  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.
