Online Behavior Balancing Model for Influence Maximization in Twitter

Abstract

Background: Social influence estimation is an important aspect of viral marketing. The majority of the influence estimation models for online social networks are either based on Independent Cascade (IC) or Linear Threshold (LT) models. These models are based on some hypothesis: (1) process of influence is irreversible; (2) classification of user’s status is binary, i.e., either influenced or non-influenced; (3) process of influence is either single person’s dominance or collective dominance but not the both at the same time. However, these assumptions are not always valid in the real world, as human behavior is unpredictable. Objective: Develop a generalized model to handle the primary assumptions of the existing influence estimation models. Methods: This paper proposes a Behavior Balancing (BB) Model, which is a hybrid of IC and LT models and counters the underlying assumptions of the contemporary models. Results: The efficacy of the proposed model to deal with various scenarios is evaluated over six different twitter election integrity datasets. Results depict that BB model is able to handle the stochastic behavior of the user with up to 35% improved accuracy in influence estimation as compared to the contemporary counterparts. Conclusion: The BB model employs the activity or interaction information of the user over the social network platform in the estimation of diffusion and allows any user to alter their opinion at any time without compromising the accuracy of the predictions.