A literature review on opinion leaders detection in social networks: approaches, methods and metrics
Year: 2026
Type: article
Abstract: A social network serves as a dynamic arena where online users actively participate in exchanges involving the sharing of opinions and ideas. Amidst this digital landscape, certain individuals stand out as opinion leaders, wielding the power to shape the perspectives of their peers. Opinion leaders are active in diverse domains such as marketing, advertising, politics, health, finance and investment, education and scientific research, etc. However, Opinion Leaders Detection (OLD) faces several challenges, including data collection and data quality, the diversity of social platforms, the network complexity, the dynamic nature of social networks, the contextualization of opinion leaders, and the importance of ethical concerns. These challenges have led to a rich and scattered literature, with a wide range of methods and techniques developed to address OLD challenges. This paper reviews established methods in OLD, categorizing them based on criteria such as social network platforms, approaches, methods, techniques, and evaluation metrics. It highlights the benefits and limitations of each approach and method, aiming to provide researchers with a comprehensive overview and suggesting ways to overcome the limitations of conventional methods. The paper also discusses current literature and identifies future research directions to address existing gaps.
Source: Discover Computing
Institutions: Tunis University, Manouba University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,520
OpenAlex ID: W7201848673