SODAS Data Discussion 9 December 2022
Copenhagen Center for Social Data Science (SODAS) aspirers to be a resource for all students and researchers at the Faculty of Social Sciences. We therefore invite researchers across the faculty to present ongoing research projects, project applications or just a loose idea that relates to the subject of social data science.
The rules are simple: short research presentations of ten minutes are followed by twenty minutes of debate. No papers will be circulated beforehand, and the presentations cannot be longer than five slides.
Presenter: Leon Houf, Heidelberg University
Title: Protective networks around harassers
Abstract: Harassment in any form is a widespread phenomenon in social contexts, including work environments such as academia. Often, harassers have a network around them that protects them from consequences of their harassing behaviour. This "harasser network" can protect against allegations and shield from (legal) consequences, to the point that perpetrators can stay employed and continue their behavior. In this project we aim to better understand these protective networks around harassers. We will develop a rigorous formalism and use empirical data to identify types of protective networks and the methods they employ to protect harassers. The results of this research should inform policymakers how to design systems and mechanisms that can enforce consequences on harassers without being impeded by their protective networks.
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Presenter: Lau Lilleholt, SODAS, University of Copenhagen
Title: The Danish Personality and Social Behavior Panel (DPSBP)
Abstract: In the fall of 2021, we (Ingo Zettler and Lau Lilleholt) launched The Danish Personality and Social Behavior Panel (DPSBP). DPSBP is a large-scale panel study, involving 14,071 Danish citizens, which covers personality and prosocial/antisocial behaviors, using a combination of both self-report and incentivized behavioral measures. Notably, the data from DPSBP can be linked to data from Statistics Denmark, making it a unique and widely applicable dataset.