User-Centred Social Media

Our group participates in the DFG-funded Research Training Group (Graduate School) “User-Centred Social Media” which aims at developing new models and methods for analyzing, designing and evaluating social media from a user-centred perspective. The main research fields in the RTG are modelling and understanding user behavior, social media engineering, and social media analytics. The program is an interdisciplinary endeavor located in our University department that fully integrates researchers from computer science and psychology. We contribute to the RTG with two PhD topics which lie in the areas of trustworthy recommendations based on social media sources and health-related behavioural interventions.

Modelling and Understanding User Behavior

Publications

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Jürgen Ziegler

Full Professor

Related publications

Explaining Recommendations by Means of Aspect-Based Transparent Memories

Donkers, T., Kleemann, T., & Ziegler, J. (2020). In F. Paternò & N. Oliver (Eds.), Proceedings of the 25th International Conference on Intelligent User Interfaces (pp. 166–176). The Association for Computing Machinery.

Impact of Item Consumption on Assessment of Recommendations in User Studies

Loepp, B., Donkers, T., Kleemann, T., & Ziegler, J. (2018). Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18), 49–53.

Psychological User Characteristics and Meta-Intents in a Conversational Product Advisor

Ma, Y., Kleemann, T., & Ziegler, J. (2022). Proceedings of the 9th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, 3222, 18–32.

STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous Data

Wang, C., Sahebi, S., & Torkamaan, H. (2021). In J. He, R. Unland, E. Santos Jr., X. Tao, H. Purohit, W.-J. van den Heuvel, J. Yearwood, & J. Cao (Eds.), WI-IAT ’21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 453–462). Association for Computing Machinery.

The Dual Echo Chamber: Modeling Social Media Polarization for Interventional Recommending

Donkers, T., & Ziegler, J. (2021). Fifteenth ACM Conference on Recommender Systems, 12–22.

Identifying Group-Specific Mental Models of Recommender Systems: A Novel Quantitative Approach

Kunkel, J., Ngo, P. T., Ziegler, J., & Krämer, N. (2021). In C. Ardito, R. Lanzilotti, A. Malizia, H. Petrie, A. Piccinno, G. Desolda, & K. Inkpen (Eds.), Human-Computer Interaction – INTERACT 2021: Proceedings, Part IV (Vol. 12935, pp. 383–404). Springer.

Modeling User Interaction at the Convergence of Filtering Mechanisms, Recommender Algorithms and Advisory Components

Kleemann, T., Wagner, M., Loepp, B., & Ziegler, J. (2021). Mensch Und Computer 2021 – Tagungsband, 531–543.

On the Convergence of Intelligent Decision Aids

Loepp, B. (2021). Mensch Und Computer 2021 – Workshopband.

Towards a Better Understanding of Online Influence: Differences in Twitter Communication Between Companies and Influencers

Hernandez Bocanegra, D. C., Borchert, A., Brünker, F., Shahi, G. K., & Ross, B. (2020). ACIS 2020 Proceedings, 18.

Exploring chatbot user interfaces for mood measurement: A study of validity and user experience

Torkamaan, H., & Ziegler, J. (2020). Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers, 135–138.

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