Open AccessA Multi‐semantics Classification Method Based on Deep Learning for Incredible Messages on Social Media
Author(s) -
Wu Lianwei,
Rao Yuan,
Yu Hualei,
Wang Yiming,
Ambreen Nazir
Publication year - 2019
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2019.05.002
Subject(s) - computer science , semantics (computer science) , social media , metadata , deep learning , construct (python library) , artificial intelligence , mechanism (biology) , information retrieval , world wide web , programming language , philosophy , epistemology
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.