Beyond Likes: How Normative Feedback Complements Engagement Signals on Social Media

Many online platforms incorporate engagement signals, such as likes, into their interface design to boost engagement. However, these signals can unintentionally elevate content that may not support normatively desirable behavior, especially when toxic content correlates strongly with popularity indicators. In this study, we propose structured prosocial feedback as a complementary signal, which highlights content quality based on normative criteria. We design and implement an LLM-based feedback system, which evaluates user comments based on principles from positive psychology, such as individual well-being. A pre-registered user study then examines how existing peer-based (popularity) and the new expert-based feedback interact to shape users' reposting behavior in a social media setting. Results show that peer feedback increases conformity to popularity cues, while expert feedback shifts choices toward normatively higher-quality content. This illustrates the added value of normative cues and underscores the potential benefits of incorporating such signals into platform feedback systems to foster healthier online environments.