Prof. Dr. Felix Naumann

Project Description

User participation has become an integral part of news, journalism, and political communication. Social networks are not merely a representation of users' real-world relations but function as information distributors, protest platforms, and political campaign tools. News are no longer dominated by news corporations, instead, everybody can report news, discuss topics in public and share their opinions. Democracy 2.0 allows participation in political processes at a finger tip and information is available to everybody at any time. The downside of this development - fake news, hate speech, online stalking - poses a thread to the open, participatory discussion culture. In this project, we focus on the analysis of news in traditional media, e.g. by analyzing political bias in major news outlets and analyzing comments on news articles to identify, e.g., hate speech. We also investigate social networks and the information that can be extracted from them.


Project-Related Publications

  • 1.
    Lazaridou, K., Krestel, R., Naumann, F.: Identifying Media Bias by Analyzing Reported Speech. International Conference on Data Mining. IEEE (2017).
  • 2.
    Lazaridou, K., Krestel, R.: Identifying Political Bias in News Articles. International Conference on Theory and Practice of Digital Libraries. IEEE Technical Committee on Digital Libraries (2016).
  • 3.
    Jenders, M., Lindhauer, T., Kasneci, G., Krestel, R., Naumann, F.: A Serendipity Model For News Recommendation. KI 2015: Advances in Artificial Intelligence - 38th Annual German Conference on AI, Dresden, Germany, September 21-25, 2015, Proceedings. pp. 111–123. Springer (2015).
  • 4.
    Krestel, R., Werkmeister, T., Wiradarma, T.P., Kasneci, G.: Tweet-Recommender: Finding Relevant Tweets for News Articles. Proceedings of the 24th International World Wide Web Conference (WWW). ACM (2015).
  • 5.
    Schubotz, T., Krestel, R.: Online Temporal Summarization of News Events. Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). pp. 679–684. IEEE Computer Society (2015).
  • 6.
    Krestel, R., Bergler, S., Witte, R.: Modeling human newspaper readers: The Fuzzy Believer approach. Natural Language Engineering. 20, 261–288 (2014).