoski, D., Mozetič, I., & Kralj Novak, P. (2017). Stance and influence of Twitter users regarding the Brexit referendum. Computational Social Networks, 4(1). https://doi.org/10.1186/s40649-017-0042-6