Affiliations: [a] Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan
| [b] Department of MIS, Business College, King Khalid University, Saudi Arabia
| [c] Department of Software Engineering, Al-Ain University of Science and Technology, Al-Ain, UAE
Correspondence:
[*]
Corresponding author. Hikmat Ullah Khan, Department of computer science, COMSATS University Islamabad, Wah Campus, Pakistan. E-mail: [email protected].
Abstract: The social web appears to enrich human lives by providing effective applications for online social interactions. Microblogs are one of the most important applications of the social Web. The Microbloggers who influence the social community users through their content in the form of tweets are known as the influential microbloggers. The identification of such influential microbloggers has vast applications in advertising, online marketing, corporate communication, information dissemination, etc. This paper investigates the problem of identifying influential microbloggers by proposing MIPPLA (Model to identify Influential using Productivity, Popularity and Link Analysis) model which integrates the modules of Productivity and Popularity. The Productivity module considers a micro-blogger’s activity and the Popularity module identifies a microbloggers influence in an online social community. In addition, we modify the classic PageRank by utilizing the Twitter features such as retweet, mention, and reply for ranking the influential users. The proposed approaches are evaluated using real-world social networks. The results prove that the MIPPLA model efficiently identifies and ranks the top influential users in an effective manner as compared to the existing techniques.
Keywords: Social web, online social networks, microblogs, influential users, big
data, data mining
