Hierarchical Fake News Detection Model Based on Multi-Task Learning and Adversarial Training

The harmfulness of online fake news has brought widespread attention to fake news detection by researchers. Most existing methods focus on improving the accuracy and early detection of fake news, while ignoring the frequent cross-topic issues faced by fake news in online environments. A hierarchical fake news detection method (HAMFD) based on multi-task learning and adversarial training is proposed. Through the multi-task learning task at the event level, subjective and objective information is introduced. A subjectivity classifier is used to capture sentiment shift within events, aiming to improve in-domain performance and generalization ability of fake news detection. On this basis, textual features and sentiment shift features are fused to perform event-level fake news detection and enhance detection accuracy. The post-level loss and event-level loss are weighted and summed for backpropagation. Adversarial perturbations are added to the embedding layer of the post-level module to deceive the detector, enabling the model to better resist adversarial attacks and enhance its robustness and topic adaptability. Experiments are conducted on three real-world social media datasets, and the results show that the proposed method improves performance in both in-domain and cross-topic fake news detection. Specifically, the model attains accuracies of 91.3% on Twitter15, 90.4% on Twitter16, and 95.7% on Weibo, surpassing advanced baseline methods by 1.6%, 1.5%, and 1.1%, respectively.