Behavioral Information Diffusion for Opinion Maximization in Online Social Networks

Abstract

Online social networks provide a platform to diffuse information and influence people's opinion. Conventional models for information diffusion do not take into account the specifics of each users’ personality, behavior, and their opinion. This work adopts the “Big Five” model from the social sciences to ascribe each user node with a personality. We propose a behavioral independent cascade (BIC) model that considers the personalities and opinions of user nodes when computing propagation probabilities for diffusion. We use this model to study the opinion maximization (OM) problem and prove it is NP-hard under our BIC model. Under the BIC model, we show that the objective function of the proposed OM problem is not submodular. We then propose an algorithm to solve the OM problem in linear-time based on a state-of-the-art influence maximization (IM) algorithm. We run extensive simulations under four cases where initial opinion is distributed in polarized/non-polarized and community/non-community cases. We find that when communities are polarized, activating a large number of nodes is ineffective towards maximizing opinion. Further, we find that our proposed algorithm outperforms state-of-the-art IM algorithms in terms of maximizing opinion in uniform opinion distribution—despite activating fewer nodes to be spreaders.