Abstract
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.
Highlights
With the rapid development of social media platforms, such as Twitter, fake news can spread rapidly on the internet and affect people’s lives and judgment
THE PROPOSED MVAN MODEL We propose a novel neural network model, multi-view attention networks (MVAN), to detect fake news based on the source tweet and its propagation structure
We use the mean value of other user features in the same propagation structure to fill it
Summary
With the rapid development of social media platforms, such as Twitter, fake news can spread rapidly on the internet and affect people’s lives and judgment. Liu [9] modelled the propagation path as a multivariate time series and applied a combination of RNN and convolution neural network (CNN) to capture the changes of user characteristics along the propagation path. S. Ni et al.: MVAN for Fake News Detection on Social Media the structured propagation graph at the same time, RNN and graph neural network (GNN) were used to process these two kinds of data. To make the model have better learning ability and certain interpretability, two different attention mechanisms, text semantic attention and propagation structure attention, were added to RNN and GNN. (3) Our model is more robust in early fake news detection and the model has some interpretability in both perspectives of text and propagation structure
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