Hasso-Plattner-Institut
Prof. Dr. Felix Naumann
  
 

Julian Risch

I am a Ph.D. student at the Information Systems Group and a member of the HPI Research School. My research focuses on topic modeling and deep learning with applications in the field of comment analysis. Further, I am involved in projects on patent classification and book recommendation.

Contact Information

Prof.-Dr.-Helmert-Straße 2-3
D-14482 Potsdam
Room: F-2.08

Phone: +49 331 5509 272

Email: Julian Risch

Open Master's Theses

I provide supervision for Master's theses in the area of News Comment Analysis, e.g., Toxic Comment Classification, User Engagement Prediction, Comment Recommendation, and Discussion Summarization/Visualization. Feel free to schedule an informal meeting with me to discuss details of these topics and/or your own ideas.

Teaching

Publications

Delete or not Delete? Semi-Automatic Comment Moderation for the Newsroom

Risch, Julian; Krestel, Ralf in Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (co-located with COLING) page 166-176 . 2018 .

Comment sections of online news providers have enabled millions to share and discuss their opinions on news topics. Today, moderators ensure respectful and informative discussions by deleting not only insults, defamation, and hate speech, but also unverifiable facts. This process has to be transparent and comprehensive in order to keep the community engaged. Further, news providers have to make sure to not give the impression of censorship or dissemination of fake news. Yet manual moderation is very expensive and becomes more and more unfeasible with the increasing amount of comments. Hence, we propose a semi-automatic, holistic approach, which includes comment features but also their context, such as information about users and articles. For evaluation, we present experiments on a novel corpus of 3 million news comments annotated by a team of professional moderators.
Delete or not Delete? Sem... - Download
Further Information
Tags comments_analysis  hpi  isg  web_science