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

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

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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 meta-data 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.

Information credibility evaluationRumor detectionSocial mediaText classification

WU Lianwei、RAO Yuan、YU Hualei、WANG Yiming、AMBREEN Nazir

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Lab of Social Intelligence and Complex Data Processing, School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China

This work is supported by the World-Class Universities(Disciplines)and the Characteristic Development Guidance Funds for the CenNational Natural Science Foundation of ChinaMinistry of Education Fund Project"Cloud Number Integration Science and Education Innovation"Basic Scientific Research Operating Expenses of Central UniversitiesShaanxi Provincial Science and Technology Department Collaborative Innovation ProjectShaanxi Soft Science Key Project

PY3A022F0208072017B00030ZDYF20170062015XT-212013KRZ10

2019

10.1049/cje.2019.05.002

中国电子杂志(英文版)

中国电子杂志(英文版)

CSTPCDCSCDSCIEI
ISSN：1022-4653
年,卷(期)：2019.28(4)
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