Text and data mining comprises the development and application of methods which are designed to extract knowledge that is relevant to the social sciences from unstructured texts or data streams.
Main research areas are:
- Detection of statistical regularities in data and text and alignment of these regularities with variables of interest such as political leaning or gender
- Combine digital behavioral data and survey data to create new types of user models
- Semantic enrichment and analysis of collaboratively generated documents (e.g. wikipedia articles or scientific publications) and the social dynamics of the creation process (e.g. conflicts, productivity)
- Statistical modelling of sequential human behavior (e.g., the decisions made when navigating on the web or individual movement in urban surroundings)
- Detection, disambiguation and linking of entities which are of interest for the social sciences in academic publications (especially references to research data)
- Extraction of key information from texts and (semi-)automatic indexing