期刊
IEEE ACCESS
卷 9, 期 -, 页码 106907-106917出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3100245
关键词
Social networking (online); Feature extraction; Deep learning; Blogs; Neural networks; Mathematical model; Logic gates; Fake news detection; graph attention networks; attention; deep learning; social media
资金
- Qualcomm through the Taiwan University Research Collaboration Project
- Ministry of Science and Technology, Taiwan [MOST 109-2221-E-006-173, NCKU B109-K027D]
Fake news on social media is a widespread and serious problem. The study developed a novel neural network model, MVAN, which significantly outperforms existing methods in detecting fake news, capturing information from source tweets and propagation structure to provide explanations.
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.
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