How to Find Opinion Leader on the Online Social Network?
Bailu Jin, Mengbang Zou, Zhuangkun Wei, Weisi Guo

TL;DR
This paper reviews and compares diverse multidisciplinary methods for identifying opinion leaders in online social networks, highlighting their differences and proposing a trans-disciplinary approach with a case study on Twitter.
Contribution
It introduces a cross-disciplinary review connecting social, graph, compressed sensing, and control theories, and proposes a novel trans-disciplinary method for influence modeling.
Findings
Different methodologies reveal varied influence dynamics.
Trans-disciplinary embedding improves influence detection.
Case study demonstrates method effectiveness on Twitter data.
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…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Complex Network Analysis Techniques · Social Media and Politics
