Arnim Bleier

When populists become popular: comparing Facebook use by the right-wing movement Pegida and German political parties

ABSTRACT Previous research has acknowledged the use of social media in political communication by right-wing populist parties and politicians. Less is known, however, about its pivotal role for right-wing social movements which rely on personalized messages to mobilize supporters and challenge the mainstream party system. This paper analyzes online political communication by the right-wing populist movement Pegida and German political parties. We investigate to which extent parties attract supporters of Pegida, to which extent they address topics similar to Pegida and whether their topic use has become more similar over a period of almost two years. The empirical analysis is based on Facebook posts by main accounts and individual representatives of these political groups. We first show that there are considerable overlaps in the audiences of Pegida and the new challenger in the party system, AfD. Then we use topic models to characterize topic use by party and surveyed crowdworkers to which extent they perceive the identified topics as populist communication. The results show that while Pegida and AfD talk about rather unique topics and smaller parties engage to varying degrees with the topics populists emphasize, the two governing parties CDU and SPD clearly deemphasize those. Overall, the findings indicate that the considerable attention devoted to populist actors and shifts in public opinion due to the refugee crisis have left only moderate marks in political communication within the mainstream party system.

The Polylingual Labeled Topic Model

In this paper, we present the Polylingual Labeled Topic Model, a model which combines the characteristics of the existing Polylingual Topic Model and Labeled LDA. The model accounts for multiple languages with separate topic distributions for each language while restricting the permitted topics of a document to a set of predefined labels. We explore the properties of the model in a two-language setting on a dataset from the social science domain. Our experiments show that our model outperforms LDA and Labeled LDA in terms of their held-out perplexity and that it produces semantically coherent topics which are well interpretable by human subjects.

Election Campaigning on Social Media: Politicians, Audiences, and the Mediation of Political Communication on Facebook and Twitter

Although considerable research has concentrated on online campaigning, it is still unclear how politicians use different social media platforms in political communication. Focusing on the German federal election campaign 2013, this article investigates whether election candidates address the topics most important to the mass audience and to which extent their communication is shaped by the characteristics of Facebook and Twitter. Based on open-ended responses from a representative survey conducted during the election campaign, we train a human-interpretable Bayesian language model to identify political topics. Applying the model to social media messages of candidates and their direct audiences, we find that both prioritize different topics than the mass audience. The analysis also shows that politicians use Facebook and Twitter for different purposes. We relate the various findings to the mediation of political communication on social media induced by the particular characteristics of audiences and sociotechnical environments.

Systematically Monitoring Social Media: the case of the German federal election 2017

=3079927 Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Predicting elections with Twitter: What 140 characters reveal about political sentiment. In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (pp. 178–185). Palo Alto, CA: AAAI Press. Vaccari, C., Chadwick, A., & O’Loughlin, B. (2015). Dual screening the political: Media events, social media, and citizen engagement. Journal of Communication, 65 (6), 1041–1061. doi: 10.1111/jcom.12187 Vergeer, M., Hermans, L., & Sams, S. (2013). Online social networks and microblogging in political campaigning: The exploration of a new campaign tool and a new campaign style. Party Politics, 19 (3), 477–501. doi: 10.1177/1354068811407580 Williams, C. B., & Gulati, G. J. (2013). Social networks in political campaigns: Facebook and the congressional elections of 2006 and 2008. New Media & Society , 15 (1), 52–71. doi: 10.1177/1461444812457332 Yang, J., & Kim, Y. M. (2017). Equalization or normalization? Voter–candidate engagement on Twitter in the 2010 U.S. midterm elections. Journal of Information Technology & Politics, 14 (3), 232–247. doi: 10.1080/ 19331681.2017.1338174

Topic model tutorial: A basic introduction on latent dirichlet allocation and extensions for web scientists

In this tutorial, we teach the intuition and the assumptions behind topic models. Topic models explain the co-occurrences of words in documents by extracting sets of semantically related words, called topics. These topics are semantically coherent and can be interpreted by humans. Starting with the most popular topic model, Latent Dirichlet Allocation (LDA), we explain the fundamental concepts of probabilistic topic modeling. We organise our tutorial as follows: After a general introduction, we will enable participants to develop an intuition for the underlying concepts of probabilistic topic models. Building on this intuition, we cover the technical foundations of topic models, including graphical models and Gibbs sampling. We conclude the tutorial with an overview on the most relevant adaptions and extensions of LDA.

A System for Probabilistic Linking of Thesauri and Classification Systems

This paper presents a system which creates and visualizes probabilistic semantic links between concepts in a thesaurus and classes in a classification system. For creating the links, we build on the Polylingual Labeled Topic Model (PLL-TM) (Posch et al., in KI 2015: advances in artificial intelligence, 2015). PLL-TM identifies probable thesaurus descriptors for each class in the classification system by using information from the natural language text of documents, their assigned thesaurus descriptors and their designated classes. The links are then presented to users of the system in an interactive visualization, providing them with an automatically generated overview of the relations between the thesaurus and the classification system.