Combined rumor and non-rumor dataset

This dataset, comprising 103,806 text entries, is a comprehensive resource for rumor detection on social media, constructed by merging benchmark collections including PHEME, LIAR Fake News, Twitter15, Twitter16, and ISOT Fake News. It features a binary classification schema (47% rumor, 53% non-rumor) and integrates original and adversarially augmented samples to enhance model robustness. Augmentation, applied selectively to the rumor class, employs the TextAttack framework with EmbeddingAugmenter (20% word swaps) and CharSwapAugmenter (character-level perturbations), preserving semantic integrity while introducing realistic textual variations. Preprocessing includes text normalization (e.g., lowercase conversion, URL/user placeholders)