Prof. Dr. Felix Naumann

Project Description

The comment sections of online newspapers are an important space to indulge in political discussions and discuss various opinions. These discussion forums have to be moderated due to the misuse by spammers, haters, trolls, and means of propaganda. This moderation process is very expensive and many online news providers have discontinued their comment sections. With more and more political campaigning, or even agitation being distributed over the internet, serious and safe platforms to discuss political topics are increasingly important.

In this project, we therefore analyze comments, users, and articles to understand the dynamics, the information flow, and the interactions in the comment sections. We work on detecting inappropriate comments, predicting popular news topics, identifying fake news and recommending information.


  • Hate Speech Detection
  • Volume Prediction

Associated Activities

  • Master Thesis by Victor Künstler, 2019: Modeling User Behavior in Online Discussions on News Platforms
  • Master Thesis by Johannes Filter, 2019: Context-aware Classification of News Comments
  • Master Seminar, 2018: Text Mining in Practice
  • Master Thesis by Carl Ambroselli, 2018: Quality Management for Online News Comments
  • Master Project, 2017: Hate Speech Detection
  • Master Thesis by Dustin Gläser, 2017: Detection of Inappropriate Content in Online Comments
  • Master Thesis by Christian Godde, 2016: Classification of German Newspaper Comments

Project-Related Publications

  • Risch, J., Krestel, R.: Toxic Comment Detection in Online Discussions. In: Agarwal, B. (ed.) Deep learning based approaches for sentiment analysis. Springer (2019).
  • Risch, J., Krebs, E., Löser, A., Riese, A., Krestel, R.: Fine-Grained Classification of Offensive Language. Proceedings of GermEval (co-located with KONVENS). pp. 38-44 (2018).
  • van Aken, B., Risch, J., Krestel, R., Löser, A.: Challenges for Toxic Comment Classification: An In-Depth Error Analysis. Proceedings of the 2nd Workshop on Abusive Language Online (co-located with EMNLP). pp. 33-42 (2018).
  • Risch, J., Krestel, R.: Aggression Identification Using Deep Learning and Data Augmentation. Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (co-located with COLING). pp. 150-158 (2018).
  • Risch, J., Krestel, R.: Delete or not Delete? Semi-Automatic Comment Moderation for the Newsroom. Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (co-located with COLING). pp. 166-176 (2018).
  • Ambroselli, C., Risch, J., Krestel, R., Loos, A.: Prediction for the Newsroom: Which Articles Will Get the Most Comments? Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). pp. 193-199. ACL, New Orleans, Louisiana, USA (2018).
  • Godde, C., Lazaridou, K., Krestel, R.: Classification of German Newspaper Comments. Proceedings of the Conference Lernen, Wissen, Daten, Analysen. pp. 299-310. CEUR-WS.org (2016).