Fake News

Fake News and Collective Decision Making.
Rapid Automated Assessment of Media Bias

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How topics are covered in the news frames public debates and thus has a profound impact on collective decision making. News may be subtly biased through specific word choices or framing, intentional omissions or misrepresentation of specific details. Examples include the terms “illegal aliens” or “undocumented immigrants” in the coverage on immigration-related topics. Additionally, news authors can bias coverage by including or omitting specific information to support a certain perspective on the reported topic, and hence influence their audience. In the most extreme cases, fake news may present entirely fabricated facts to intentionally manipulate public opinion towards a given topic. A rich diversity of opinions is desirable but systematically biased information, if not recognized as such, can be problematic as a basis for decision making. Therefore, it is crucial to empower news readers in recognizing relative biases in coverage by providing timely identification of media bias that can be delivered together with the actual news coverage – for example, through a specifically designed news aggregator platform.

In this project we connect a long tradition of social science research on media bias with state-of-the-art methodology from computer science. The first part of the project centers around achieving rapid automated assessment of news media bias from a more technical, computer science point of view. The second, social science part of the project then is concerned with systematically studying how information about (relative) bias in the news could then be disseminated to enable – rather than to hinder – consensus formation and, in turn, collective decision making.

Relevant Literature

F Hamborg, K Donnay, and B Gipp (2019): Automated Identification of Media Bias in News Articles: An Interdisciplinary Literature Review. International Journal on Digital Libraries (IJDL). https://doi.org/10.1007/s00799-018-0261-y

F Hamborg, N Meuschke, and B Gipp (2018): Bias-aware News Analysis using Matrix-based News Aggregation. International Journal on Digital Libraries (IJDL). https://doi.org/10.1007/s00799-018-0239-9

F Hamborg, S Lachnit, M Schubotz, T Hepp, and B Gipp (2018): Giveme5W: Main Event Retrieval from News Articles by Extraction of the Five Journalistic W Questions. Proceedings of the 13th International Conference on Information (iConference 2018), 356–366. https://doi.org/10.1007/978-3-319-78105-1_39

F Hamborg, N Meuschke, C Breitinger, and B Gipp (2017): news-please: A Generic News Crawler and Extractor. Proceedings of the 15th International Symposium of Information Science (ISI 2017), 218–223.

Publications Funded by the Project

F Hamborg, A Zhukova, and B Gipp (2019): Illegal Aliens or Undocumented Immigrants? Towards the Automated Identification of Bias by Word Choice and Labeling. Proceedings of the 14th International Conference on Information (iConference 2019), 179–187. https://doi.org/10.1007/978-3-030-15742-5_17

F Hamborg, A Zhukova, and B Gipp (2019): Automated Identification of Media Bias by Word Choice and Labeling in News Articles. Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL 2019). https://doi.org/10.1109/JCDL.2019.00036

F Hamborg, C Breitinger, and B Gipp (2019): Giveme5W1H: A Universal System for Extracting Main Events from News Articles. Proceedings of the 7th International Workshop on News Recommendation and Analytics (INRA 2019).