Abstract
Media bias has significant negative effects, such as influencing elections and shaping people’s perceptions. However, the relationship between media bias and personalised news recommendation algorithms (widely adopted by many news platforms) remains unclear. In this study, we describe a novel framework that simulates user interactions with recommendation algorithms, allowing us to explore how the degree of bias in the news articles presented to users by personalized recommendation systems changes over time. Our experiments show that leading personalized news recommendation algorithms are sensitive to media bias, causing shifts in the proportion of biased news articles they recommend over time. These findings emphasize the importance of recognizing the influence of media bias on personalized news recommendation algorithms and the need to raise user awareness about media bias to encourage more diverse and balanced news consumption. The source code is available at https://github.com/ruanqin0706/UserRecSimulation.git.
This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 18/CRT/6183. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.
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Ruan, Q., Mac Namee, B., Dong, R. (2023). Unveiling the Relationship Between News Recommendation Algorithms and Media Bias: A Simulation-Based Analysis of the Evolution of Bias Prevalence. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XL. SGAI 2023. Lecture Notes in Computer Science(), vol 14381. Springer, Cham. https://doi.org/10.1007/978-3-031-47994-6_17
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DOI: https://doi.org/10.1007/978-3-031-47994-6_17
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