Presentations and conferences
276 Übereinstimmungen gefunden / 1-15 16-30 31-45 46-60 61-75 76-90 91-105 106-120 121-135 136-150 151-165 166-180 181-195 196-210 211-225 226-240 241-255 256-270 271-276
Online survey data contamination through Large Language Models: Predicting LLM-generated answers to open narrative questions.Höhne, J. K., Claaßen, J., Bach, R., & Haensch, A.-C. (2026, Juni).Online survey data contamination through Large Language Models: Predicting LLM-generated answers to open narrative questions. Vortrag im Rahmen der 16th Scientific Conference on Data Collections Methods hosted by the ADM, the ASI and the Federal Statistical Office. |
How does smartphone participation in online surveys differ across 12 European countries?Claaßen, J., Gummer, T., Rettig, T., & Höhne, J. K. (2026, Juni).How does smartphone participation in online surveys differ across 12 European countries? Vortrag im Rahmen der 16th Scientific Conference on Data Collections Methods hosted by the ADM, the ASI and the Federal Statistical Office. |
Evaluating the effectiveness of CAPTCHAs and attention checks for LLM-driven survey bots.Shahania, S., Niemann-Lenz, J., & Broneske, D. (2026, Juni).Evaluating the effectiveness of CAPTCHAs and attention checks for LLM-driven survey bots. Vortrag auf der Konferenz 16th Scientific Conference on Data Collections Methods hosted by the ADM, the ASI and the Federal Statistical Office, Wiesbaden, Germany. |
Lexikalisch verloren, semantisch gefunden: LLM-gestützte Suche auf der Grundlage von DDI-Metadaten.Broneske, D., Daniel, A., Buck, D., & Weber, A. (2026, Juni).Lexikalisch verloren, semantisch gefunden: LLM-gestützte Suche auf der Grundlage von DDI-Metadaten. Vortrag im Rahmen der 10. Konferenz für Sozial- und Wirtschaftsdaten (10|KSWD), Berlin. Abstract
To ensure that research data is easily discoverable by researchers, search systems are needed that make data accessible in relation to specific research questions. Research data centres currently often make their metadata searchable using lexical search engines (e.g. Elasticsearch) and return results based on lexical matches. Whilst this enables full-text search and a sophisticated ranking of search results by relevance, it has the disadvantage of failing to identify semantically relevant metadata if subject-specific concepts or research questions are not explicitly named in the metadata. This is a problem, as theoretical constructs often exist only as latent dimensions [...] Full Abstract: https://zenodo.org/records/20844070 |
Was Forschende für die Sekundärnutzung qualitativer Daten brauchen: Eine Bestandsaufnahme aktueller Bedarfe, Barrieren und Lösungsansätze.Mozygemba, K., Hollstein, B., Gebel, T., Hanekop, H., Köchling, S., Lösch, T., & Saloga, G. (2026, Juni).Was Forschende für die Sekundärnutzung qualitativer Daten brauchen: Eine Bestandsaufnahme aktueller Bedarfe, Barrieren und Lösungsansätze. Vortrag auf der Konferenz 10. Konferenz für Sozial- und Wirtschaftsdaten, Berlin. |
Challenges for the FDZ-DZHW: Transparency and trustworthiness of research data in an AI-influenced world.Buck, D., & Daniel, A. (2026, Juni).Challenges for the FDZ-DZHW: Transparency and trustworthiness of research data in an AI-influenced world. Vortrag im Rahmen der International Conference Wissenschaftsreflexion. What is it? What is the need for it?, Leibniz Universität Hannover, Hannover. |
The imitation game: Evaluating persona-driven LLM response behavior in web surveys.Shahania, S. (2026, Juni).The imitation game: Evaluating persona-driven LLM response behavior in web surveys. Vortrag auf der Konferenz The 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Hongkong, SAR, China. https://doi.org/10.1007/978-981-92-1468-6_33 |
Love, Loathing, and Loyalty: Affective Counterpublics on German Political TikTok in the Run-up to the 2025 Federal Election.Niemann-Lenz, J., Revers, M., Rodeck, L., & Meyer, H. (2026, Juni).Love, Loathing, and Loyalty: Affective Counterpublics on German Political TikTok in the Run-up to the 2025 Federal Election. Vortrag auf der Konferenz 76th Annual ICA Conference, Capetown, South Africa. |
Online data contamination through LLMs: Identifying LLM-based answers in surveys, experiments, and crowdsourcing tasks.Claaßen, J., Höhne, J. K., Bach, R., & Haensch, A.-C. (2026, Mai).Online data contamination through LLMs: Identifying LLM-based answers in surveys, experiments, and crowdsourcing tasks. Poster im Rahmen der Konferenz Computational Social Science: Democracy at Risk? Societal Challenges, Data, and Research Infrastructures in the Age of CSS, Wien, Österreich. |
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