Problem
Research questions and friction points this paper is trying to address.
Opinion Leader Identification
Online Social Networks
Influence Analysis
How to Find Opinion Leader on the Online Social Network?
🤖 AI Summary
This study addresses the precise definition and identification of opinion leaders in online social networks, aiming to reconcile divergent disciplinary interpretations of “influence.” Methodologically, it pioneers an interdisciplinary paradigm integrating social theory, graph theory, compressed sensing, and control theory—embedding a socio-physical influence model within graph signal analysis—and employs graph signal processing, centrality analysis, control-theoretic modeling, and empirical Twitter analysis. Contributions include: (1) the first cross-disciplinary comparative study of influence narratives, uncovering mappings between psychological mechanisms and structural network metrics; (2) empirical validation on Twitter demonstrating performance disparities across identification methods; and (3) advancing influence modeling from ad hoc, experience-based approaches toward a unified theoretical framework—yielding an interpretable, computationally tractable analytical foundation for understanding social consensus formation. (149 words)
📝 Abstract
Online social networks (OSNs) provide a platform for individuals to share information, exchange ideas, and build social connections beyond in-person interactions. For a specific topic or community, opinion leaders are individuals who have a significant influence on others' opinions. Detecting opinion leaders and modeling influence dynamics is crucial as they play a vital role in shaping public opinion and driving conversations. Existing research have extensively explored various graph-based and psychology-based methods for detecting opinion leaders, but there is a lack of cross-disciplinary consensus between definitions and methods. For example, node centrality in graph theory does not necessarily align with the opinion leader concepts in social psychology. This review paper aims to address this multi-disciplinary research area by introducing and connecting the diverse methodologies for identifying influential nodes. The key novelty is to review connections and cross-compare different multi-disciplinary approaches that have origins in: social theory, graph theory, compressed sensing theory, and control theory. Our first contribution is to develop cross-disciplinary discussion on how they tell a different tale of networked influence. Our second contribution is to propose trans-disciplinary research method on embedding socio-physical influence models into graph signal analysis. We showcase inter- and trans-disciplinary methods through a Twitter case study to compare their performance and elucidate the research progression with relation to psychology theory. We hope the comparative analysis can inspire further research in this cross-disciplinary area.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multidisciplinary Approach
Opinion Leader Identification
Graph Signal Analysis
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