Persuasion driven influence propagation in social networks

Abstract

With the explosive growth of online social network services such as Facebook and Twitter, people now are providing ample opportunities to share information, ideas, and innovations among others. Studying social influence and information diffusion in online social networks can be extremely useful for various real-life applications, notably influencer marketing and viral marketing. Influence maximization problem, motivated by viral marketing applications, has received extensive attentions from social network analysis community in recent years. The goal of the problem is to find a small set of k seed nodes in a social network that maximize the influence spread under certain influence propagation models. Based on the widely adopted Independent Cascade model, a Persuasiveness Aware Cascade (PAC) model which considers social persuasion in influence propagation is proposed. In this model, the user-to-user influence probability is estimated by three types of social persuasion, namely, tie strength, peer conformity, and authority. Experiments conducted over real-world social networks suggest that the proposed model with the new social persuasion measures is more effective in describing real-world influence propagation than the well-studied propagation models for influence maximization.