A Study on the Centrality Measures to Determine Social Media Influencers in Twitter

Abstract

In recent years, many people have been influenced by YouTuber stars, celebrities, and influencers to their online products, especially on Twitter. Many celebrities who have many followers would start promoting certain products to all their fans and followers. Fans and followers have to share the show with their friends to gain more popularity among other audiences. Celebrities have played a vital role in corporate brands as they can promote the brand products and have also attracted many people who are interested in purchasing the products. Therefore, the group of celebrities can be called social media influencers (SMI). In social network analysis (SNA), a node value influential a network called centrality. Centrality is defined as a value that represents how many connections are from nodes to other nodes (Wasserman & Faust, 1994). There are many methods to define centrality to identify the effect of each node in a social network such as Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), and Eigenvector Centrality (EC). Among these, the eigenvector centrality will give the most influential node in a network. A node with the highest eigenvector value among the other nodes is the most influential/important node in a network. Data was collected from Twitter using the Twitter API with the hashtag #pizzzahut. The goal of this research is to identify the main influencer in the Twitter community. It applied the eigenvector centrality to observe the effect of the centrality value for Twitter data. The result shows that there is a significant difference among the 3 most influential users. This result will be used for future research that will be focused on small and medium enterprise (SME) Twitter data. This research is held a comparison analysis between the 4 centrality measurements approach for determining the most influential user with social network Twitter as its case study. Keywords: Social Media Influencer, Social Network Theory, Centrality Measures