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The Influence of Media Bias on News Recommender Systems

Published: 19 June 2023 Publication History

Abstract

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.

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  • (2023)Reducing Media Bias in News Headlines2023 31st Irish Conference on Artificial Intelligence and Cognitive Science (AICS)10.1109/AICS60730.2023.10470850(1-4)Online publication date: 7-Dec-2023

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cover image ACM Conferences
UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
June 2023
333 pages
ISBN:9781450399326
DOI:10.1145/3565472
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 19 June 2023

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Author Tags

  1. algorithmic media bias
  2. debiasing
  3. filter bubble
  4. media bias
  5. media bias detection
  6. news recommendation
  7. user preference shifting

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  • (2023)Reducing Media Bias in News Headlines2023 31st Irish Conference on Artificial Intelligence and Cognitive Science (AICS)10.1109/AICS60730.2023.10470850(1-4)Online publication date: 7-Dec-2023

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