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
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.
Schindler, M., & Domahidi, E. (2022). The computational turn in online mental health research: A systematic review. New Media & Society. https://doi.org/10.1177/14614448221122212
Zehring, M., & Domahidi, E. (2022). Thirty Years After the German Reunification—Exploring Stereotypes About East Germans on Twitter. International Journal Of Communication, 16(2022), 4029-4049. https://ijoc.org/index.php/ijoc/article/view/19010/3868
Xu, Y., Yu, J., & Löffelholz, M. (2022). Portraying the Pandemic: Analysis of Textual-Visual Frames in German News Coverage of COVID-19 on Twitter. Journalism Practice, 1–21. https://doi.org/10.1080/17512786.2022.2058063
Domahidi, E., Merkt, M., Thiersch, C., Utz, S., & Schüler, A. (2021). You Want This Job? Influence and Interplay of Self-Generated Text and Picture Cues in Professional Networking Service Profiles on Expertise Evaluation. Media Psychology, 25, 290-317. https://doi.org/10.1080/15213269.2021.1927104
Schindler, M., & Domahidi, E. (2021). The growing field of interdisciplinary research on user comments: A computational scoping review. New Media & Society 23(8), 2474-2492. https://doi.org/10.1177/1461444821994491
Banda, J. M., Tekumalla, R., Wang, G., Yu, J., Liu, T., Ding, Y., ... & Chowell, G. (2020). A large-scale COVID-19 Twitter chatter dataset for open scientific research—An international collaboration. Epidemiologia, 2(3), 315-324. https://doi.org/10.3390/epidemiologia2030024