Multimodal Fusion with BERT and Attention Mechanism for Fake News Detection

• URL: http://arxiv.org/abs/2104.11476v2
• Date: Tue, 27 Apr 2021 05:16:15 GMT
• Title: Multimodal Fusion with BERT and Attention Mechanism for Fake News Detection
• Authors: Nguyen Manh Duc Tuan, Pham Quang Nhat Minh
• Abstract summary: We present a novel method for detecting fake news by fusing multimodal features derived from textual and visual data. Experimental results showed that our approach performs better than the current state-of-the-art method on a public Twitter dataset by 3.1% accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fake news detection is an important task for increasing the credibility of information on the media since fake news is constantly spreading on social media every day and it is a very serious concern in our society. Fake news is usually created by manipulating images, texts, and videos. In this paper, we present a novel method for detecting fake news by fusing multimodal features derived from textual and visual data. Specifically, we used a pre-trained BERT model to learn text features and a VGG-19 model pre-trained on the ImageNet dataset to extract image features. We proposed a scale-dot product attention mechanism to capture the relationship between text features and visual features. Experimental results showed that our approach performs better than the current state-of-the-art method on a public Twitter dataset by 3.1% accuracy.

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