Smart Information Spreading for Opinion Maximization in Social Networks

TL;DR

This paper introduces a novel approach to opinion maximization in social networks through efficient information spreading, utilizing dynamic Bayesian networks and learning algorithms to outperform random spreading strategies.

Contribution

It proposes a new model combining multiple sources and social learning, with centralized and decentralized algorithms for effective opinion maximization.

Findings

Smart spreading outperforms random spreading significantly.

Unfavorably located smart sources can outperform favorably placed random sources.

Proposed methods are effective in both synthetic and real-world networks.

Abstract

The goal of opinion maximization is to maximize the positive view towards a product, an ideology or any entity among the individuals in social networks. So far, opinion maximization is mainly studied as finding a set of influential nodes for fast content dissemination in a social network. In this paper, we propose a novel approach to solve the problem, where opinion maximization is achieved through efficient information spreading. In our model, multiple sources inject information continuously into the network, while the regular nodes with heterogeneous social learning abilities spread the information to their acquaintances through gossip mechanism. One of the sources employs smart information spreading and the rest spread information randomly. We model the social interactions and evolution of opinions as a dynamic Bayesian network (DBN), using which the opinion maximization is formulated as a sequential decision problem. Since the problem is intractable, we develop multiple variants of centralized and decentralized learning algorithms to obtain approximate solutions. Through simulations in synthetic and real-world networks, we demonstrate two key results: 1) the proposed methods perform better than random spreading by a large margin, and 2) even though the smart source (that spreads the desired content) is unfavorably located in the network, it can outperform the contending random sources located at favorable positions.