Leveraging the Social Network Structure of Influencers to Understand and Predict User Engagement

Abstract

As collaborations between brands and social media influencers become increasingly popular, predicting and understanding the capacity of an influencer to generate user engagement (such as likes and comments) has garnered increasing attention from researchers. Not surprisingly, managers have been relying on follower-based statistics to identify individuals with potential to reach a vast number of users on social media. However, this approach may often direct managers to accounts with millions of followers accompanied with high recruiting costs. In this paper, we argue that the network structure of influencers is a useful measure for capturing an influencer’s ability to generate engagement. Using Instagram data, we perform a deep-learning analysis on the social interaction network of influencers and show that the network structure alone explains a large share of the variations in user engagement, even outperforming traditionally used variables such as the number of followers in the case of comments. We also show that many insights can be obtained from the network structure. Notably, we find that high-performing influencers form elite sub-communities that may not be central to the larger social network. This study contributes to the emergent literature on the importance of social ties in the digital environment.