Improving the Robustness of Rumor Detection Models with Metadata-Augmented Evasive Rumor Datasets

Rumors on social media can cause serious harm. Advances in NLP enable deceptive rumors resembling real posts, necessitating more robust detection. One approach collects and augments a dataset with adversarial rumors meant to evade models. Understanding evasive rumors and adding them to a dataset improves model robustness. We demonstrate effective data augmentation that significantly improves detection models. State-of-the-art accuracy drops by up to 29.5% against evasive rumors, while our augmentation raises it by up to 14.62%. Results highlight data augmentation’s importance for robust detection models countering evasion. Our evaluation shows the value of augmentation for developing models robust against adversarial attacks.