AI-Powered Reciprocity: Exploring How Algorithmic Affordances Shape Social Support and Information Sharing Intentions on Social Media

While artificial intelligence (AI) is increasingly reshaping information consumption on social media, how algorithmic systems influence users’ behaviors has received limited attention. Drawing on affordance, social support, and social exchange theory, this study examines the effects of algorithmic affordances on reciprocal information-sharing intentions on AI-powered social media, with particular emphasis on the mediating roles of informational and emotional support. An online survey of 538 social media users in the United States reveals that content filtering and human–algorithm interplay are positively associated with perceived informational and emotional support, whereas automated decision-making shows no significant association. Both forms of social support, in turn, positively predict reciprocal intentions and mediate the relationships between algorithmic affordances and reciprocity, with emotional support exerting a stronger effect. These findings advance theoretical understanding of algorithmic affordances in information behavior and offer practical insights for designing AI-driven social media platforms that foster supportive user experiences and reciprocal engagement.