The Computational Social Science (CSS) department focuses on the one hand on the exploration of novel methods and algorithms for the analysis of social phenomena on the WWW, based on – for example - data from social media. On the other, the department has expertise on knowledge discovery and -acquisition that is used for organizing the output of social science processes. In addition, these new methods and other state-of-the-art approaches form the basis for the development and operation of research-based services for the social sciences.
CSS develops algorithms and novel, non-obtrusive methods for the social sciences based on state-of-the-art approaches from the domains of machine learning, data mining and network analysis. Current projects in the department focus on the quantitative analysis of political electoral processes and dynamics based on social media data as well as the quantitative analysis of other social processes in social media or log data.
Community- and usage orientation are a high priority for the improvement of existing, and the development of new services for the social sciences. Through a combination of reactive, non-reactive, experimental and other methods the department acquires insights into requirements and incentive structures of the social science community. This knowledge is used for further development of existing, as well as the development of new GESIS online services.
The CSS department consists of the following teams:
The Data Science team is tasked with developing new methods and approaches to answer social science research questions through empirical investigations of emergent forms of data such as online social networks on the World Wide Web. This is accomplished via the adoption of existing and the development of new algorithms and methods for machine learning, data mining and network analysis.
The Knowledge Discovery team is tasked with the development of scalable procedures for the acquisition, processing and enrichment of large amounts of text data, such as, social science literature or research data. The results from these activities help to increase usability and algorithmic control of GESIS online services.
The Social Analytics and Services team is tasked with the empirical analysis of social behavior of users in online communities, for example in GESIS online services to better understand the dynamics of user activity on the Web. The team is also responsible for the development of innovative, data-driven offers for the social sciences.