A New Supervised Epidemic Model for Intelligent Viral Content Classification

Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 216 - 221, 26.12.2016

Abdulkerim Şenoğlu , Uraz Yavanoğlu , Suat Özdemir

Öz

In this study, we propose an
information diffusion model which is based on neural networks, artificial
intelligence and supervised epidemic approach. We collected epidemically
diffused data from Twitter with supervision to create a ranking system that forms
the base of our diffusion model. The collected data is also used to train the
proposed model. The outputs of the proposed model are shown to be useful for
the provenance problem and the diffusion prediction systems in both physical
and social networks. Knowing the viral content beforehand can be used in
advertisement, industry, politics or any other end user that wants to reach a large
number of people. Our performance analysis show that the proposed model can
achieve over 90% training success rate and 78% test success rate of classifying
viral content which is better than some of the existing models.

Anahtar Kelimeler

Artificial Neural Networks (ANN), Epidemic approach, Supervised learning, Information diffusion

Kaynakça

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