维普中文期刊产品整合服务

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

查看全文 作　　者：WU [1]Lianwei;RAO [1]Yuan;YU [1]Hualei;WANG [1]Yiming;AMBREEN [1]Nazir 高影响力作者 机构地区：[1]Lab of Social Intelligence and Complex Data Processing, School of Software Engineering, Xi’an Jiaotong University高影响力机构 出　　处：《Chinese Journal of Electronics》索引2019年第28卷第4期,共10页高影响力期刊 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. 关 键 词：Information CREDIBILITY evalua tion RUMOR det ection SOCIAL media TEXT classification.
相关文献

参考文献(35)

引证文献(4)

网站首页 | 关于我们 | 联系我们 | 产品服务 | 客服中心 | 广告服务 | 版权声明 | 网站联盟 | 友情链接 | 售卡网点

版权所有© 渝B2-20050021-1　渝公网安备 50019002500403号　违法和不良信息举报中心

互联网出版许可证 新出网证(渝)字10号 全国400电话 - 免长途话费