Self Multi-Head Attention-based Convolutional Neural Networks for fake news detection


Autoři: Yong Fang aff001;  Jian Gao aff001;  Cheng Huang aff001;  Hua Peng aff002;  Runpu Wu aff003
Působiště autorů: College of Cybersecurity Sichuan University, Chengdu, Sichuan, China aff001;  College of Electronics and Information Engineering Sichuan University, Chengdu, Sichuan, China aff002;  China Information Technology Security Evaluation Center, Beijing 100085, China aff003
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222713

Souhrn

With the rapid development of the internet, social media has become an essential tool for getting information, and attracted a large number of people join the social media platforms because of its low cost, accessibility and amazing content. It greatly enriches our life. However, its rapid development and widespread also have provided an excellent convenience for the range of fake news, people are constantly exposed to fake news and suffer from it all the time. Fake news usually uses hyperbole to catch people’s eyes with dishonest intention. More importantly, it often misleads the reader and causes people to have wrong perceptions of society. It has the potential for negative impacts on society and individuals. Therefore, it is significative research on detecting fake news. In the paper, we built a model named SMHA-CNN (Self Multi-Head Attention-based Convolutional Neural Networks) that can judge the authenticity of news with high accuracy based only on content by using convolutional neural networks and self multi-head attention mechanism. In order to prove its validity, we conducted experiments on a public dataset and achieved a precision rate of 95.5% with a recall rate of 95.6% under the 5-fold cross-validation. Our experimental result indicates that the model is more effective at detecting fake news.

Klíčová slova:

Convolution – Deep learning – Natural language processing – Neural networks – Semantics – Social media – Word embedding – Lexical semantics


Zdroje

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2019 Číslo 9

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