The Impact of Algorithmic Recommendation on Youth’s News Exposure Behavior: The Moderating Role of Media Literacy

Abstract

Nowadays, young people consume news very differently from the past. Instead of reading newspapers or watching TV news, many of them rely on mobile apps that use algorithms to recommend content. These algorithms track what users click on, how long they stay, and what they like, then push similar content to them. This makes information access convenient, but over time, users may end up seeing only a narrow range of topics [1][2]. This study focuses on whether heavy use of algorithmic recommendation is associated with the breadth, depth, and homogeneity of news that young people are exposed to, and whether media literacy can buffer these associations. A questionnaire survey was conducted online with 312 valid responses from people aged 18–35. Results show that higher use of algorithmic recommendation is associated with narrower news breadth and higher content homogeneity, but it is not significantly associated with news depth. Media literacy plays a significant moderating role: for those with higher media literacy, the negative associations of algorithmic recommendation are weaker [3][4]. These findings suggest that improving young people’s media literacy is a practical way to reduce the downsides of algorithmic news distribution.