A New Supervised Epidemic Model for Intelligent Viral Content Classification - ÅenoÄlu, Abdulkerim; YavanoÄlu, Uraz; Ãzdemir, Suat (International Journal of Intelligent Systems and Applications in Engineering, 2016)
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.