Rewriting Bias: Automated Rewriting to Reduce Media Bias Polarisation in News Recommender Systems

Media bias in news reporting significantly influences public perception and societal dynamics. Personalised news recommender systems, widely adopted by digital platforms, often exacerbate patterns of polarisation by amplifying users’ pre-existing preferences, creating filter bubbles, and reinforcing ideological divides. To address this issue, this work explores sentence rewriting as a novel strategy to mitigate polarisation in the dissemination of biased news content. We propose automated techniques leveraging word replacement methods and large language models (LLMs) to rewrite biased news content into neutral versions while preserving meaning and readability. Extensive experiments evaluate the effectiveness of sentence rewriting across various components of the news recommendation feedback loop, including user reading histories, candidate news datasets, and training datasets. Experimental results demonstrate the ability of sentence rewriting to reduce media bias polarisation in the dissemination of biased news content while maintaining or enhancing recommendation accuracy. Furthermore, this work provides a comprehensive assessment of media bias polarisation mitigation through a multidimensional evaluation framework and examines the ethical and societal challenges of addressing media bias polarisation in news recommendations. It also highlights the need for collaborative solutions involving platforms, news organisations, and users to foster more transparent news recommender systems.