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Maximizing the Spread of Positive Influence by Deadline

Influence maximization has found applications in various fields such as sensor placement, viral marketing, controlling rumor outbreak, etc. In this paper, we propose a targeted approach to influence maximization in polarized networks i.e. networks where we already know or can predict node's opinion about a product or topic. The goal is to find a set of individuals to target, such that positive opinion about a specific topic or the product to be launched is maximized. Another key aspect that is present in most of the existing viral marketing algorithms is that they do not take into account the timeliness of the product adoption. In this paper, we present a framework where we infer the polarity, activity levels of the users, and then select seeds to launch viral marketing campaigns such that positive influence about the product is maximized by the given deadline.