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World Wide Web

, Volume 21, Issue 3, pp 663–685 | Cite as

Modelling majority and expert influences on opinion formation in online social networks

  • Rajkumar Das
  • Joarder Kamruzzaman
  • Gour Karmakar
Article

Abstract

Two most important social influences that shape the opinion formation process are: (i) the majority influence caused by the existence of a large group of people sharing similar opinions and (ii) the expert influence originated from the presence of experts in a social group. When these two effects contradict each other in real life, they may pull the public opinions towards their respective directions. Existing models on opinion formation utilised the idea of expertise levels in conjunction with the expressed opinions of the agents to encapsulate the expert effect. However, they have disregarded the explicit consideration of the majority effect, and thereby failed to capture the concurrent and combined impact of these two influences on opinion evolution. To represent the majority and expert impacts, we explicitly use the concept of opinion consistency and expertise level consistency respectively in an innovative way by capitalizing the notion of entropy in measuring the homogeneity of a group. Consequently, our model successfully captures the opinion dynamics under the concomitant influence of majority and expert. We validate the efficacy of our model in capturing opinion dynamics in a real world scenario using the opinion evolution traces collected from a widely used online social network (OSN) platform. Moreover, simulation results reveal the impact of the aforementioned effects, and confirm that our model can properly capture the consensus, polarization and fragmentation properties of public opinion. Our model is also compared with some recent models to evaluate its performance in both real world and simulated environments.

Keywords

Opinion formation Online social networks Majority Expert Consistency 

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Faculty of Information TechnologyMonash UniversityVictoriaAustralia
  2. 2.School of Engineering and Information TechnologyFederation University AustraliaVictoriaAustralia

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