Computational sociology

The research line Computational Sociology centers on the analysis of large-scale behavioral data to better understand the dynamics of complex social systems operate, focusing on topics such as cooperation, collective action, inequality, and the dynamics of social networks. 

It draws on a wide range of data sources, including digital traces generated through everyday online activity (such as social media platforms), administrative data collected by institutions, voluntarily donated data from individuals, and large-scale online experiments. To model such data, we use and develop state-of-the-art methods including social network analysis, natural language processing, machine learning, and agent-based modeling. 

Examples of current research areas:

1)    Cooperation and trust in online markets: how formal and informal institutions in contemporary online markets such as sharing- and gig platforms or Dark Web market places solve trust problems between users (e.g., buyers and sellers)? 
2)    Self-reinforcing inequalities: how do 'the rich become richer' dynamics unravel and lead to the implementation of inferior candidates, technologies and standards of behaviour?
3)    Collective action in (online) social networks: how does modern digital communication technology such as social media  mediate the emergence of collective action?
4)    Quality of digital trace data: How can we assess and correct measurement errors in digital trace data by combining these traces with more traditional data sources, such as surveys?

Examples of our research

Recent publications

Team