Platform-use motivations and bridging social capital in recommendation-driven social media: the role of algorithmic resistance

Recommendation algorithms increasingly influence how users encounter content and social connections on social media. This study examines the relationships among platform-use motivations, algorithmic resistance, and bridging social capital in recommendation-driven social media use. Drawing on uses and gratifications theory and social capital research, we analyzed survey data from 459 Xiaohongshu users using structural equation modeling. The results showed that information seeking and social interaction motivations were negatively associated with algorithmic resistance, whereas entertainment motivation was positively associated with it. Algorithmic resistance was negatively associated with bridging social capital. Mediation analyses further revealed positive indirect associations of information seeking and social interaction motivations with bridging social capital through lower algorithmic resistance, whereas entertainment motivation showed a negative indirect association through higher algorithmic resistance. These findings identify algorithmic resistance as one behavioral pathway linking platform-use goals with social-resource outcomes in recommendation-driven social media environments.