Department for Computational Social Science

Head of Department: Prof. Dr. Raphael Heiberger (SOWI VII)

CSS (computational social science) is located at the interface of social science and computer science. Social phenomena are therefore the main focus of our research. They are, however, analyzed using "new" data types.
In particular, this includes process-generated data, such as minutes of plenary proceedings, scientific texts and collaborations, or messages on social media channels (such as tweets). Methodologically, CSS combines inferential statistics with iterative calculation rules (e.g. algorithms), or Bayesian probability classifications. These can then be used to identify topics within large quantities of texts, to predict economic growth, or to investigate the coevolution of social relationships and attributes.
In addition to data issues and the intertwined development of innovative methods, our group also focuses on their "social science fit". This means, we are commited to ensuring the validity and reliability of data and also want to ensure the theoretical applicability of the methods.

Latest Publications

  1. Dialogische Bürgerinnen- und Bürgerbeteiligung in Baden-Württemberg. (2023). In A. Vetter & U. Remer (Eds.), Politik gestalten - Kommunikation, Deliberation und Partizipation bei politisch relevanten Projekten. Springre Fachmedien.
  2. Remer, U. (2023). Wahlen, Abstimmungen und dialogorientierte Beteiligung in den Gemeinden Baden-Württembergs. In A. Vettter & U. Remer (Eds.), Politik gestalten - Kommunikation, Deliberation und Partizipation bei politisch relevanten Projekten (pp. 13–33). Springer Fachmedien.
  3. Erhard, L., & Heiberger, R. (2023). Regression and Machine Learning. In J. Skopek (Ed.), Research Handbook on Digital Sociology (pp. 129--144). Edward Elgar Publishing.
  4. Vetter, A., & Remer, U. (2023). Politische Partizipation: Beteiligung am Beispiel von Wahlen und politischem Protest im Vergleich (L. H. Anders & D. Riese, Eds.; pp. 45–67). Springer Fachmedien.
  5. Vetter, A., & Remer, U. (2023). Dialogischer Bürgerinnen- und Bürgerbeteiligung in Baden-Württemberg. Eine Einleitung. In A. Vetter & U. Remer (Eds.), Dialogische Bürgerinnen- und Bürgerbeteiligung in Baden-Württemberg (pp. 1–11). Springer Fachmedien Wiesbaden.
  6. Unger, S., Erhard, L., Wieczorek, O., Koß, C., Riebling, J., & Heiberger, R. H. (2022). Benefits and detriments of interdisciplinarity on early career scientists’ performance. An author-level approach for U.S. physicists and psychologists. PLOS ONE, 17(6), Article 6.
  7. Heiberger, R. H. (2022). Applying Machine Learning in Sociology: How to Predict Gender and Reveal Research Preferences. KZfSS Kölner Zeitschrift Für Soziologie Und Sozialpsychologie.
  8. Windzio, M., & Heiberger, R. H. (2022). The Social Ecology of Intergenerational Closure in School Class Networks. Socio-spatial Conditions of Parents’ Norm Generation and Their Effects on Students’ Interpersonal Conflicts. Social Networks.
  9. Windzio, M., & Heiberger, R. (2022). Talking About Education: How Topics Vary Between International Organizations. In K. Martens & M. Windzio (Eds.), Global Pathways to Education : Cultural Spheres, Networks, and International Organizations (pp. 239--266). Springer International Publishing.
  10. Sicakkan, H. G., & Heiberger, R. H. (2022). Between Europeanism and Nativism: Exploring a Cleavage Model of European Public Sphere in Social Media. Javnost - The Public, 0(0), Article 0.
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