Method for Mining the Opinion Leaders in Social Networks

Mustafa K. Alasadi
International Journal of Computational and Electronic Aspects in Engineering
Volume 5: Issue 4, December 2024, pp 141-147

Author's Information
Mustafa K. Alasadi1 
Corresponding Author
1Faculty of Computer Science and Information Technology, University of Sumer, Al-Rifai 64005, Thi Qar, Iraq
mustafa.kamil@uos.edu.iq
Research Paper -- Peer Research Papered
First online on – 2 December 2024

Open Access article under Creative Commons License

Cite this article –Mustafa K. Alasadi “Method for Mining the Opinion Leaders in Social Networks ”, International Journal of Computational and Electronic Aspects in Engineering, RAME Publishers, vol. 5, Issue 4, pp. 141-147, 2024.
https://doi.org/10.26706/ijceae.5.4.20241102

Abstract:-
Social network is one of the most prominent phenomena that greatly influence life in the present time. These media have changed the way people interact with others, the ways they communicate, and participate in global and local events. individuals on social network platform greatly influence each other's opinions and trends, but the degree of influence varies from one person to another greatly depending on many factors, such as fame, published content, target audience, and interaction with followers. This leads to varying influence of individuals on these platforms. Influential people can be opinion leaders on social platforms. In fact, these leaders constitute a large part of the process that influences the decisions of many individuals and companies, whether at the level of product selection or cultural and social trends. Discovering and identifying opinion leaders is very important for several reasons: directing public opinion, marketing and advertising, political and social analysis, and others. In this paper, we proposed a method to discover opinion leaders by focusing on the structure of social graphs and the position occupied by individuals. The centrality of nodes representing individuals were calculated by utilizing two essential measures: the degree centrality and betweenness centrality, but with different weights that were determined in a studied way. The method was tested on real-world dataset for three social networks and the results were promising when compared with the baseline.
Index Terms:-
social network; Opinion Leaders; Degree Centrality; Betweenness Centrality
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