How to Find Opinion Leader on the Online Social Network?

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 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.