Opinion Maximization in Social Trust Networks

TL;DR

This paper studies opinion maximization in social trust networks by generalizing opinion dynamics models and developing matrix-based methods, addressing limitations of previous oversimplified models and strict opinion representations.

Contribution

It introduces a generalized opinion dynamics model for social trust networks and proposes two novel matrix-based algorithms for opinion maximization.

Findings

Effective methods demonstrated on real-world datasets

Addresses limitations of previous bipartite opinion models

Provides practical tools for social media marketing strategies

Abstract

Social media sites are now becoming very important platforms for product promotion or marketing campaigns. Therefore, there is broad interest in determining ways to guide a site to react more positively to a product with a limited budget. However, the practical significance of the existing studies on this subject is limited for two reasons. First, most studies have investigated the issue in oversimplified networks in which several important network characteristics are ignored. Second, the opinions of individuals are modeled as bipartite states(e.g., support or not) in numerous studies, however, this setting is too strict for many real scenarios. In this study, we focus on social trust networks(STNs), which have the significant characteristics ignored in the previous studies. We generalized a famed continuous-valued opinion dynamics model for STNs, which is more consistent with real scenarios. We subsequently formalized two novel problems for solving the issue in STNs. Moreover, we developed two matrix-based methods for these two problems and experiments on real-world datasets to demonstrate the practical utility of our methods.