Fake Social Media News Detection Based on Forwarding User Representation

Recently, the proliferation of fake news has imposed a dramatic impact on the public, so the fake news detection has been attracting increasing attention. Most of existing methods employ traditional classification models or neural networks that consider news content, comments, and context to detect fake news. However, the characteristics of news vary by type, topic, and context, which are difficult to uniformly represent for fake news detection. In this article, we propose a novel fake news detection model based on retweeting user embedding using only the stable and easily accessible information of retweeting users. Our model includes three components. First, we use all retweeting users to construct the retweeting graph and then learn the user representation. Second, for each tweet, we aggregate the representations of its retweeting users to learn the tweet representation. Finally, the tweet representations are fed into a feedforward neural network to train the news classifier. To demonstrate the effectiveness of our method, we conduct experiments on two public datasets. The results show that our classifier performs well and achieves better results than other state-of-the-art methods.