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
Learn more about our consulting and services:
- Bittermann, André, Veronika Batzdorfer, Sarah Marie Müller, and Holger Steinmetz. 2021. "Mining Twitter to detect hotspots in psychology." Zeitschrift für Psychologie 229 (1): 3-14. doi: https://doi.org/10.1027/2151-2604/a000437.
- Soldner, Felix. 2021. "Detecting fake online reviews." University College London, London. SS: 2 SWS.
- Soldner, Felix, Leonie Maria Tanczer, Daniel Hammocks, Isabel Lopez-Neira, and Shane Johnson. 2021. "Using machine learning methods to study technology-facilitated abuse: Evidence from the analysis of UK crimestoppers’ text data." In The Palgrave handbook of gendered violence and technology, edited by Anastasia Powell, Asher Flynn, and Lisa Sugiura, 481-503. Basingstoke u.a.: Palgrave Macmillan. doi: https://doi.org/10.1007/978-3-030-83734-1_24.
- Soldner, Felix, Bennett Kleinberg, and Shane Johnson. 2022. "Trends in online consumer fraud: A data science perspective." In A fresh look at fraud: Theoretical and applied perspectives, edited by Stacey Wood, and Yaniv Hanoch, 167-191. Binghamton, NY: Routledge. doi: https://doi.org/10.4324/9781003017189.
- Soldner, Felix, Justin Chun-ting Ho, Mykola Makhortykh, Isabelle Van der Vegt, Maximilian Mozes, and Bennett Kleinberg. 2019. "Uphill from here: Sentiment patterns in videos from left-and right-wingYouTube news channels." In Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science, 84–93. 3rd Workshop on Natural Language Processing and Computational Social Science. doi: https://doi.org/10.18653/v1/W19-2110.