WU Lianwei, RAO Yuan, YU Hualei, WANG Yiming, AMBREEN Nazir. A Multi-semantics Classification Method Based on Deep Learning for Incredible Messages on Social Media[J]. Chinese Journal of Electronics, 2019, 28(4): 754-763. doi: 10.1049/cje.2019.05.002
Citation: WU Lianwei, RAO Yuan, YU Hualei, WANG Yiming, AMBREEN Nazir. A Multi-semantics Classification Method Based on Deep Learning for Incredible Messages on Social Media[J]. Chinese Journal of Electronics, 2019, 28(4): 754-763. doi: 10.1049/cje.2019.05.002

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

doi: 10.1049/cje.2019.05.002
Funds:  This work is supported by the World-Class Universities(Disciplines) and 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), and Shaanxi Soft Science Key Project (No.2013KRZ10).
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  • Corresponding author: RAO Yuan (corresponding author) was born in 1973. He received the Ph.D. degree in computer science from Xi'an Jiaotong University, in 2005. He is a professor of Xi'an Jiaotong University His research interests include Social Intelligence and Complex Data Processing. (Email:yuanrao@163.com)
  • Received Date: 2018-06-25
  • Rev Recd Date: 2018-09-06
  • Publish Date: 2019-07-10
  • 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.
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