Prof. Dr. Felix Naumann

Konstantina Lazaridou

Hasso-Plattner-Institut für Softwaresystemtechnik

Address: Prof.-Dr.-Helmert-Straße 2-3, D-14482 Potsdam
Phone: +49 331 5509 292
Room: Campus III G-3.2.08
Email:  konstantina.lazaridou(at)hpi.de


Ph.D. candidate at the Infomation Systems Research Group and member of the Web Science Group at the Hasso Plattner Institute for Software Systems Engineering, supervised by Dr. Ralf Krestel.


Research Interests

  • New analysis
  • Social Network Analysis
  • Web Mining
  • Graph Mining
  • Opinion and Sentiment Analysis


  • Information Retrieval and Web Search (Lecture, WS 2015/2016)
  • Graph Mining (Lecture, WS 2016/2017)
  • Recommender Systems (Seminar, SS 2017)
  • Text Mining (Seminar, SS 2017)


Master theses

  • Classification of German Newspaper Comments by Godde Christian, 2016
  • Large-scale Topic-based Analysis of Political Discussions on Twitter, Jaqueline Pollak, 2017


Classification of German Newspaper Comments

Godde, Christian; Lazaridou, Konstantina; Krestel, Ralf in Proceedings of the Conference Lernen, Wissen, Daten, Analysen volume   1670   of   CEUR Workshop Proceedings 1670 , page 299-310 . CEUR-WS.org , 2016 .

Online news has gradually become an inherent part of many people’s every day life, with the media enabling a social and interactive consumption of news as well. Readers openly express their perspectives and emotions for a current event by commenting news articles. They also form online communities and interact with each other by replying to other users’ comments. Due to their active and significant role in the diffusion of information, automatically gaining insights of these comments’ content is an interesting task. We are especially interested in finding systematic differences among the user comments from different newspapers. To this end, we propose the following classification task: Given a news comment thread of a particular article, identify the newspaper it comes from. Our corpus consists of six well-known German newspapers and their comments. We propose two experimental settings using SVM classifiers build on comment- and article-based features. We achieve precision of up to 90% for individual newspapers.
Further Information
Tags isg news_analysis web_science