Political candidates in infotainment programmes and their emotional effects on Twitter: An analysis of the 2015 Spanish general elections pre-campaign season

This is an Accepted Manuscript of an article published by Taylor & Francis in Contemporary Social Science on 2019, available online: http://www.tandfonline.com/10.1080/21582041.2017.1367833.[EN] The infotainment format offers candidates an informal setting to show a more personal side of themselves to the electorate, opening themselves up to potential voters. An example of media hybridisation, social networks users can immediately comment on infotainment television programmes, a process known as second screening. These second screeners tend to be especially active in politics. This paper analyses the immediate emotional reaction of these users as they watch infotainment programmes that air during the campaign or pre-campaign seasons and feature political candidates as guests. We have confirmed that second screeners react more emotionally towards the candidate when his or her party is mentioned, and less emotionally when the host displays an aggressive attitude through his or her non-verbal communication. When issues related to the candidate¿s personal lives are discussed, users¿ emotional reactions improve slightly. The relevance of this research stems from the fact that we are witnessing the consolidation of a politics that increasingly strays from ideological questions, and instead focuses on more emotional and personal issues.This work was supported by the Ministerio de Economia y Competitividad under Grants CSO2013-43960-R and CSO2016-77331-C2-1-R.Baviera, T.; Peris, À.; Cano-Orón, L. (2019). Political candidates in infotainment programmes and their emotional effects on Twitter: An analysis of the 2015 Spanish general elections pre-campaign season. Contemporary Social Science. 14(1):144-156. https://doi.org/10.1080/21582041.2017.1367833S144156141Baum, M. A., & Jamison, A. S. (2006). TheOprahEffect: How Soft News Helps Inattentive Citizens Vote Consistently. The Journal of Politics, 68(4), 946-959. doi:10.1111/j.1468-2508.2006.00482.xBravo-Marquez, F., Mendoza, M., & Poblete, B. (2014). Meta-level sentiment models for big social data analysis. Knowledge-Based Systems, 69, 86-99. doi:10.1016/j.knosys.2014.05.016Casero-Ripollés, A., Feenstra, R. A., & Tormey, S. (2016). Old and New Media Logics in an Electoral Campaign. The International Journal of Press/Politics, 21(3), 378-397. doi:10.1177/1940161216645340Ceron, A., & Splendore, S. (2016). From contents to comments: Social TV and perceived pluralism in political talk shows. New Media & Society, 20(2), 659-675. doi:10.1177/1461444816668187Chadwick, A. (2013). The Hybrid Media System. doi:10.1093/acprof:oso/9780199759477.001.0001Dang-Xuan, L., Stieglitz, S., Wladarsch, J., & Neuberger, C. (2013). AN INVESTIGATION OF INFLUENTIALS AND THE ROLE OF SENTIMENT IN POLITICAL COMMUNICATION ON TWITTER DURING ELECTION PERIODS. Information, Communication & Society, 16(5), 795-825. doi:10.1080/1369118x.2013.783608Giglietto, F., & Selva, D. (2014). Second Screen and Participation: A Content Analysis on a Full Season Dataset of Tweets. Journal of Communication, 64(2), 260-277. doi:10.1111/jcom.12085Grabe, M. E., & Bucy, E. P. (2009). Image Bite Politics. doi:10.1093/acprof:oso/9780195372076.001.0001Guo, L., & Vargo, C. (2015). The Power of Message Networks: A Big-Data Analysis of the Network Agenda Setting Model and Issue Ownership. Mass Communication and Society, 18(5), 557-576. doi:10.1080/15205436.2015.1045300Harrington, S. (2008). Popular news in the 21st century Time for a new critical approach? Journalism: Theory, Practice & Criticism, 9(3), 266-284. doi:10.1177/1464884907089008López-Rico, C.-M., & Peris-Blanes, À. (2017). Agenda e imagen de los candidatos de las elecciones generales de 2015 en España en programas televisivos de infoentretenimiento. El Profesional de la Información, 26(4), 611. doi:10.3145/epi.2017.jul.05Maruyama, M., Robertson, S. P., Douglas, S., Raine, R., & Semaan, B. (2017). Social Watching a Civic Broadcast. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. doi:10.1145/2998181.2998340Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113. doi:10.1016/j.asej.2014.04.011Saif, H., He, Y., & Alani, H. (2012). Semantic Sentiment Analysis of Twitter. Lecture Notes in Computer Science, 508-524. doi:10.1007/978-3-642-35176-1_32Shah, D. V., Hanna, A., Bucy, E. P., Lassen, D. S., Van Thomme, J., Bialik, K., … Pevehouse, J. C. W. (2016). Dual Screening During Presidential Debates. American Behavioral Scientist, 60(14), 1816-1843. doi:10.1177/0002764216676245Sullivan, D. G., & Masters, R. D. (1988). «Happy Warriors»: Leaders’ Facial Displays, Viewers’ Emotions, and Political Support. American Journal of Political Science, 32(2), 345. doi:10.2307/2111127Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558. doi:10.1002/asi.21416Vergeer, M., & Franses, P. H. (2015). Live audience responses to live televised election debates: time series analysis of issue salience and party salience on audience behavior. Information, Communication & Society, 19(10), 1390-1410. doi:10.1080/1369118x.2015.1093526Vilares, D., Thelwall, M., & Alonso, M. A. (2015). The megaphone of the people? Spanish SentiStrength for real-time analysis of political tweets. Journal of Information Science, 41(6), 799-813. doi:10.1177/0165551515598926Wohn, D. Y., & Na, E.-K. (2011). Tweeting about TV: Sharing television viewing experiences via social media message streams. First Monday. doi:10.5210/fm.v16i3.336