User Friction in Infinite Scrolling Algorithmic Feeds: Examining the Impact of App Use and Algorithm Dependence on News Knowledge

Abstract Background The rapid expansion of mobile applications featuring personalized recommendation algorithms and infinite scrolling news feeds has raised concerns about their role in shaping societal knowledge acquisition. Grounded in the frameworks of algorithm dependence, this study investigates how different types of algorithmic Apps (news, social media, and short video) impact users’ news knowledge. Specifically, we examine the mediating effect of algorithm dependence on the relationship between App usage and news knowledge acquisition, introducing user friction as a mechanism, with perceived information narrowing as a moderating factor. Methods Data was collected via online survey with 354 responded participants. Results Results revealed that short video Apps decrease users’ news knowledge, social media Apps indirectly reduce news knowledge through algorithm dependence, and news Apps diminish news knowledge only among users perceiving high levels of information narrowing. Conclusions These findings suggest the potential for introducing user friction to regulate algorithmic curation and mitigate its negative impact on knowledge gain, especially within algorithmic news Apps. This study contributes to understanding the complex interplay between algorithmic dependence and knowledge gain, highlighting user-centered approaches to enhancing informational diversity in algorithm-driven media.