Affiliation:
1. Department of Computer Science & Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India
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.
Publisher
Bentham Science Publishers Ltd.
Cited by
1 articles.
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