Recommended to you: an experimental study of normative influences from algorithmic and social recommendations on social media

Abstract On social media, artificial intelligence (AI) increasingly curates content alongside social contacts. We examine whether social and algorithmic recommendations shape users’ perceived social norms around a moral issue and their intentions to engage with it. Drawing on theories of human–machine communication, human–AI interaction, and social norms, this experimental survey ( N = 1,021) compares social, algorithmic, and popularity-based algorithmic recommendations (e.g., “most read”) in the context of digital immortality. Recommendation type did not affect perceived norms, and algorithmic appreciation did not moderate these effects. However, perceived social norms—especially norms attributed to one’s social environment—were positively associated with intentions to discuss and act on the issue. These findings suggest that recommendations do not deterministically exert normative influence; they, however, also point to the potential power of perceived norms in shaping engagement with emerging moral and technological issues. Future research should investigate the conditions under which algorithmic and social cues shape normative perceptions and help further clarify the role of AI-driven content curation in public discourse.