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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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Andersen, M. S., Kjærgaard, M. B. & Grønbæk, K. (2012). Using Extracted Behavioral Features to Improve Privacy for Shared Route Tracks. In A. U. Schmidt, G. Russello, I. Krontiris & S. Lian (Eds.), Security and Privacy in Mobile Information and Communication Systems : 4th International Conference, MobiSec 2012, Frankfurt am Main, Germany, June 25-26, 2012, Revised Selected Papers (pp. 107-118). Springer. https://doi.org/10.1007/978-3-642-33392-7_12
Pfaehler, E., Krieger, L., Assent, I., Nebelung, S., Zwanenburg, A. & Truhn, D. (2025). Using ensemble radiomic models to identify uncertainty in lung nodule classifications. European Journal of Radiology Artificial Intelligence, 4, Article 100047. https://doi.org/10.1016/j.ejrai.2025.100047
Enni, S. & Assent, I. (2018). Using Balancing Terms to Avoid Discrimination in Classification. In 2018 IEEE International Conference on Data Mining, ICDM 2018 (pp. 947-952). Article 8594925 IEEE Press. https://doi.org/10.1109/ICDM.2018.00116
Grønbæk, K., Bang, T. & Hansen, P. S. (2002). Using a Metro Map Metaphor for organizing Web-based learning resources. In P. Barker & S. Rebelsky (Eds.), Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2002 (pp. 647-652). Association for the Advancement of Computing in Education.

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