Key points are not available for this paper at this time.
Algorithmic personalization of news and social media content aims to improve user experience; however, there is evidence that this filtering can have the unintended side effect of creating homogeneous "filter bubbles, " in which users are over-exposed to ideas that conform with their preexisting perceptions and beliefs. In this paper, we investigate this phenomenon in the context of political news recommendation algorithms, which have important implications for civil discourse.
Liu et al. (Mon,) studied this question.