Data Theory: Interpretive Sociology and Computational Methods
The ongoing and intensifying datafication of our societies poses huge challenges as well as opportunities for social science to rethink core elements of its research enterprise. Prominently, there is a pressing need to move beyond the long-standing qualitative/quantitative divide. This talk is an argument towards developing a critical science of data, by bringing together the interpretive theoretical and ethical sensibilities of social science with the predictive and prognostic powers of data science and computational methods.
Simon Lindgren argues that the renegotiation of theories and research methods that must be made in order for them to be more relevant and useful, can be fruitfully understood through the metaphor of hacking social science: developing creative ways of exploiting existing tools in alternative and unexpected ways to solve problems.
Simon Lindgren is Professor of Sociology at Umeå University. His research is about the relationship between digital technologies and society. Lindgren studies the transformative role of digital communication technologies (internet and social media), and the consequences of datafication, algorithms and AI, with a particular focus on politics and power relations. He uses combinations of methods from computational social science and network science, and analytical frameworks from interpretive sociology and critical theory. Lindgren’s books include “Data Theory” (2020), “Digital Media and Society” (2017), and “New Noise” (2013). More information can be found at www.simonlindgren.com.
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