TU Wien Informatics

20 Years

Role

2024W

 

  • PopAut: An Annotated Corpus for Populism Detection in Austrian News Comments / Wagne, A., Neidhardt, J., & Kolb, T. E. (2024). PopAut: An Annotated Corpus for Populism Detection in Austrian News Comments. In N. Calzolari, M.-Y. Kan, V. Hoste, A. LENCI, S. Sakti, & N. Xue (Eds.), The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Main Conference Proceedings (pp. 12879–12892). ELRA Language Resources Association (ELRA).
    Project: CDL-RecSys (2022–2028)
  • Populism detection / Wagne, A. (2023, December 8). Populism detection [Presentation]. Workshop: Österreichisches Treffen zu Sentimentinferenz (ÖTSI) Österreichische Linguistik-Tagung 2023, Graz, Austria. http://hdl.handle.net/20.500.12708/193599
  • Potentials of Combining Local Knowledge and LLMs for Recommender Systems / Kolb, T. E., Wagne, A., Sertkan, M., & Neidhardt, J. (2023). Potentials of Combining Local Knowledge and LLMs for Recommender Systems. In V. W. Anelli, P. Basile, G. De Melo, F. Donini, A. Ferrara, C. Musto, F. Narducci, A. Ragone, & M. Zanker (Eds.), Proceedings of the Fifth Knowledge-aware and Conversational Recommender Systems Workshop co-located with 17th ACM Conference on Recommender Systems (RecSys 2023) (pp. 61–64). CEUR-WS.org. https://doi.org/10.34726/5334
    Download: PDF (317 KB)
    Project: CDL-RecSys (2022–2028)
  • Hands-on Session ChatGPT / Wagne, A. (2023, September 5). Hands-on Session ChatGPT [Presentation]. 2nd ACM Digital Humanism Summer School, TU Wien, Austria. http://hdl.handle.net/20.500.12708/193662
  • Populism Detection / Wagne, A. (2023, January 25). Populism Detection [Presentation]. ÖAW AI Winter School 2023, Wien, Austria.
  • COVID-19 and populism in Austrian news user comments - A machine learning approach / Wagne, A. (2023). COVID-19 and populism in Austrian news user comments - A machine learning approach [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.105940
    Download: PDF (1.84 MB)