Impacts of Hiding Friends’ Liked Content on User-Content Engagement across Newsfeed Channels

Abstract

Social media platforms often simultaneously distribute content through different newsfeed channels, most commonly, social networks, algorithmic recommendations and trending content. Prior literature has investigated each channel’s impact on user-content engagement. However, little is known about the relationships between these channels. In this research, we investigate the impacts of limiting content display from the social network channel on the quantity and diversity of user-engaged content across channels. We rely on the selective attention theory to theorize changes in users’ content engagement. We leverage a natural experiment, where a social media platform hides friends’ liked content from the social network channel, to identify the impacts. Results show that hiding friends’ liked content reduces the quantity of users’ content engagement on the entire platform. Across channels, users increase their engagement with trending content but decrease their engagement with algorithmic recommendations. Further, restricting exposure to friends’ liked content reduces the diversity of users’ content engagement. Notably, our results demonstrate heterogeneous effects depending on users’ tenure and social orientation. Social-oriented users exhibit a greater decline in the quantity and diversity of content engagement. These users are less receptive to algorithmic recommendations but more interested in trending content. In contrast, we find that the newsfeed change presents less impact on users with longer tenure. Additionally, longer-tenure users have a higher tolerance to algorithmic recommended content. Our results highlight the intercorrelation of user-content engagement across newsfeed channels and provide insights for newsfeed designs.

Keywords: Social media, User-content engagement, Newsfeed channel, Social network, Algorithmic recommendation, Trending content

JEL Classification: W15, D03