Networks, Inequality and Polarization
Why do inequality and polarization persist even when individuals face similar formal opportunities? A central hypothesis is that relational structure, not only individual traits, shapes outcomes. This line models schools, families, neighborhoods, and online ties as interconnected systems, testing how network position, social context, and interaction patterns produce unequal trajectories and polarized attitudes.
In social media settings, this work examines how belief systems, signed ties, and information exposure shape polarization, hidden extremes, and possible pathways to depolarization.
Grants and Funded Projects
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NWO Veni: The Social Contexts of Upward Mobility
Estimates how combinations of social contexts (family, school, neighborhood, peers) structure mobility and inequality using whole-population network data.
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ZonMw BePrepared: Vaccination Decision Profiles
Quantifies how beliefs and social connectivity jointly shape vaccination uptake across communities and subpopulations.
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FSBS Starter Grant: Uncovering the Dynamics of Belief Systems
Develops network-based and social-media-based methodology to study belief-system polarization, supporting the Ozgur Togay PhD line.
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Applied Data Science (ADS) SIG Grant: Leveraging LLMs for Stance Detection and Political Polarization
Builds and validates LLM-based target/stance detection workflows for measuring online political polarization, including a FAIR benchmark dataset.
Selected Publications
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ReMoDe - Recursive modality detection in distributions of ordinal data
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Community detection in bipartite signed networks is highly dependent on parameter choice
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Negative ties highlight hidden extremes in social media polarization
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Predicting COVID-19 infections using multi-layer centrality measures in population-scale networks
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Social Transmission along Multiple Pathways Promotes Information Fidelity and Reduces Divisiveness
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netCBS: Package to efficiently create network measures using CBS networks (POPNET) in the RA