The Influence of Media Bias on News Recommender Systems

Currently I am at the beginning of my fourth year of a structured PhD programme with an expectation to graduate in May 2024. The advancement of Internet technology has led to the proliferation of accessible online news media, which has overwhelmed people’s lives. Online news platforms have developed personalised recommendation systems to help readers avoid information overload and enhance their experience. However, the filter bubble, one of the side effects of personalised news recommendations, has received severe criticism for limiting readers’ perspectives. Media bias, which is one of the factors causing the “filter bubble” phenomenon, is widely present in news media. It has been extensively studied in the field of social sciences due to its unconscious distortion of readers’ views. Although many studies have focused on examining the effect of media bias on users and their political choices, there is still a lack of direct research on the impact of media bias on news dissemination platforms, such as personalised news recommender systems. My PhD research project aims to explore the influence of media bias on news recommender systems, and understand the factors that accelerate the recommendation of biased news to readers. To help algorithm designers gain insight into the sensitivity of proposed recommendation algorithms to media bias, and to design debiasing algorithms to weaken the impact of media bias on news recommender systems.