Speaker: Karsten Donnay (University of Konstanz)
Venue: The Alan Turing Institute
Research on conflict suggests that perceived inequalities are key mobilizing factors. Understanding the exact mechanisms by which perceived inequalities become salient is therefore fundamental to our understanding of how political instability arises. Empirical analysis to date typically rely on structural indicators of inequality which are often highly aggregate, unspecific measures that do not reflect individual experiences. Moreover, these indicators do not capture perceived inequalities, which may or may not reflect actual structural inequalities, and thus fail to capture the feelings of deprivation and frustration that drive mobilization. Surveys on the other hand can be used to elicit perceptual measurements but are typically limited in spatial and temporal specificity. This talk examines recent approaches to fine-grained measurements of perceptions of inequalities that aim to overcome these limitations using data gleamed from artifacts of human interactions. It further highlights the corresponding contributions of data science to the quantitative study of political instability at the level of individual events or incidents.
Karsten Donnay is an Assistant Professor of Computational Social Science at the Center for Data and Methods in the Department of Politics and Public Administration at the University of Konstanz (Germany). Previously, he was a postdoctoral researcher at the Department of International Relations & Political Science at the Graduate Institute of International and Development Studies in Geneva (Switzerland), and the National Consortium for the Study of Terrorism and Responses to Terrorism (START) at the University of Maryland (USA). In his research, he combines a substantive interest in political science – studying social processes such as urban violence and crime, conflict and terrorism, or social influence through traditional and new media – with the development and refinement of quantitative methodologies for social science research