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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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Tylén, K., Svane Qvist, A., Foss Kjeldsen, R., Gonzalez de la Higuera Rojo, S., Heimann, K., Fay, N., Johannsen, N. N., Riede, F., Lombard, M. & Fusaroli, R. (2023). Reconstructing early human symbolic evolution using transmission experiments. In M. Goldwater, F. K. Anggoro, B. K. Hayes & D. C. Ong (Eds.), Proceedings of the 45th Annual Conference of the Cognitive Science Society https://escholarship.org/uc/item/2mh95711
Tommasel, A., Pablos Sarabia, R. & Assent, I. (2023). Re2Dan: Retrieval of Medical Documents for e-Health in Danish. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023 (pp. 1208-1211). Association for Computing Machinery. https://doi.org/10.1145/3604915.3610655
Quercia, A., Morrison, A., Scharr, H. & Assent, I. (2023). SGD Biased towards Early Important Samples for Efficient Training. In G. Chen, L. Khan, X. Gao, M. Qiu, W. Pedrycz & X. Wu (Eds.), IEEE International Conference on Data Mining, ICDM 2023, Shanghai, China, December 1-4, 2023 (pp. 1289-1294). IEEE. https://doi.org/10.1109/ICDM58522.2023.00163
Ping Huang, M., Schreibman, S., Papadopoulos, C., Scholger, W. & Kuzman Šlogar, K. (2023). Social Justice in the DigitalHumanities Community of Practice. Abstract from Digital Humanities 2023 (ADHO), Graz, Austria.

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