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Text and Data Mining

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
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  • Dahou, Abdelhalim Hafedh, Mohamed Amine Cheragui, and Ahmed Abdelali. 2023 (Forthcoming). "Performance Analysis of Arabic Pre-Trained Models on Named Entity Recognition Task." 14th Conference Recent Advances In Natural Language Processing, RANLP.
  • Diera, Andor, Abdelhalim Hafedh Dahou, Lukas Galke, Fabian Karl, Florian Sihler, and Ansgar Scherp. 2023 (Forthcoming). "GenCodeSearchNet: A Benchmark Test Suite for Evaluating Generalization in Programming Language Understanding." First GenBench workshop on generalisation (benchmarking) in NLP, Co-located with EMNLP.
  • Dahou, Abdelhalim Hafedh, and Brigitte Mathiak. 2023 (Forthcoming). "Subject Classification of Software Repository." 15th international conference on Knowledge Discovery and Information Retrieval, KDIR.
  • Lietz, Haiko, Mohsen Jadidi, Daniel Kostic, Milena Tsvetkova, and Claudia Wagner. 2023 (Forthcoming). "Individual and gender inequality in computer science: A career study of cohorts from 1970 to 2000." Quantitative Science Studies.
  • Kohne, Julian. 2023. "ChatDashboard - A Framework to collect, link, and process donated WhatsApp Chat Log Data." European Survey Research Association Conference (ESRA), University of Milano - Bicocca, Mailand, 2023-07-18.