Too Big to Sell? A Computational Analysis of Network and Content Characteristics among Mega and Micro Beauty and Fashion Social Media Influencers

Abstract

Fashion and beauty brands leverage social media influencers to shape purchasing decisions, improve cost effectiveness, and reach wider audiences. New conventional wisdom has brands moving away from megainfluencers toward microinfluencers due to greater perceived relatability and trustworthiness. This study employs a novel computational approach integrating network analysis and computational text analysis to understand differences in content and its diffusion through mega- and microinfluencer Twitter networks. Findings debunk conventional wisdom that microinfluencers can best fill unique roles by forging intimate, emotion-laden interpersonal connections. While microinfluencers are more central to two-way dialogue within their networks, megainfluencers garner more affect directed toward them, indicating greater trust. Practical implications for the continued value of megainfluencers and the identification and development of promising microinfluencers are discussed.