Computational Communication Science Group
Christine Wendo King'ang'i
The Computational Communication Science Group is dedicated to the analysis of digital media content and communication processes as well as the associated changes for individuals and society.
The research group operates at the combination between communication science and computer science, following a strong interdisciplinary approach. The main research interest lies in the fields of (Cognitive) Biases in Digital Media and Social Consequences of Online Media Use.
In addition to traditional methods of communication science, computational methods will be applied, improved, and evaluated.
The Computational Communication Science Group primarily investigates the usage of digital media and its effects on individuals and society. In addition to classical communication science methods, the group focuses on the application and evaluation of computational approaches in communication studies.
Cognitive and Algorithmic Biases in Digital Media
Social Consequences of Online Media Use
We offer students a sound education in communication science with close reference to how computational methods can open up insights into topics in communication and social science. This includes theoretical and practical work on methodological and algorithmic challenges that arise in the analysis of digital data and e.g., social media. In doing so, we emphasize high-quality, internationally oriented teaching, including English-language courses and degree programs.
Urman, A., Domahidi, E., Gruber, J. B., Jovančević, A., Maier, M., Gërguri, D., Mazak, J., & Velden, M. (2026). Gender Representation in Large Language Models: A Cross-Linguistic and Cross-Model Analysis. Computational Communication Research, 8(2), 1-39. https://doi.org/10.5117/CCR2026.2.11.URMA
Niemann-Lenz, J., Schatto-Eckrodt, T., Domahidi, E., & Mahrt, M. (2026). Introduction to the Special Issue “The Datafication of Communication–New Methodological Approaches and Challenges”. Medien & Kommunikationswissenschaft, 74(1), 3-7. https://doi.org/10.5771/1615-634X-2026-1-3
Yu, J., Domahidi, E., Gamannossi degl’Innocenti, D., & Zollo, F. (2026). Crisis, country, and party lines: Politicians’ misinformation behavior and public engagement. Journal of Computational Social Science, 9, Article 21. https://doi.org/10.1007/s42001-025-00447-y
Yu, J., Domahidi, E., Johansson, B., & Steinmetz, N. (2025). Multilevel government crisis communication on social media: A comparative and longitudinal approach. International Communication Gazette, 0(0). https://doi.org/10.1177/17480485251404809
Schulze, H., Buehling, K., & Zehring, M. (2025). The Telegram COVID-19 Protest Dataset 2020-2022. Computational Communication Research, 7(1), 1. https://doi.org/10.5117/CCR2025.1.9.SCHU
Christine Wendo King'ang'i, M.A.
Aliya Andrich, M.A.
Felipe Barreto de Souza Martins , M.A.
Dr. Anke Stoll
