Rewriting Bias: Mitigating Media Bias in News Recommender Systems through Automated Rewriting

Qin Ruan, Jin Xu, Susan Leavy, Brian Mac Namee, Ruihai Dong

Abstract: Personalised news recommender systems are effective in disseminating news content based on users’ reading histories but can also amplify and proliferate biased media. This work examines the potential of automated sentence rewriting methods, utilising word replacement methods and large language models (LLMs), to mitigate this side effect of recommender systems. We present a two-step workflow: the application of automated sentence rewriting methods to rewrite biased sentences, and the integration of these rewritten sentences into the recommendation process. We evaluate the effectiveness of sentence rewriting approaches in a simulation framework, to assess how well they mitigate the spread of biased news. Our study demonstrates that applying sentence rewriting to users’ reading histories can result in a significant reduction in the propagation of biased media. Our contributions are threefold: we pioneer the use of LLMs for mitigating the spread of biased news by recommender systems; we demonstrate that algorithms trained on debiased content maintain or improve recommendation accuracy; and we provide a comprehensive exploration of the effectiveness of applying sentence rewriting methods to various components within a recommender system, as well as an investigation of the underlying reasons for their efficacy. This work advances our understanding of media bias mitigation in news content and recommendation algorithms, providing valuable insights into how news recommender systems can prevent the dissemination of biased information.