Analyzing opinion dynamics in online social networks

Robin Cohen; Alan Tsang; Krishna Vaidyanathan; Haotian Zhang; Robin Cohen; Alan Tsang; Krishna Vaidyanathan; Haotian Zhang

doi:10.3934/bdia.2016011

Big Data and Information Analytics

2016, Volume 1, Issue 4: 279-298. doi: 10.3934/bdia.2016011

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Analyzing opinion dynamics in online social networks

Cheriton School of Computer Science University of Waterloo Waterloo, Ontario, Canada

Published: 01 October 2016

In this paper, we examine the challenge of performing analyses of opinion dynamics in online social networks. We present a model for studying the influence exerted by peers within the network, emphasizing the role that skepticism can play with respect to establishing consensus of opinion. From here, we focus on some key extensions to the model, with respect to the nature of peers (their familiarity relationships, their empathy) and the presence of peers with particular profiles, as well as with specific clustering of peer relationships. Specifically, we show that the influence of trusted confidants on individuals behaves in a predictable fashion; moreover, we show that the underlying model is robust to individual variations in empathy within the population. These empirical results provide important insights to those seeking to examine and analyze patterns of influence within social networks.
- Multiagent system,
- information analytics,
- online social networks,
- opinion dynamics,
- information diffusion
Citation: Robin Cohen, Alan Tsang, Krishna Vaidyanathan, Haotian Zhang. Analyzing opinion dynamics in online social networks[J]. Big Data and Information Analytics, 2016, 1(4): 279-298. doi: 10.3934/bdia.2016011

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Abstract

In this paper, we examine the challenge of performing analyses of opinion dynamics in online social networks. We present a model for studying the influence exerted by peers within the network, emphasizing the role that skepticism can play with respect to establishing consensus of opinion. From here, we focus on some key extensions to the model, with respect to the nature of peers (their familiarity relationships, their empathy) and the presence of peers with particular profiles, as well as with specific clustering of peer relationships. Specifically, we show that the influence of trusted confidants on individuals behaves in a predictable fashion; moreover, we show that the underlying model is robust to individual variations in empathy within the population. These empirical results provide important insights to those seeking to examine and analyze patterns of influence within social networks.

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