Influencer analysis on social networks

Abstract

Along with the growth of social networks, social marketing, and influencer marketing, the analysis of influencers, especially the analysis of micro-influencers on social networks, becomes an interesting topic that attracts many researchers as well as industry organizations. An ideal micro-influencer is a user who is able to create positive content relevance to the business and get high engagement from his/her audience, continuously and consistently. Various approaches have been suggested for this problem but there is still a research gap that no effort meets these requirements yet. Resolving this gap is the main aim of this study.

This study proposes a novel approach for identifying influencers on social networks using three metrics including the amplification factor to evaluate the information propagation, the passion point to measure the user's preference for a brand or its products and services, and the content creation score to estimate user's ability in creating contents on a social network. The main hypothesis is that the approach using these metrics will propose high-performing influencers. The study has been compared with some recent relevant methods as the baseline. It is also tested in the real world and the experiment shows that the proposed method's influencers deliver a react-to-purchase conversion rate's efficiency and a good return on investment in the influencer marketing campaign.