The Dark Side of Algorithms

Algorithm-driven social media platforms are increasingly designed to deliver personalized health content that caters to users’ varied information needs. However, the opaque collection and use of personal data raise significant privacy concerns. Drawing on communication privacy management (CPM) theory, this study examines how users’ ethical considerations of algorithm-driven social media shape their privacy-related perceptions and, in turn, influence their trust in and engagement with health information recommended by these platforms. An online survey of 1,051 users revealed that ethical considerations were positively associated with both social privacy concerns and perceptions of institutional privacy practices. Social privacy concerns negatively predicted trust and engagement with recommended health information, whereas perceptions of institutional privacy practices positively predicted trust, which subsequently enhanced engagement. Ethical considerations exhibited dual indirect effects on engagement: a negative pathway via social privacy concerns and trust and a positive pathway via perceptions of institutional privacy practices and trust. Theoretical and practical implications are discussed

Affect (linguistics · Great Rift · Health information · Information privacy · Perception · Personally identifiable information · Privacy policy · Social media · Ethics and Social Impacts of AI · Privacy, Security, and Data Protection · Social Media in Health Education