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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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Mathisen, A. & Grønbæk, K. (2018). Clear Visual Separation of Temporal Event Sequences. In 2017 IEEE Visualization in Data Science, VDS 2017 (pp. 7-14). Article 8573439 IEEE. https://doi.org/10.1109/VDS.2017.8573439
Mansour, H., Presser, M. & Bjerrum, T. C. B. (2018). Comparison of Seven Business Model Innovation Tools for IoT Ecosystems. 68-73. Paper presented at IEEE Internet of Things: 2018 IEEE 4th World Forum on Internet of Things, Singapore. https://doi.org/10.1109/WF-IoT.2018.8355219
Neerbek, J., Assent, I. & Dolog, P. (2018). Detecting Complex Sensitive Information via Phrase Structure in Recursive Neural Networks. In D. Phung, V. S. Tseng, G. I. Webb, B. Ho, M. Ganji & L. Rashidi (Eds.), Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings: PAKDD '18 (Vol. 10939, pp. 373-385). Springer VS. https://doi.org/10.1007/978-3-319-93040-4_30

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