MVAN: Multi-View Attention Networks for Fake News Detection on Social Media

Shiwen Ni, Jiawen Li, Hung Yu Kao

研究成果: Article同行評審

71 引文 斯高帕斯(Scopus)

摘要

Fake news on social media is a widespread and serious problem in today's society. Existing fake news detection methods focus on finding clues from Long text content, such as original news articles and user comments. This paper solves the problem of fake news detection in more realistic scenarios. Only source shot-text tweet and its retweet users are provided without user comments. We develop a novel neural network based model, Multi-View Attention Networks (MVAN) to detect fake news and provide explanations on social media. The MVAN model includes text semantic attention and propagation structure attention, which ensures that our model can capture information and clues both of source tweet content and propagation structure. In addition, the two attention mechanisms in the model can find key clue words in fake news texts and suspicious users in the propagation structure. We conduct experiments on two real-world datasets, and the results demonstrate that MVAN can significantly outperform state-of-the-art methods by 2.5% in accuracy on average, and produce a reasonable explanation.

原文English
文章編號9497048
頁(從 - 到)106907-106917
頁數11
期刊IEEE Access
9
DOIs
出版狀態Published - 2021

All Science Journal Classification (ASJC) codes

  • 一般工程
  • 一般材料科學
  • 一般電腦科學

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