Media Bias Analysis

These projects seek to (semi-)automatically identify slanted news coverage, i.e., media bias, in news articles. Fundamentally, we aim to approach the issue of media bias by combining the expertise of two academic disciplines: computer science and the social sciences. Specifically, we employ automated and efficient text analysis methods, such as natural language processing (NLP), with manual and effective media bias analysis concepts, such as frame analysis.

  • news-please is an open source, easy-to-use news crawler that extracts structured information from almost any news website. It can follow recursively internal hyperlinks and read RSS feeds to fetch both most recent and also old, archived articles.
  • NewsBird is a news aggregator that implements matrix-based news aggregation (MNA). In MNA, users explore different perspectives in news coverage by visually inspecting a two-dimensional matrix, which, for example, shows the main media perspective within one country about another country.
  • Givem5W1H is a system that extracts the journalistic five W and one H (5W1H) questions from news articles, i.e., who did what, when, where, why, and how.

Our group has been awarded a 3-year research grant by the Heidelberger Akademie der Wissenschaften for our interdisciplinary research on media bias.


Related Publications

[2018] Automated Identification of Media Bias in News Articles: An Interdisciplinary Literature Review

F. Hamborg, K. Donnay, and B. Gipp

International Journal on Digital Libraries (IJDL)

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[2018] Bias-aware News Analysis using Matrix-based News Aggregation

F. Hamborg, N. Meuschke, and B. Gipp

International Journal on Digital Libraries (IJDL)

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[2018] Extraction of Main Event Descriptors from News Articles by Answering the Journalistic Five W and One H Questions

F. Hamborg, C. Breitinger, M. Schubotz, S. Lachnit, and B. Gipp

in Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL)

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[2017] news-please: A Generic News Crawler and Extractor

F. Hamborg, N. Meuschke, C. Breitinger, and B. Gipp

in Proceedings of the 15th International Symposium on Information Science, 2017

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