Opinion Maximization in Social Networks

TL;DR

This paper models opinion formation in social networks to optimize targeted campaigns, proposing algorithms to identify key individuals that maximize positive opinions, with demonstrated efficiency on real data.

Contribution

It formalizes the campaign-design problem as CAMPAIGN, analyzes its complexity, and develops algorithms for effective solution in social networks.

Findings

Algorithms efficiently identify target individuals for positive opinion maximization.

The approach is validated on real social network data.

The problem is shown to be computationally challenging.

Abstract

The process of opinion formation through synthesis and contrast of different viewpoints has been the subject of many studies in economics and social sciences. Today, this process manifests itself also in online social networks and social media. The key characteristic of successful promotion campaigns is that they take into consideration such opinion-formation dynamics in order to create a overall favorable opinion about a specific information item, such as a person, a product, or an idea. In this paper, we adopt a well-established model for social-opinion dynamics and formalize the campaign-design problem as the problem of identifying a set of target individuals whose positive opinion about an information item will maximize the overall positive opinion for the item in the social network. We call this problem CAMPAIGN. We study the complexity of the CAMPAIGN problem, and design algorithms for solving it. Our experiments on real data demonstrate the efficiency and practical utility of our algorithms.