IDENTIFICATION OF POTENTIAL INFLUENCERS IN FACEBOOK USING NETWORK GRAPH METRICS

Social Media have been widely adopted by many businesses organizations. Social media sites like Facebook, Twitter are common phenomena for these companies to offer business services to their customers. Social networks are becoming a critical media for business and marketing. The amount of interaction generated at these sites are in the form like post, comments, Tweets, likes etc. influences the attitude and behavior of others. It is important to monitoring and identifying the potential influencer at these sites for better business opportunities and growth. For example, in the context of marketing on social networks, it is necessary to identify which users should be involved in an advertisement campaign. Each user is described by a large number of attributes, which transforms the problem of identifying relevant users in a needle in a haystack problem. It is important to quickly extract a limited number of meaningful characteristics that can be used to identify relevant users or potential influencers. Potential influencers may play a key role in the content dissemination and how users may be affected by different dissemination strategies. Network Graph Metrics such as Degree, Betweenness Centrality, Eigenvector Centrality, Clustering Coefficient, Closeness Centrality are used to identify the potential users in the social media networks. This paper presents a case study performed over a Facebook user with 250+ connections to explain how potential influencers are identified using Network Graph Metrics through visual representation. It is found those potential influencers are easy target for businesses to tap and keep them engaged for growth of their businesses.