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Popularity Prediction of Online News Based on Radial Basis Function Neural Networks with Factor Methodology

Wei Wu,
Wen-Cai Du,
Hong-Zhou Xu,
Hui Zhou,
Meng-Xing Huang,

Abstract


Online news reflects the dramatically increasing trend of social network use. Understanding what type of online news is popular and easy to spread to the public is a valuable focus for media influence analysis and social marketing. By abstracting detailed characteristics of online news, important influential factors are selected from diverse variables according to the principle component method and function approximation. In consideration of the high-dimensionality of the popularity ranking model, back-propagation neural networks (BPNN) was employed to predict popularity using artificial neural networks. The simulation results compare various forecasting methods based on factors achieved in previous work. This provides an effective prediction model according to real situations, with an accuracy level of 95%.

Keywords


Online news popularity; Back-propagation neural networks; Factor analysis; Model identification; Neural network prediction

Citation Format:
Wei Wu, Wen-Cai Du, Hong-Zhou Xu, Hui Zhou, Meng-Xing Huang, "Popularity Prediction of Online News Based on Radial Basis Function Neural Networks with Factor Methodology," Journal of Internet Technology, vol. 17, no. 5 , pp. 915-927, Sep. 2016.

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