2024W

• Social Network Analysis / 194.050 / VU

• Classifying User Roles in Online News Forums: A Model for User Interaction and Behavior Analysis / Scholz, F., Kolb, T. E., & Neidhardt, J. (2024). Classifying User Roles in Online News Forums: A Model for User Interaction and Behavior Analysis. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp. 240–249). https://doi.org/10.1145/3631700.3665187
  Project: CDL-RecSys (2022–2028)
• Evaluating Group Fairness in News Recommendations: A Comparative Study of Algorithms and Metrics / Huebner, B., Kolb, T. E., & Neidhardt, J. (2024). Evaluating Group Fairness in News Recommendations: A Comparative Study of Algorithms and Metrics. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp. 337–346). https://doi.org/10.1145/3631700.3664897
  Project: CDL-RecSys (2022–2028)
• Unlocking the Potential of Content-Based Restaurant Recommender Systems / Godolja, D., Kolb, T. E., & Neidhardt, J. (2024). Unlocking the Potential of Content-Based Restaurant Recommender Systems. In A. Tuomi (Ed.), Information and Communication Technologies in Tourism 2024 (pp. 239–244). Springer, Cham. https://doi.org/10.1007/978-3-031-58839-6_26
  Project: CDL-RecSys (2022–2028)
• 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). http://hdl.handle.net/20.500.12708/199247
  Download: PDF (902 KB)
  Project: CDL-RecSys (2022–2028)
• Enhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy Measures / Kolb, T. E. (2024). Enhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy Measures. In RecSys ’24: Proceedings of the 18th ACM Conference on Recommender Systems (pp. 1388–1394). Association for Computing Machinery. https://doi.org/10.1145/3640457.3688027
  Project: CDL-RecSys (2022–2028)
• 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)
• Like a Skilled DJ - an Expert Study on News Recommendations Beyond Accuracy / Kolb, T. E., Nalis-Neuner, I., & Neidhardt, J. (2023). Like a Skilled DJ - an Expert Study on News Recommendations Beyond Accuracy. In B. Kille (Ed.), Proceedings of the International Workshop on News Recommendation and Analytics co-located with the 2023 ACM Conference on Recommender Systems (RecSys 2023). CEUR-WS.org. https://doi.org/10.34726/5332
  Download: PDF (530 KB)
  Project: CDL-RecSys (2022–2028)
• Hands-on Session ChatGPT / Kolb, T. E. (2023, September 5). Hands-on Session ChatGPT [Presentation]. 2nd ACM Digital Humanism Summer School, Wien, Austria.
  Project: CDL-RecSys (2022–2028)
• Sentiment Analysis / Kolb, T. E. (2023, January 25). Sentiment Analysis [Presentation]. ÖAW AI Winter School 2023, Austria.
  Project: CDL-RecSys (2022–2028)
• The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic / Kolb, T. E., Nalis, I., Sertkan, M., & Neidhardt, J. (2022). The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic. In Kolb Thomas (Ed.), Unofficial Proceedings of the 5th FAccTRec Workshop on Responsible Recommendation at RecSys 2022. https://doi.org/10.48550/ARXIV.2209.07608
  Project: CDL-RecSys (2022–2028)
• The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers / Kolb, T. E., Sekanina, K., Kern, B. M. J., Neidhardt, J., Wissik, T., & Baumann, A. (2022). The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers. In LREC 2022 Conference Proceedings (pp. 4708–4716). European Language Resources Association. https://doi.org/10.34726/4101
  Download: PDF (604 KB)
  Project: DYSEN (2020–2021)
• Dynamic sentiment analysis for measuring media bias / Kolb, T. E. (2022). Dynamic sentiment analysis for measuring media bias [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.73300
  Download: PDF (1.15 MB)
• Creating an Austrian language polarity dictionary with the crowd / Kolb, T. E., Sekanina, K., Kern, B. M. J., Neidhardt, J., Baumann, A., & Wissik, T. (2021, December 11). Creating an Austrian language polarity dictionary with the crowd [Conference Presentation]. Workshop: “Österreichisches Treffen zu Sentimentinferenz (ÖTSI)” der Österreichischen Linguistik-Tagung 2021, Austria. http://hdl.handle.net/20.500.12708/153831
  Download: Slides of the Presentation (1.08 MB)
  Project: DYSEN (2020–2021)
• A review and cluster analysis of German polarity resources for sentiment analysis / Kern, B., Baumann, A., Kolb, T., Sekanina, K., Hofmann, K., Wissik, T., & Neidhardt, J. (2021). A review and cluster analysis of German polarity resources for sentiment analysis. In 3rd Conference on Language, Data and Knowledge (LDK 2021) (pp. 1–17). OASICS. https://doi.org/10.4230/OASIcs.LDK.2021.37

• 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)
• Exploring group fairness in news media recommendations: Algorithms, metrics, and grouping / Huebner, B. (2023). Exploring group fairness in news media recommendations: Algorithms, metrics, and grouping [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.107255
  Download: PDF (870 KB)