Enhancing rumor detection with data augmentation and generative pre-trained transformer

Abstract

The advent of social networks has facilitated the rapid dissemination of false information, including rumors, leading to significant societal and individual damages. Extensive research has been dedicated to rumor detection, ranging from machine learning techniques to neural networks. However, the existing methods could not learn the deep concepts of the rumor text to detect the rumor. In addition, imbalanced datasets in the rumor domain reduce the effectiveness of these algorithms. This study addresses this challenge by leveraging the Generative Pre-trained Transformer 2 (GPT-2) model to generate rumor-like texts, thus creating a balanced dataset. Subsequently, a novel approach for classifying rumor texts is proposed by modifying the GPT-2 model. We compare our results with state-of-art machine learning and deep learning methods as well as pre-trained models on the PHEME, Twitter15, and Twitter16 datasets. Our findings demonstrate that the proposed model, implementing advanced artificial intelligence techniques, has improved accuracy and F-measure in the application of detecting rumors compared to previous methods.