Felix Hamborg

Doctoral Researcher

Recipient of the Carl-Zeiss Scholarship

Research Interests

I love working with news articles  and in general text documents. Specifically, my research focuses on the automated identification of media bias by word choice and labeling in news articles. My research interests are:

  • Media bias and content analysis
  • Text analysis
  • Natural language processing (NLP)
  • Information retrieval

I believe in open access and strive to release my research projects as soon as they are ready for the public. You can find them listed on the right or on GitHub.

Bachelor & Master Theses

Most of the projects and theses that I supervise are concerned with text analysis and natural language processing. Besides supervising theses related to Computer Science, I also enjoy working together with students from the Social and Economic Data Science (SEDS) program and Computational Linguistics.

For information on possible projects just have a look at the News Analysis section in our list of project topics or drop me an email.


Please click here for a list of my publications published with the Information Science Group. For a full list of my publications, including patents, please refer to my profile on Google Scholar.

Publications that received an award or were nominated for one are listed below:

  • F. Hamborg, S. Lachnit, M. Schubotz, T. Hepp, and B. Gipp, “Giveme5W: Main Event Retrieval from News Articles by Extraction of the Five Journalistic W Questions,” in Proceedings of the iConference 2018, 2018. Best Paper Award Finalist PDF
  • F. Hamborg, N. Meuschke, and B. Gipp, “Matrix-based News Aggregation: Exploring Different News Perspectives,” in Proceedings ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), 2017. Best Student Paper Award PDF

    Media coverage


    Summer Term 18

    • Seminar: Selected Topics in Data Science

    Winter Term 17/18

    • Seminar: Cryptocurrencies and Blockchain in Practice

    Summer Term 17

    Winter Term 16/17

    • Seminar: Selected Topics in Information Retrieval

    Summer Term 16