Probabilistic Decision Modeling in Social Networks

Abstract

Bayesian approaches have been successfully applied in social network analysis to study group behaviors such as online information dissemination and voting pattern. The focus has been on estimating the structure and strength of peer influence and its impact on the decisions of an individual. Less attention has been given to incorporating contextual information and individuals' hidden characteristics (or bias). In this work, we examine the social dynamics where social influence and contextual information play pivotal roles in driving one's decision. We design a probabilistic graphical model called CLAP to understand users' decision behavior in a social network, with an emphasis on both social-level and individual-level factors. To this end, the proposed model introduces hidden bias states associated with each actor and jointly estimates each actor's hidden bias state together with the social influence network. We demonstrate the effectiveness of CLAP on two types of social networks, a real-world US Congress network where senators vote on new bills, and online Twitter networks where users debate on the effectiveness of vaccine and lockdown policy during COVID-19. The experiment results show that CLAP outperforms state-of-the-art game theoretic approaches in predicting user decision. Further, the estimated social influence networks by CLAP has high edge homogeniety ratios.