Computational Communication Science Group

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

iStockphoto / monsitj
TU Ilmenau / Michael Reichel (ari)


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.


Wilms, L., Gerl, K., Stoll, A., & Ziegele, M.(2024).Technology acceptance and transparency demands for toxic language classification – interviews with moderators of public online discussion fora.Human–Computer Interaction.

Andrich, A., Weidner, F., & Broll, W. (2023). Zeitgebers, Time Judgments, and VR: A Constructive Replication Study. 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (pp. 1-2). IEEE.

Haim, M., Hase, V., Schindler, J., Bachl, M., & Domahidi, E. (2023). Editorial to the Special Issue:(Re) Establishing quality criteria for content analysis: A critical perspective on the field’s core method. SCM Studies in Communication and Media, 12(4), 277-288.

Binder, A., Matthes, J., Domahidi, E., & Bachl, M. (2023). Moving from Offline to Online: How COVID-19 Affected Research in the Social and Behavioral Sciences. American Behavioral Scientist, 0(0).

Jost, P., Heft, A., Buehling, K., Zehring, M., Schulze, H., Bitzmann, H., & Domahidi, E. (2023). Mapping a Dark Space: Challenges in Sampling and Classifying Non- Institutionalized Actors on Telegram. Medien & Kommunikationswissenschaft, 71(3–4), 212–229.