A Multi-semantics Classification Method Based on Deep Learning for Incredible Messages on Social Media  

作　　者：WU Lianwei RAO Yuan YU Hualei WANG Yiming AMBREEN Nazir 

机构地区：[1]Lab of Social Intelligence and Complex Data Processing, School of Software Engineering, Xi’an Jiaotong University

出　　处：《Chinese Journal of Electronics》2019年第4期754-763,共10页电子学报（英文版）

基　　金：supported by the World-Class Universities(Disciplines);the Characteristic Development Guidance Funds for the Central Universities(No.PY3A022);the National Natural Science Foundation of China(No.F020807);Ministry of Education Fund Project “Cloud Number Integration Science and Education Innovation”(No.2017B00030);Basic Scientific Research Operating Expenses of Central Universities(No.ZDYF2017006);Shaanxi Provincial Science and Technology Department Collaborative Innovation Project(No.2015XT-21);Shaanxi Soft Science Key Project(No.2013KRZ10)

摘　　要：How to classify incredible messages has attracted great attention from academic and industry nowadays.The recent work mainly focuses on one type of incredible messages(a.k.a rumors or fake news)and achieves some success to detect them.The existing problem is that incredible messages have different types on social media,and rumors or fake news cannot represent all incredible messages.Based on this,in the paper,we divide messages on social media into five types based on three dimensions of information evaluation metrics.And a novel method is proposed based on deep learning for classifying the five types of incredible messages on social media.More specifically,we use attention mechanism to obtain deep text semantic features and strengthen emotional semantics features,meanwhile,construct universal metadata as auxiliary features,concatenating them for incredible messages classification.A series of experiments on two representative real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods.How to classify incredible messages has attracted great attention from academic and industry nowadays. The recent work mainly focuses on one type of incredible messages(a.k.a rumors or fake news) and achieves some success to detect them. The existing problem is that incredible messages have different types on social media, and rumors or fake news cannot represent all incredible messages. Based on this, in the paper, we divide messages on social media into five types based on three dimensions of information evaluation metrics. And a novel method is proposed based on deep learning for classifying the five types of incredible messages on social media.More specifically, we use attention mechanism to obtain deep text semantic features and strengthen emotional semantics features, meanwhile, construct universal metadata as auxiliary features, concatenating them for incredible messages classification. A series of experiments on two representative real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

关 键 词：Information CREDIBILITY evalua tion RUMOR det ection SOCIAL media TEXT classification. 

分 类 号：TN[电子电信]