Rumor Knowledge Embedding Based Data Augmentation for Imbalanced Rumor Detection

Abstract Rumor detection aims to detect rumors in a timely manner to prevent malicious rumors from misleading the public and disrupting social order. However, rumor detection suffers from the problem of imbalanced data . Existing methods of text generation and imbalanced learning are insufficient in addressing this imbalance because they are not specialized in rumor tasks. We propose a knowledge graph-based rumor data augmentation method: Graph Embedding-based Rumor Data Augmentation (GERDA), which simulates the generation process of rumor from the perspective of knowledge. To model the generation process of false information, we introduce knowledge representation in the process of text generation. To better learn the graph structured rumor data, we propose a graph-based rumor text generative model G2S-AT-GAN, which uses an attention-based graph convolutional neural network and a generative adversarial network for rumor text generation. Experiments show that our method is able to generate high-quality rumors of diverse topics and the generated rumors can further address rumor data imbalance for better performance in rumor detection.