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DATALAB

  • Center for Digital Social Research

About DATALAB


DATALAB – Center for Digital Social Research is an interdisciplinary research center established in 2016 under the School of Communication and Culture at Aarhus University, Denmark. Led by Professor Anja Bechmann, the center conducts forefront research on algorithmic communication platforms and citizens, collectives, and populations in datafied societies. The center focuses on AI-powered platforms, associated socio-technical actors, patterns of agency and influence, and effects on communication flows. 

DATALAB hosts fundamental research projects that are theoretically based, empirically tested, and often including large-scale trace data. Our research has also contributed to informing decisions on policy and regulatory frameworks (e.g. in relation to platforms and AI). The projects at the center utilize a wide range of methods from computational social science often combining learning models with experiments, surveys, digital ethnography, and interviews.   

DATALAB researchers and projects share a vision and fundamental interest in creating novel methods and reinterpreting theories to better understand platforms and the modern techno-social fabric. Our projects provide novel knowledge on algorithmic and data-driven agency and societies with a particular sensitivity towards principles of democracy, human rights, and ethics.


Contact


Anja Bechmann

Center Director
anjabechmann@cc.au.dk
+45 5133 5138





Recent Publications


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Enevoldsen, K., Jensen, K. N., Kostkan, J., Szabo, B., Kardos, M., Vad, K., Heinsen, J., Núñez, A. B., Barmina, G., Nielse, J., Larsen, R., Vahlstrup, P. B., Møldrup-Dalum, P., Elliot, D., Galke, L., Schneider-Kamp, P. & Nielbo, K. L. (2025). Dynaword: From One-shot to Continuously Developed Datasets. ArXiv. https://arxiv.org/abs/2508.02271
Quercia, A., Yildiz, E., Cao, Z., Krajsek, K., Morrison, A., Assent, I. & Scharr, H. (2025). Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks. In Proceedings - 2025 IEEE Winter Conference on Applications of Computer Vision, WACV 2025 (pp. 6435-6445). IEEE. https://doi.org/10.1109/WACV61041.2025.00627
Krieger, L., Beer, A., Matthews, P., Thiesson, A. M. & Assent, I. (2025). FAIRDEN: FAIR DENSITY-BASED CLUSTERING. In 13th International Conference on Learning Representations, ICLR 2025 (pp. 19570-19589). International Conference on Learning Representations, ICLR.

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