Neural Network Based Attention Degree Prediction for Internet Incidents in One-Crest Period

Author

He, Sha ; Wang, Yuzi ; Wang, Yue ; Zhang, Qingjie ; Zhang, Yuejin ; Wang, Tianmei

Author_Institution

Sch. of Inf., Central Univ. of Finance & Econ., Beijing, China

fYear

2012

fDate

20-21 Oct. 2012

Firstpage

153

Lastpage

158

Abstract

Observing an Internet incident, we find that its attention degrees develop in multiple wave crests. We propose a basic model to predict the trend of one wave crest based on back propagation (BP) neural network. Simulation experiments show that our model can predict one-crest trend of an Internet incident under the assumption that its maximum attention degree can be estimated. Our work can serve as an auxiliary tool for social or commercial workers to make decisions based on public opinions.

Keywords

Internet; backpropagation; decision making; neural nets; BP neural network; Internet incidents; back propagation neural network; commercial workers; decision making; maximum attention degree; multiple wave crests; neural network based attention degree prediction; one-crest period; public opinions; social workers; Biological neural networks; Educational institutions; Internet; Market research; Predictive models; Training; BP neural network; Internet incidents; attention degree; forcasting; public opinions;

fLanguage

English

Publisher

ieee

Conference_Titel

Management of e-Commerce and e-Government (ICMeCG), 2012 International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4673-2943-9

Type

conf

DOI

10.1109/ICMeCG.2012.46

Filename

6374899