The Effects of Media Bias on News Recommendations

Abstract

The negative effects of media bias, such as influencing readers’ perceptions and affecting their social decisions, have been widely identified by social scientists. However, the combined impact of media bias and personalised news recommendation systems has remained largely unstudied, especially in real-world news recommendation datasets. Our study bridges this gap by analysing how leading algorithms influence the spread of biased news among news recommendation system users with diverse preferences. In this article, we show that current state-of-the-art news recommendation algorithms amplify the amount of biased media that readers consume and that, while the quality of their recommendations is largely similar, different news recommendation algorithms have differing sensitivities to media bias. We present experimental results that compare the performance of different news recommendation algorithms for users with different subject interests and different levels of prior history of reading biased media. Our analysis reveals that some state-of-the-art news recommendation algorithms that perform well at the recommendation task also lead to large amounts of biased news being recommended to readers. These findings suggest significant potential for negative impacts from increasing volumes of biased media being promoted by news recommendation algorithms. This highlights the importance for organisations to offer more trustworthy personalised news recommendations to mitigate the propagation of bias in news consumption.

Keywords

• Algorithmic media bias
• filter bubbles
• media bias
• media bias detection
• news recommendation