Fake News and Collective Decision Making – Rapid Automated Assessment of Media Bias
Project duration:
2019-2023
Project Consortium:
University of Zurich (CH)
University of Konstanz (DE)
University of Göttingen (DE)
Website:
https://www.hadw-bw.de/en/young-academy/win-kolleg/win7/fake-news
Project Description:
The project was funded by the Heidelberger Akademie der Wissenschaften, the academy of sciences of the state of Baden-Württemberg as part of the WIN-Kolleg program. From January 2022 to December 2023, Dr. Felix Hamborg took over as the project lead and Prof. Bela Gipp and myself were associated researchers on the project after leading it for the previous three years. The project aimed to provide insights into how slanted news media coverage impacts public debates and, in turn, affects collective decision making.
News may be subtly biased through specific word choices or framing, intentional omissions or misrepresentation of specific details. 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.
This project combined 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 centered 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 was 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.
Related Publications:
- Timo Spinde, Elisabeth Richter, Martin Wessel, Juhi Kulshrestha and Karsten Donnay. (2023). What do Twitter Comments Tell About News Article Bias? Assessing the Impact of News Article Bias on its Perception on Twitter. Online Social Networks and Media 37–38: 100264. [Open Access]
- Anastasia Zhukova, Terry Ruas, Felix Hamborg, Karsten Donnay and Bela Gipp. (2023). What’s in the News? Towards Identification of Bias by Commission, Omission, and Source Selection (COSS). Proceedings of the 2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL). Champaign, IL: IEEE, 258–259. [Preprint]
- Anastasia Zhukova, Felix Hamborg, Karsten Donnay and Bela Gipp. (2022). XCoref: Cross-document Coreference Resolution in the Wild. In M. Smits et al. (eds): iConference 2022, Lecture Notes in Computer Science 13192. Cham: Springer, 272-291. [arXiv]
- Felix Hamborg, Timo Spinde, Kim Heinser, Karsten Donnay and Bela Gipp. (2021). How to Effectively Identify and Communicate Person-Targeting Media Bias in Daily News Consumption? Proceedings of the 15th ACM Conference on Recommender Systems, 9th International Workshop on News Recommendation and Analytics (INRA). Amsterdam, Netherlands, 1-11. [arXiv]
- Felix Hamborg, Kim Heinser, Anastasia Zhukova, Karsten Donnay and Bela Gipp. (2021) Newsalyze: Effective Communication of Person-Targeting Biases in News Articles. Proceedings of the 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL). Champaign, IL: IEEE, 130-139. [arXiv]
- Franziska Weeber, Felix Hamborg, Karsten Donnay and Bela Gipp. (2021). Assisted Text Annotation Using Active Learning to Achieve High Quality with Little Effort. Proceedings of the 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL). Champaign, IL: IEEE, 287-288. [arXiv]
- Felix Hamborg and Karsten Donnay. (2021). NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 2021. Fully virtual event. Stroudsburg, PA: ACL, 1663–1675. [Open Access]
- Timo Spinde, Lada Rudnitckaia, Jelena Mitrović, Felix Hamborg, Michael Granitzer, Bela Gipp and Karsten Donnay. (2021). Automated Identification of Bias Inducing Words in News Articles Using Linguistic and Context-oriented Features. Information Processing and Management 58(3): 102505. [Open Access]
- Felix Hamborg, Karsten Donnay and Bela Gipp. (2021). Towards Target-Dependent Sentiment Classification in News Articles. In K. Toeppe et al. (eds): iConference 2021, Lecture Notes in Computer Science 12646. Cham: Springer, 156-166. [arXiv]
- Timo Spinde, Lada Rudnitckaia, Kanishka Sinha, Felix Hamborg, Bela Gipp and Karsten Donnay. (2021). MBIC – A Media Bias Annotation Dataset Including Annotator Characteristics. Proceedings of the iConference 2021. Beijing, China (Fully virtual event). [arXiv, MBIC Data]
- Anastasia Zhukova, Felix Hamborg, Karsten Donnay and Bela Gipp. (2021). Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons. In K. Toeppe et al. (eds): iConference 2021, Lecture Notes in Computer Science 12646. Cham: Springer, 514-526. [arXiv]
- Felix Hamborg, Anastasia Zhukova, Karsten Donnay and Bela Gipp. (2020). Newsalyze: Enabling News Consumers to Understand Media Bias. Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL). New York, NY: ACM, 455–456. [arXiv]
- Timo Spinde, Felix Hamborg, Angelica Becerra, Karsten Donnay and Bela Gipp. (2020). Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias. Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL). New York, NY: ACM, 389-392. [arXiv]
- Felix Hamborg, Karsten Donnay and Bela Gipp. (2019). Automated Identification of Media Bias in News Articles: An Interdisciplinary Literature Review. International Journal on Digital Libraries 20(4): 391-415. [Open Access]