Topic to Image: A Rumor Detection Method Inspired by Image Forgery Recognition Technology

This article is inspired by image forgery recognition techniques. If we regard topic comments as image pixels, the whole topic is a complete image. The image differences between rumor topics and nonrumor topics are reflected in image pixels just like forged images, and then, the problem of detecting rumor topics can be regarded as the problem of recognition images of rumor topics. First, the Topic2Image algorithm is proposed to use the semantic information to quantify the adversarial intensity among comments. It is mapped to the topological relationship among user comments. Also, the relative positions of the comment nodes are determined by the adversarial intensity. Second, considering the competitive relationship between positive and negative comments, a sentimental mutual influence model is proposed. Based on the evolutionary game theory, a transfer matrix of sentimental mutual influence is constructed. Internal and external factors of rumor detection are considered at the individual and group levels, respectively. Finally, considering the advantages of convolutional neural network (CNN) for image processing, a simple rumor detection algorithm topic image rumor detection (TIRD) based on topic image classification is proposed. Using CNNs and gray-level co-occurrence matrix to extract global and local features of topic images and combining them with the transfer matrix of sentimental mutual influence, the detection of topic rumor is realized. Experiments demonstrate the feasibility of transforming topic rumors into image. In addition, the effectiveness of image forgery recognition technology for detecting rumors is verified.