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Agreeing to Disagree? The Twitter Debate on Catalonia’s Independence

Elections
National Identity
Social Media
Joan Balcells
Universitat Oberta de Catalunya
Joan Balcells
Universitat Oberta de Catalunya
Albert Padro-Solanet
Open University of Catalonia

Abstract

Popular social media like Twitter have become a massive and powerful channel for building, expressing and sharing public opinions. There is, however, much discussion among scholars about the kind of communication which Twitter enables and its effects on public deliberation (e.g. opinion polarization and radicalization, quality of conversations, etc.). Social media are often regarded as fostering patterns of opinion “homophily”, connecting individuals with similar ideas and beliefs, and creating homogeneous and separate opinion communities. Nevertheless, Twitter has some special features which can encourage dialogue across different groups and political lines, allowing individuals to freely engage in conversation with any other user and thereby be potentially exposed to opposing views. In terms of democratic deliberation, these two different hypotheses ultimately have serious implications for both the emergence and quality of public opinion. This paper tries to provide a better understanding of how public deliberation actually takes place in social media. Using the debate on the independence of Catalonia as a case study, the aim is to analyze Twitter interactions empirically between the pro and against independence supporters in the context of the elections to the Catalan Parliament 2015. Considered by the pro-independence supporters as a de facto referendum on secession, these elections saw an unprecedented turnout (in line with the referendum experiences of Scotland and Québec), and took place after a long period of massive grassroots mobilizations and protests against the status quo. This debate is a clear example of a highly controversial, vibrant and contested issue over a matter involving a whole political community. Methodologically, the paper relies on the content analysis of a random collection of tweets captured in streaming, and limited by certain parameters to focus on relevant samples. The case provides a promising setting for testing both the level and quality of dialogue exchanges in Twitter between opposing sides in a highly polarized political debate.