Restraining the formation of filter bubbles with algorithmic affordances: Toward more balanced information consumption and decreased attitude extremity

In combating filter bubbles, an undesirable consequence of personalized recommendations, prior research has focused on improving algorithms to increase the diversity of the content recommended. Following a user-centered approach firmly grounded in information science, this study is dedicated to optimizing interaction patterns with algorithmic affordances, aiming to augment the diversity of the content consumed and induce favorable attitude changes. A controlled experiment was conducted on a mock personalized recommender system that provided both information and interactivity affordances, exemplified by stance labels and stance-based filters, respectively. A total of 142 participants were recruited to browse recommendations generated by the system on a specific controversial topic, and the selectivity of their information consumption behavior and the change in their attitude extremity were measured. It was found that both types of affordances were effective in reducing users' behavioral selectivity. While stance labels inhibited the consumption of pro-attitudinal information, stance-based filters facilitated the consumption of counter-attitudinal information. Furthermore, the affordances could immediately mitigate the attitude extremity of those with a higher level of algorithmic literacy. The findings not only enrich the growing body of literature on filter bubbles but also offer valuable implications for the affordance design practices of personalized recommender systems.