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A Scalable Data Mining Model for Social Media Influencer Identification

  • Jyoti Sunil More
  • Chelpa Lingam
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 628)

Abstract

Social network mining is a growing research area which combines together different fields such as machine learning, graph theory, parallel algorithms, data mining, optimization, etc., with the aim of dealing with issues like behavior analysis, finding interacting groups, finding influencers, information diffusion, etc. in a social network. This paper deals with one of these important issues i.e., Influencer Identification in social networks. This paper presents a data mining modelling approach for a twitter network, to find the most influential user among the given pair of users. This could be scaled over the entire network. We used a data mining model to score the test data and predict the influential user among the given pair of users. This approach of modeling can potentially be used for building many of the marketing and sales strategies wherein the influencer may be motivated for diffusing information or new ideas.

Keywords

Data mining Influencers Social network mining Decision tree Logistic regression 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  1. 1.Ramrao Adik Institute of TechnologyMumbaiIndia
  2. 2.Department of Computer EngineeringLokmanya Tilak College of Engineering (Affiliated to University of Mumbai)Navi MumbaiIndia
  3. 3.Department of Computer EngineeringPillai’s HOC College of Engineering (Affiliated to University of Mumbai)RasayaniIndia

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