Rumor Classification Model Based on Deep Convolutional Neural Networks

Rui SUN, Yong ZHONG, Wan-bo LUO

Abstract


With the rapid development of mobile Internet and big data technology, the recognition classification and trend prediction of rumors became has important research subject. At present, the traditional methods represented by the hidden markov model and support vector machine (SVM) has many problems, such as low classification accuracy, excessive reliance on long-term historical data and weak system practicability. Based on the new generation of artificial intelligence theory, such as deep learning, this paper proposed a rumor classification model based on deep convolutional neural networks. By training deep convolutional neural network, the classifier was constructed to solve the problem of high accuracy identification and classification of network rumors.

Keywords


Deep convolutional neural network, Rumor classification, Deep learning


DOI
10.12783/dtcse/iece2018/26597

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