Detecting fake news on Chinese social media based on hybrid feature fusion method

With the rapid growth of the scale of social media information, it is getting more and more difficult for social media to detect fake news by using manual review. The spread of fake news may misguide the public, cause social panic, and even lead to violence, which could be avoided by using early detection technology to timely identify fake news on social media. Since fake news is often deliberately designed to attract attention, it is difficult for mongers to provide pictures that match the fabricated stories. However, most of existing multi-modal solutions only use the information of images and text, but do not take into account the correlation between them, which limits the effect of model detection effect. In this paper, we proposed a novel Fake News Detection Framework (FNDF) in Sina Weibo based on hybrid feature fusion method. Specifically, a total of 16 features from text, images and users are extracted to distinguish fake news. Moreover, we extract image-text correlation between text and images. Then, a new deep neural network model called Fake News Net (FNN) is built to implement the detection of fake news, which makes use of a pre-training model named Enhanced Representation through Knowledge Integration (ERNIE), a convolution network named Visual Geometry Group (VGG-19), and a Back Propagation (BP) neural network. We validated it on a publicly available dataset, which shows that the F1-score of the FNN model reaches 95.90%, outperforming the state-of-the-art methods by 3.08%. The ablation experiment also proves that the correlation between images and texts increased the F1-score of the model by 3.15%. And the data balancing experiments show that our model still keeps outstanding detection performance when there is less fake news compared to real news, which is closer to the real-world scenario. The research in this paper provides theoretical methods and research ideas for the detection of fake news on social networks.