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

Abstract

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.

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“…As the next step, the pooled feature maps are flattened through a reshape function to make the feature vector pulls concatenated. Flattening = pooled.reshape (11) The above equation takes rows and appends them all to create a single column vector.…”

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“…Another study [9] used self multihead attention-based CNN (SMHACNN). e study implemented CNN and self multihead attention (SMHA) techniques and evaluated the truthfulness of news based on its content.…”

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