  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.
