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.
Our research on Text and Data Mining
- 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
- Abdedaiem, Amin, Abdelhalim Hafedh Dahou, Mohamed Amine Cheragui, and Brigitte Mathiak. 2024 (Forthcoming). "FASSILA: A Corpus for Algerian Dialect Fake News Detection and Sentiment Analysis." In ACLing 2024: 6th International Conference on AI in Computational Linguistics, Procedia Computer Science.
- Dahou, Abdelhalim Hafedh, Mohamed Amine Cheragui, Amin Abdedaiem, and Brigitte Mathiak. 2024 (Forthcoming). "Enhancing Model Performance through Translation-based Data Augmentation in the context of Fake News Detection." In ACLing 2024: 6th International Conference on AI in Computational Linguistics., Procedia Computer Science.
- Daikeler, Jessica, Leon Fröhling, Indira Sen, Lukas Birkenmaier, Tobias Gummer, Jan Schwalbach, Henning Silber, Bernd Weiß, Katrin Weller, and Clemens Lechner. 2024. "Assessing Data Quality in the Age of Digital Social Research: A Systematic Review." Social Science Computer Review online first. doi: https://doi.org/10.1177/08944393241245395.
- Zens, Maria, Katrin Weller, and Claudia Wagner, ed. 2023. Expert Insights into Studying Vulnerable Communities Online. An Interview with Kyriaki Kalimeri and Yelena Mejova. GESIS Guides to Digital Behavioral Data 4. Köln: GESIS. https://www.gesis.org/fileadmin/admin/Dateikatalog/pdf/dbd-guides/dbd_guide_04_studying_vulnerable_communities_online_kalimeri_mejova.pdf.
- Sen, Indira, Dennis Assenmacher, Mattia Samory, Isabelle Augenstein, Wil van der Aalst, and Claudia Wagner. 2023. "People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection." 2023. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP, edited by Houda Bouamor, Juan Pino, and Kalika Bali, 10480-10504. Singapore: Association for Computational Linguistics.
| Title | Start | End | Funder |
|---|---|---|---|
|
Kompetenzzentrum Datenqualität in den Sozialwissenschaften
(KODAQS)
|
2023-11-15 | 2026-11-14 | Bund |
|
NFDI for Data Science and Artificial Intelligence
(NFDI4DS)
|
2021-10-01 | 2026-09-30 | DFG |
|
NFDI for Business, Economic and Related Data
(BERD@NFDI)
|
2021-10-01 | 2026-09-30 | DFG |
|
Dehumanization Online: Measurement and Consequences (Professorinnenprogramm)
(DeHum)
|
2021-01-01 | 2026-09-30 | SAW (Leibniz) |
Find out more about our consulting and services:
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Analyzing Digital Behavioral Data
Methods, tools, frameworks and infrastructures for analyzing digital behavioral data.
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GESIS Guides to Digital Behavioral Data
Expertise and hands-on advice on the acquisition and analysis of digital behavioral data and the computational methods needed.