An Initial Conceptualization of Algorithm Responsiveness: Comparing Perceptions of Algorithms Across Social Media Platforms

Abstract

Human–algorithm interaction has emerged as a pressing area in social media research because algorithms curate and govern most communication on social media. In the present study, we focus on the perceptions people have of algorithms as it relates to their identity and goals. Bridging interpersonal communication theory into human–algorithm interactions, we explicate the concepts of perceived algorithm responsiveness (PAR) and perceived algorithm insensitivity (PAI). In two preregistered studies, we examine PAR and PAI across the social media ecology, finding that TikTok has a higher PAR and lower PAI compared with Facebook and Instagram. Data suggest that algorithm awareness is only weakly correlated with PAR or PAI, and that PAR was a significant predictor of people’s social media enjoyment. These results contribute to research on human–algorithm interaction by conceptualizing and testing two new theoretical concepts to explain the algorithm responsiveness process.