Impacts of Reducing Visibility of Friends' Liked Content on User Content Engagement Across Newsfeed Channels

• 成果类型: Article
• 署名作者: Zhang, Xiaohui; He, Qinglai; Zhang, Zhongju
• 署名单位: University of Wisconsin System; University of Wisconsin Milwaukee; University of Wisconsin System; University of Wisconsin Madison; Arizona State University; Arizona State University-Tempe
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047; 1526-5536
• DOI: 10.1287/isre.2024.0871
• 发表日期: 2026-06
• 关键词: online discussion platforms; content distribution; newsfeed channels; user engagement; trending content; social networks; regression discontinuity; consumption; media; COMPETITION; patterns; adoption; internet

摘要

Online discussion platforms distribute content primarily through social and nonsocial newsfeed channels. Prior research has examined user engagement within individual channels, but less is known about their interactions. This study investigates the impact of reducing the visibility of a social channel (friends' liked content) on the quantity and diversity of user engagement with other newsfeed channels. Drawing on the channel substitution and complementarity framework, we develop our hypotheses and test them using a newsfeed change that reduces the visibility of friends' liked content. Our results show that this change leads to a general decline in both the quantity and diversity of userengaged content. Across channels, users increase their engagement with other social channels (e.g., friends' posts and trending topics), indicating a substitution effect among social channels. In contrast, they engage less with nonsocial content, suggesting a complementary relationship between social and nonsocial channels. Further, mechanism explorations indicate that the degree to which channels fulfill users' social and informational needs influences whether they substitute or complement each other. Additionally, the decline in content diversity stems from reduced interaction with niche content liked by social connections. These findings offer key insights for content distribution strategies, newsfeed design, and efforts to mitigate echo chambers on digital platforms.