Measuring Influence on Instagram: a Network-oblivious Approach

Abstract

This paper focuses on the problem of scoring and ranking influential users of Instagram, a visual content sharing online social network (OSN). Instagram is the second largest OSN in the world with 700 million active Instagram accounts, 32% of all worldwide Internet users. Among the millions of users, photos shared by more influential users are viewed by more users than posts shared by less influential counterparts. This raises the question of how to identify those influential Instagram users. In our work, we present and discuss the lack of relevant tools and insufficient metrics for influence measurement, focusing on a network oblivious approach and show that the graph-based approach used in other OSNs is a poor fit for Instagram. In our study, we consider user statistics, some of which are more intuitive than others, and several regression models to measure users' influence.