Information linking develops models which allow for linking heterogenous types of information through semantic web technologies. Information retrieval develops models which improve digital information search.
Main research areas in the field of information linking and retrieval are:
- User studies and logfile analyses to analyze the information behavior of social scientists
- Linking different types of information as well as combining survey data with research data from other academic domains
- Making information retrieval easier and more personal
- Integrated access to information via linked information (“link retrieval”)
- Developing domain specific recommender and ranking services
- Novel logfile based metrics for evaluating interactive retrieval systems
- Soldner, Felix, Fabian Plum, Bennett Kleinberg, and Shane Johnson. 2022. "From the dark to the surface web: Scouting eBay for counterfeits." ODISSEI Conference for Social Science in the Netherlands 2022, Open Data Infrastructure for Social Science and Economic Innovations, Utrecht, 03.11.2022.
- Soldner, Felix, Bennett Kleinberg, and Shane Johnson. 2022. "Confounds and overestimations in fake review detection: Experimentally controlling for product-ownership and data-origin." PLoS ONE 17 (12): e0277869. doi: https://doi.org/10.1371/journal.pone.0277869.
- Batzdorfer, Veronika. 2022. "Theory-driven modelling of complex socio-psychological constructs in text." Invited Panel Talk on the Workshop on Computational Linguistics for Political Text Analysis (CPSS-2022), Universität Potsdam, 12.09.2022.
- Soldner, Felix, Fabian Plum, Bennett Kleinberg, and Shane Johnson. 2022. "From the dark to the surface web: Scouting eBay for counterfeits." Cambridge Cybercrime Centre: Fifth Annual Cybercrime Conference, 05.09.2022.
- Soldner, Felix, Justin Chun-ting Ho, Mykola Makhortykh, Isabelle W.J. van der Vegt, Maximilian Mozes, and Bennett Kleinberg. 2019. "Sentiment patterns in videos from left- and right-wing YouTube news channels." Euro CSS 2019, 02.09.2019.