Department for Computational Social Science

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

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CSS (Computational Social Science) is located at the intersection of social science and computer science. Social phenomena are the main focus of our research. They are, however, analyzed using a variety of data types, sometimes diverging from established data sources like panel surveys, sometimes complementing those. In particular, this data includes process-generated data, such as scientific texts, patents, minutes of plenary proceedings, or messages on social media channels. The variety of data affords a variety of methods as analytical stategies.

We apply text-as-data approaches (e.g., Erhard et al. 2025), statistical models of social networks (e.g., Windzio & Heiberger 2024), or agent-based modeling (e.g.,  Kaffai & Heiberger 2021) to explain social phenomena like populism, bullying or disease spread. A specific focus of our group's work lies on the combination of computer linguistic methods with survey data (e.g., Erhard et al. 2021  or Heiberger et al. 2021). 

Recent advances of Large Language Models (LLM) provide yet another field of research for Sowi VII, as we explore the "social science fit" of LLMs, i.e, we are commited to ensuring the validity and reliability of data, and the theoretical applicability of the methods.

Sowi VII is responsible for teaching statistics at the Bachelor-level and computational methods at the Master-level. We aim to offer a modern approach to learning statistical modeling from basic understandings of data to first experiences of state-of-the-art methods. In all our classes, we emphasize practical, hands-on experiences from a social science perspective.

 

Latest Publications

  1. Erhard, L., Hanke, S., Remer, U., Falenska, A., & Heiberger, R. H. (2024). PopBERT. Detecting Populism and Its Host Ideologies in the German Bundestag. Political Analysis. https://doi.org/10.1017/pan.2024.12
  2. Hanke, S., Sicakkan, H. G., Wolleghem, P. G. V., & Heiberger, R. H. (2024). Policy Actors’ Struggle for Attention: The Role of Peer Networks in the Migration Discourse on Twitter (X). Nonprofit Policy Forum. https://doi.org/doi:10.1515/npf-2023-0126
  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. https://www.e-elgar.com/shop/gbp/research-handbook-on-digital-sociology-9781789906752.html
  4. 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. https://doi.org/10.1371/journal.pone.0269991
  5. 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. https://doi.org/10.1371/journal.pone.0269991
  6. Heiberger, R. H. (2022). Applying Machine Learning in Sociology: How to Predict Gender and Reveal Research Preferences. KZfSS Kölner Zeitschrift Für Soziologie Und Sozialpsychologie. https://doi.org/10.1007/s11577-022-00839-2
  7. 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. https://doi.org/10.1080/13183222.2022.2067724
  8. Wieczorek, O., Unger, S., Riebling, J., Erhard, L., Koß, C., & Heiberger, R. (2021). Mapping the field of psychology: Trends in research topics 1995–2015. Scientometrics. https://doi.org/10.1007/s11192-021-04069-9
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