From type-2 fuzzy rate-based neural networks to social networks' behaviors

the booming environment of social networks like Facebook, Twitter and Instagram is expanding more and more instantly. These rapid changes increase the complexity of these networks simultaneously. In this paper, a new approach is proposed to model some complex behaviors of social networks, including “attention inversion”. The model consists of an embedded type-2 fuzzy inference system in collaboration with rate-based neural networks. Finally, an experiment is performed on some selected Twitter hashtags to represent the performance of the model.