معرفی
Erdem Yörük is a Professor in Sociology at Koç University, with additional affiliations as Associate Member at the University of Oxford's Department of Social Policy and Intervention, and Affiliated Faculty at the Ford Institute of Human Security (University of Pittsburgh, Central European University). As Director of the Center for Computational Social Sciences at Koç University, he leads major research initiatives including the ERC-funded projects Emerging Welfare and Politus, along with the H2020 Social Comquant project.
- Ph.D. in Sociology from Johns Hopkins University (2012)
- M.A. in Sociology from Johns Hopkins University (2009)
- M.A. in Sociology from Boğaziçi University (2006)
- B.Sc. in Electrical and Electronics Engineering from Boğaziçi University (2002)
His research integrates Computational Social Sciences with Political Sociology to analyze the interplay between Social Movements and Welfare Policy. Current work focuses on creating cross-national datasets (Global Welfare Dataset (GLOW) and Global Contentious Politics Database (GLOCON)) to explore how governments utilize social assistance as both Mobilization and Containment mechanisms in response to grassroots political activity.
Major findings from his publications in journals like World Development, Governance, and Politics & Society demonstrate that Emerging Market Economies have developed distinct Populist Welfare State Regimes through interactions between Structural Pressures, Institutional Frameworks, and Political Agency. Notable among these is his 2022 book The Politics of the Welfare State in Turkey (University of Michigan Press), which presents a political explanation for Turkey's shift from employment-based social security to poverty-targeted assistance.
He has organized EU-funded Training Workshops on topics including Social Media Data Research, Network Analysis with R, and Digital Trace Data applications. His methodological contributions span Random Sampling techniques for protest event coding, Multilingual Annotation protocols, and Machine Learning Integration with expert rule systems.
