Multi-View Representations for Fake News Detection

doi:10.3969/j.issn.1671-1122.2024.03.009

Abstract

Abstract:

Social networks have become a major channel for people to access and share information in their daily lives, while also providing shortcuts for the spread of fake news. Nowadays, the detection of online fake news has been widely concerned and studied by the academic community, but the current methods lack in-depth exploration based on multiple perspectives of news or ignore the different directions of different information in news. In order to provide a more comprehensive perspective for fake news detection task, this paper proposed a multi-view representations for fake news detection (MVRFD) model based on three perspectives: news content, user information and news propagation. Firstly, the co-attention mechanism was used to represent the multimodal information in news content, and the graph neural network with different directions was used to aggregate user information and views in the process of news transmission. Then the dual-co-attention mechanism was used to realize the information interaction between multiple perspectives. Finally, the features of news content and the news context were integrated. Experiments on the publicly available dataset show that the proposed model achieves 96.7% accuracy and 96.8% F1 score, which are better than the mainstream text processing models and single-view-based detection models.

Key words: fake news detection, graph neural network, multimodal representation, attention mechanism, multi-view representation

CLC Number:

TP309

ZHANG Xinyou, SUN Feng, FENG Li, XING Huanlai. Multi-View Representations for Fake News Detection[J]. Netinfo Security, 2024, 24(3): 438-448.

Figures/Tables 8

References 30

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统计数据	数量
帖子	3805656个
用户	2746818个
图片	3845张
新闻事件	4664个
真实新闻	2351个
虚假新闻	2313个
事件的平均传播时间	2460.7 h
每个事件平均帖子数	816个
单个事件最大帖子数	59318个
单个事件最小帖子数	10个
最小转发深度	2
最大转发深度	73
平均转发深度	6.19
转发深度超过5的事件数	2198个

Model	准确率	虚假新闻			真实新闻
Model	准确率	精确度	召回率	F1	精确度	召回率	F1
LSTM	87.8 %	88.5 %	87.7 %	87.9 %	86.5 %	88.1 %	87.0 %
BERT	89.7 %	88.7 %	91.4 %	89.9 %	90.5 %	87.9 %	89.0 %
BERT+Attention	91.1 %	91.0 %	91.8 %	91.3 %	91.3 %	90.1 %	90.6 %
TextCNN	90.4 %	90.3 %	91.5 %	90.5 %	91.0 %	89.9 %	90.0 %
CARMN	92.5 %	92.2 %	92.7 %	92.4 %	92.7 %	92.3 %	92.5 %
Bi-GCN	94.4 %	92.6 %	96.8 %	94.6 %	96.6 %	92.0 %	94.1 %
DA-GCN	94.4 %	94.1 %	94.6 %	94.4 %	94.7 %	94.1 %	94.4 %
DAN-Tree	95.8 %	94.6 %	97.2 %	95.8 %	97.2 %	94.5 %	95.8 %
MVRFD	96.7 %	96.0 %	97.7 %	96.8 %	97.5 %	95.5 %	96.4 %