A Projected Gradient Method for Opinion Optimization with Limited Changes of Susceptibility to Persuasion

TL;DR

This paper introduces a new opinion optimization model that limits changes in susceptibility, using a projected gradient method suitable for large-scale social networks, and demonstrates its effectiveness through experiments.

Contribution

It proposes a novel opinion optimization model with limited susceptibility changes and develops a scalable projected gradient algorithm for large networks.

Findings

The proposed method outperforms baseline algorithms in experiments.

The model effectively limits susceptibility changes while optimizing opinions.

Scalable algorithm handles millions of agents efficiently.

Abstract

Many social phenomena are triggered by public opinion that is formed in the process of opinion exchange among individuals. To date, from the engineering point of view, a large body of work has been devoted to studying how to manipulate individual opinions so as to guide public opinion towards the desired state. Recently, Abebe et al. (KDD 2018) have initiated the study of the impact of interventions at the level of susceptibility rather than the interventions that directly modify individual opinions themselves. For the model, Chan et al. (The Web Conference 2019) designed a local search algorithm to find an optimal solution in polynomial time. However, it can be seen that the solution obtained by solving the above model might not be implemented in real-world scenarios. In fact, as we do not consider the amount of changes of the susceptibility, it would be too costly to change the susceptibility values for agents based on the solution. In this paper, we study an opinion optimization model that is able to limit the amount of changes of the susceptibility in various forms. First we introduce a novel opinion optimization model, where the initial susceptibility values are given as additional input and the feasible region is defined using the $\ell_p$-ball centered at the initial susceptibility vector. For the proposed model, we design a projected gradient method that is applicable to the case where there are millions of agents. Finally we conduct thorough experiments using a variety of real-world social networks and demonstrate that the proposed algorithm outperforms baseline methods.