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Recently, the availability of textual data has increased massively. Parliamentary records, party manifestos, media articles and social media contributions are accessible and there are numerous political science research questions that can be answered analysing these data.

Within the context of policy analysis, we will focus on the policy cycle stages of agenda setting and policy formulation. The seminar uses data from party manifestos, media debates and social media discussions as examples of analysis. Drawing on theorical approaches such as salience theory and issue ownership, we will analyse what topics parties formulate in their election manifestos over the years and how media debates echo elections.

The course introduces students to the quantitative analysis of textual data stemming from political documents. It covers most widely used methods for the empirical analysis of textual data from data pre-processing stages to the interpretation of findings. The course establishes theoretical knowledge and uses it in practical hands on sessions. We will use the R environment for the practical sessions. No previous knowledge of the programming language R is needed, as the course covers an introductory session. Everyone needs to install R and R-Studio on their own computer and use the software actively for class assignments and the project.

• Jacobi, Carina, Wouter van Atteveldt, and Kasper Welbers. 2016. ‘Quantitative Analysis of Large Amounts of Journalistic Texts Using Topic Modelling’. Digital Journalism 4 (1): 89–106. https://doi.org/10.1080/21670811.2015.1093271.
• Grimmer, Justin, and Brandon M. Steward. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3):267–297, 2013.
• Gemenis, Kostas. 2013. ‘What to Do (and Not to Do) with the Comparative Manifestos Project Data’. Political Studies 61: 3–23. https://doi.org/10.1111/1467-9248.12015.
• Merz, Nicolas, Sven Regel, and Jirka Lewandowski. 2016. ‘The Manifesto Corpus: A New Resource for Research on Political Parties and Quantitative Text Analysis’. Research & Politics 3 (2): 2053168016643346. https://doi.org/10.1177/2053168016643346.
• Silge, Julia, and David Robinson. Text mining with R: A tidy approach. O'Reilly, 2017. Online available at: https://www.tidytextmining.com

The course consists of self-study using prepared online tutorials and quizzes as well as five online life sessions. The participation in the last life session includes a presentation and is mandatory for everyone to receive ECTS credits. The course uses the Moodle platform with additional information and resources for individual learning. Furthermore, online appointments can be made for specific questions.

Examination: Students are required to present an own small research project, which will be developed throughout the course (in English or German). The course finishes with an oral examination (in English or German) at the end of the semester.