Perception versus Reality: Evaluating User Awareness of Political Selective Exposure in News Recommender Systems.

News Recommender Systems (NRSs) have become increasingly pivotal in shaping the news landscape, particularly in how news is disseminated. This has also led to concerns about information diversity, especially regarding selective exposure in the realm of political news. Users may not recognize that news content presented to them is subject to selective exposure, through users that incorporate political beliefs. Within the U.S. two-party system, our research explores the interactions between NRSs and users’ ability to discern news articles that align with their political biases. We performed an online experiment (N = 160) to address the issue of user awareness and self-recognition of selective exposure within NRSs. Users were asked to select any number of news articles that matched their political orientation (i.e., Democrat or Republican) from a list of 50 news articles (5 Democrat, 5 Republican, 40 filler articles), which were either ranked saliently towards their political orientation or randomly. Contrary to expectations, our findings reveal no significant difference in article selection between participants exposed to a baseline random order and those who where presented with the more salient and easy to select version. We did observe that Republicans performed worse than Democrats in identifying aligning articles, based on precision and recall metrics.