Vorträge und Tagungen
228 Übereinstimmungen gefunden / 1-15 16-30 31-45 46-60 61-75 76-90 91-105 106-120 121-135 136-150 151-165 166-180 181-195 196-210 211-225 226-228
Data Sharing offener und sensibler Daten über Forschungsdatenzentren und weitere Dateninfrastrukturen.Buck, D. (2025, September).Data Sharing offener und sensibler Daten über Forschungsdatenzentren und weitere Dateninfrastrukturen. Vortrag im Rahmen des Fachforum 'Technische Umsetzung von Open Data‘ - Kompetenzzentrum Open Data (CCOD), Bundesverwaltungsamt, Köln. |
Identifying bots in online surveys through robotic language in open narrative answers: A prediction approach.Claaßen, J., Höhne, J. K., Bach, R., & Haensch, A.-C. (2025, Juli).Identifying bots in online surveys through robotic language in open narrative answers: A prediction approach. Poster im Rahmen der International Conference on Computational Social Science (IC2S2), Norrköping, Schweden. |
LLM-driven bot infiltration: Protecting web surveys through prompt injections.Claaßen, J., Höhne, J. K., & Wolf, B. L. (2025, Juli).LLM-driven bot infiltration: Protecting web surveys through prompt injections. Poster im Rahmen der International Conference on Computational Social Science (IC2S2), Norrköping, Schweden. |
Promoting professionalisation: A case study on achieving CoreTrustSeal certification and improved access to international and interdisciplinary data.Buck, D., Beck, K., Hoffstätter, U., & Siegers, P. (2025, Juli).Promoting professionalisation: A case study on achieving CoreTrustSeal certification and improved access to international and interdisciplinary data. Vortrag auf der Konferenz European Survey Research Association (ESRA 2025), Utrecht, Die Niederlande. |
LLM-driven bots in web surveys: Predicting robotic language in open narrative answers.Claaßen, J., Höhne, J. K., Bach, R., & Haensch, A.-C. (2025, Juli).LLM-driven bots in web surveys: Predicting robotic language in open narrative answers. Vortrag im Rahmen der European Survey Research Association (ESRA) Conference, Utrecht, Niederlande. |
Measuring health-related parental digital behavior: A proof-of-concept study using web tracking data.Claaßen, J., & Revilla, M. (2025, Juli).Measuring health-related parental digital behavior: A proof-of-concept study using web tracking data. Vortrag im Rahmen der European Survey Research Association (ESRA) Conference, Utrecht, Niederlande. |
Beyond anti-elitism and out-group attacks: How concerns shape the AfD's populist representation on German TikTok during the 2024 European elections.Meyer, H., Niemann-Lenz, J., Rodeck, L., & Revers, M. (2025, Juni).Beyond anti-elitism and out-group attacks: How concerns shape the AfD's populist representation on German TikTok during the 2024 European elections. Vortrag auf der Konferenz 75th Annual Conference of the International Communication Association (ICA), Denver, USA. https://doi.org/10.31219/osf.io/yk3u4 |
Explaining item-nonresponse in open questions with requests for voice responses.Salvatore, C., & Höhne, J. K. (2025, Juni).Explaining item-nonresponse in open questions with requests for voice responses. Vortrag auf dem Workshop Mobile Apps and Sensors in Surveys (MASS), London School of Economics (LSE), London, UK. |
Mobile device tracking: Explaining discrepancies between survey self-reports and digitally tracked behavior.Claaßen, J., Bosch, O., & Höhne, J. K. (2025, Juni).Mobile device tracking: Explaining discrepancies between survey self-reports and digitally tracked behavior. Vortrag auf dem Workshop Mobile Apps and Sensors in Surveys (MASS), London School of Economics (LSE), London, UK. |
Comparing LLM-driven bots with answers from a Facebook survey.Claaßen, J., & Höhne, J. K. (2025, Mai).Comparing LLM-driven bots with answers from a Facebook survey. Poster auf dem Workshop Computational Social Science: AI and Society – Exploring Inequality in the Digital Age, Universität Mannheim, Mannheim. |
Meet-the-data: EUROSTUDENT – Daten zur Hochschulbildung in Europa.Hauschildt, K., & Daniel, A. (2025, Mai).Meet-the-data: EUROSTUDENT – Daten zur Hochschulbildung in Europa. Vortrag im Rahmen der Meet-the-Data-Veranstaltungsreihen 2024/2025, Verbund Forschungsdaten Bildung (VerbundFDB), Frankfurt/Main. |
Datenüberlassung an ein FDZ oder selbst eines gründen? Aufgaben und Strukturen eines FDZ.Siegers, P., & Buck, D. (2025, Mai).Datenüberlassung an ein FDZ oder selbst eines gründen? Aufgaben und Strukturen eines FDZ. Vortrag auf dem Workshop Daten sicher teilen - Landkarte der Möglichkeiten, Berlin, Hannover, Köln. |
AutoML meets hugging face: Domain-aware pretrained model selection for text classification.Safikhani, P. (2025, April/Mai).AutoML meets hugging face: Domain-aware pretrained model selection for text classification. Poster auf der Konferenz 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, Albuquerque, USA. Abstract
The effectiveness of embedding methods is crucial for optimizing text classification performance in Automated Machine Learning (AutoML). However, selecting the most suitable pre-trained model for a given task remains challenging. This study introduces the Corpus-Driven Domain Mapping (CDDM) pipeline, which utilizes a domain-annotated corpus of pre-fine-tuned models from the Hugging Face Model Hub to improve model selection. Integrating these models into AutoML systems significantly boosts classification performance across multiple datasets compared to baseline methods. Despite some domain recognition inaccuracies, results demonstrate CDDM’s potential to enhance model selection, streamline AutoML workflows, and reduce computational costs. |
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