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. Source code and datasets are available here.

Word Embeddings

We provide 300-dimensional fastText embeddings, which we pre-trained on more than 60 million comments from The Guardian: (5.5GB): Link

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

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