589264 Master Thesis Anna L
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EMOTIONS IN POPULIST SOCIAL MEDIA CAMPAIGNING An Empirical Analysis of the Tweeting Behavior of the Alternative für Deutschland Master Thesis Brand and Communications Management 79 Pages (168,102 characters) Anna Laube Supervisor Tore Kristensen September 17, 2018 Anna Laube EMOTIONS IN POPULIST SOCIAL MEDIA CAMPAIGNING An Empirical Analysis of the Tweeting Behavior of the Alternative für Deutschland 1 Table of Content Table of Content 1 List of Figures 3 List of Tables 4 List of Appendices 4 Abstract 5 Glossary 6 1. INTRODUCTION 7 1.1 Problem Statement Formulation 8 1.3 Structure 10 II. LITERATURE REVIEW 12 Theoretical Foundation 12 2.1 The theory of the “Rational Citizen” 12 2.2 Emotions and their influence on political behavior 13 2.3 The theory of Affective Intelligence 14 2.3.1 The Disposition System 15 2.3.2 The Surveillance System 16 2.4 The impacts of anger and anxiety on the voter 18 3. Political communication, social media and populism in the 21st century 20 3.1 Political campaigning on social media 20 3.2 Twitter as an element of political discourse 20 3.3 Polarized politics: Social Media and confirmation bias inflamed in echo chambers 22 3.4 Twitter, emotions and their influence on information sharing 23 3.5 Populist campaigning, social media and their use of emotions 24 4. How to analyze sentiment and emotions in texts 26 4.1 Scientific sentiment analysis tools 27 5. Hypothesis and Framework 29 5.1 Hypothesis Formulation 29 5.2 Theoretical Framework 32 6. Case Study: The right-wing populist party AfD in Germany 34 2 6.1 The Alternative für Deutschland (AfD) 34 6.2 The voter base of the Alternative für Deutschland 34 III. METHODOLOGY 37 7. Methodological Considerations 37 8. Research Design, Data and Sample 41 8.1 The Case: The Alternative für Deutschland 41 8.2 Approach Synopsis 41 8.3 Phase 1 & 2: Data extraction and pre-processing 42 8.4 Phase 3: Data Analysis 44 8.4.1 Sentiment Analysis 44 8.4.2 Content and Media analysis 45 8.4.3 Regression Analysis 49 8.4.3.1 Variables 49 8.4.3.2 Estimation Method 50 8.5 Methodological Limitations 51 IV. RESULTS 53 9.1 The AfD and their use of anger and fear on Twitter 53 9.2 The AfD and their distribution of news media 55 9.3 The content of AfD tweets 58 9.4 Anger, fear and blame attribution in populist tweets 63 9.5 The AfD and the influence of anger and fear on sharing behavior 65 V. DISCUSSION & CONCLUSION 74 10.1 Discussion 74 10.2 Conclusion 76 10.3 Limitations 77 10.4 Implications for future research 78 11. Appendix 80 12. References 86 3 List of Figures Figure 1: The three research fields the thesis draws on .................................................................................................. 10 Figure 2: Structure of the thesis ...................................................................................................................................... 11 Figure 3: Topical structure of the literature review. ...................................................................................................... 12 Figure 4: Affective Intelligence Theory .......................................................................................................................... 19 Figure 5: Influence of anger and fear appeals on the voter ............................................................................................ 30 Figure 6: Theoretical framework of the thesis. .............................................................................................................. 33 Figure 7: Approach overview of AfD Twitter Analysis ................................................................................................ 42 Figure 8: Top 30 most tweeted external media websites by selected AfD accounts on Twitter ................................. 56 Figure 9: Word cloud visualization of top 100 most used hashtags in tweets .............................................................. 62 Figure 10: Daily Timelines of independent variables Anger and Anxiety .................................................................... 65 Figure 11: Histograms of dependent variable and explanatoryt variables .................................................................... 67 Figure 12: Scatter diagrams Retweet Count vs. Anger resp. Anxiety ........................................................................... 69 4 List of Tables Table 1: Methodological research process for this thesis based on the research onion ................................................ 40 Table 2: Official AfD Twitter Accounts (August 2018). ............................................................................................. 43 Table 3: Party Member Twitter Accounts. (August 2018). ......................................................................................... 43 Table 4: Categorization of AfD tweets according to content and field. ...................................................................... 46 Table 5: Summary Statistics of Variables used in the Regression Analysis .................................................................. 50 Table 6: AfD tweets charged with anger and anxiety .................................................................................................... 53 Table 7: Top 3 AfD tweets charged with anxiety. ......................................................................................................... 54 Table 8: Top 3 AfD tweets charged with anger. ............................................................................................................ 55 Table 9: AfD tweeting activity classified according to Original, Retweets and URLs. ............................................... 56 Table 10: 50 Most shared media sites by AfD Twitter accounts. ................................................................................. 57 Table 11: Analysis of AfD tweets according to text content ........................................................................................ 59 Table 12: AfD tweet’s messages classified to attacked groups ...................................................................................... 60 Table 13: Most used hashtags in the tweets (Rank 1-20) .............................................................................................. 61 Table 14: AfD tweet’s messages classified to “attacked” groups linked with emotions .............................................. 63 Table 15: Correlation Matrix of Independent Variables used in the Regression Analyses ......................................... 68 Table 16: Negative Binomial Regression Results .......................................................................................................... 69 Table 17: Wald Test Results for Coefficients of Anger and Anxiety ........................................................................... 71 Table 18: Top Most retweeted tweets in corpus. ......................................................................................................... 72 Table 19: Summary of tests of the hypotheses .............................................................................................................. 73 List of Appendices Appendix 1: Final result of the 2017 Bundestag election in Germany, second vote as a percentage of the AfD. ...... 80 Appendix 2: Shift of votes in Bundestagswahl from other parties to the AfD compared from 2013 to 2017. ......... 81 Appendix 3: Research Onion ......................................................................................................................................... 82 Appendix 4: External news media websites used in the corpus of the AfD tweets. ..................................................... 83 5 Abstract Politicians, when trying to convince voters usually appeal to emotions (Brader, 2005). Especially populists often work with negative emotions in their campaigns to affect rational mechanisms of decision making to get electoral support (Hameleers et al., 2017). This thesis studies the effects of anger and fear in populist communication on sharing behavior on social media. Based on the theory of Affective Intelligence the author proposes that tweets by political accounts that contain anger will result in more retweets than those containing fear. The empirical analysis was done through the collection of a data set of 1461 tweets taken from 17 official Twitter accounts by the Alternative für Deutschland (AfD) and their party members during the period of July 17 until August 9, 2018. By applying sentiment analysis techniques the author found evidence for the intensified use of both anger and fear in the tweets and its positive impact on retweet quantity. Evidence for a higher impact of anger than fear on sharing behavior could, however, not be confirmed. Furthermore, an additional content analysis was done to categorize the topics of the tweets and the external sources of information being shared. The results confirm the assumptions that populist parties predominantly criticize elites and out-groups, and often refer to information from right-leaning media platforms. These findings help to explain the recent success of populist parties. 6 Glossary Term Explanation AIT Affective Intelligence Theory AfD Alternative für Deutschland (Alternative for Germany) API Application Programming Interface. A standardized programming interface for accessing data or algorithms from a website Bundestag German federal parliament CDU Christlich Demokratische Union (Christian Democratic Union of Germany) CSU Christlich