AI-driven Influencer and Market Analysis: A Social Network Approachto Measure E-Commerce Relationships

Authors

  • Juhaida Abu Bakar Data Management & Software Solution Research Lab, School of Computing, Universiti Utara Malaysia, Malaysia
  • Chong Kar Min Data Management & Software Solution Research Lab, School of Computing, Universiti Utara Malaysia, Malaysia
  • Mohd Zulhisham Mohd Radzi Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia
  • Fauziah Baharom Data Management & Software Solution Research Lab, School of Computing, Universiti Utara Malaysia, Malaysia
  • Yuhanis Yusof Data Management & Software Solution Research Lab, School of Computing, Universiti Utara Malaysia, Malaysia
  • Mohamed Ali Saip Data Management & Software Solution Research Lab, School of Computing, Universiti Utara Malaysia, Malaysia
  • Ruziana Muhamad Rasli School of Multimedia Technology & Communication, Universiti Utara Malaysia, Malaysia
  • Muhammad Amirul Ariff Zulkifli Data Management & Software Solution Research Lab, School of Computing, Universiti Utara Malaysia, Malaysia
  • Nuratikah Jamaludin Data Management & Software Solution Research Lab, School of Computing, Universiti Utara Malaysia, Malaysia
  • Mustafa Ali Abuzaraida Department of Computer Science, Faculty of Information Technology, Misurata University, Libya

DOI:

https://doi.org/10.32890/jict2025.24.3.1

Keywords:

Social network analysis, AI-driven market analysis, influencer identification, weighted degree centrality, business decision-making

Abstract

Social network analysis is a process of studying social structures and relationships using graph theory and data analysis techniques. It involves mapping and measuring connections and entities in a network. However, on online selling platforms, identifying influential entities such as individuals and high-value products remains a challenge due to the complexity of customer and seller interactions. This study aims to assess seller performance and product lifetime value using AI-driven network analysis involving a measure of centrality. AI-driven network analysis utilises artificial intelligence (AI) to identify influential individuals and predict emerging trends in consumer engagement. It uses weighted degree and betweenness centrality to assess their effectiveness in identifying influential entities, including sellers, products, or organisations in a commercial network. Weighted degree centrality measures the strength and frequency of direct connections, while betweenness centrality identifies entities that act as intermediaries across different network segments. The analysis reveals that weighted degree centrality, with a value of 3190 for annual seller performance, is more closely aligned with actual sales performance and stakeholder assessments, making it a more suitable metric for supporting business decisions in this context. The findings demonstrate that AI-driven analytics enable businesses to consistently identify high-performing sellers and products based on their structural positions within the network. It contributes to the development of more targeted marketing strategies, systematic recognition of top performers, and enhanced customer engagement through data-informed decision-making. Future research may explore the integration of dynamic network modelling with multi-layered e-commerce networks, thereby increasing the depth of analysis across various platforms and industries.

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Additional Files

Published

31-07-2025

Issue

Vol. 24 No. 3 (2025): Journal of Information and Communication Technology (JICT) Vol.24, No.3, July 2025

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Abu Bakar, J., Min, C. K., Mohd Radzi, M. Z., Baharom, F., Yusof, Y., Saip, M. A., Muhamad Rasli, R., Zulkifli, M. A. A., Jamaludin, N., & Abuzaraida, M. A. (2025). AI-driven Influencer and Market Analysis: A Social Network Approachto Measure E-Commerce Relationships. Journal of Information and Communication Technology, 24(3), 1-21. https://doi.org/10.32890/jict2025.24.3.1

Research impact

Harvested 2026-09-04
2 citations, from Scopus — the highest of the sources checked

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Identifiers DOI 10.32890/jict2025.24.3.1 OpenAlex W4412815026 Scopus 105017728754

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