Unverified Rumor Detection

Chang Shu, Xiaochu Tang, Jing Xiao, Yuanxu Chen

Abstract: Currently, early rumor detection has garnered significant attention due to the rapid dissemination of information on social media. Many existing studies approach rumor detection as a traditional classification task, disregarding the existence of unverified rumors that require future verification. To address this limitation, we present T3-ERD, a novel framework that employs an improved BigBird transformer-based model for detecting rumors of specific types while concurrently leveraging reinforcement learning to determine early time checkpoints and categorize unverified rumors. And we introduce a novel bucket strategy, Online Dynamic, which dynamically clusters comments and processes them in batches, demonstrating remarkable effectiveness. The T3-ERD achieves F1 score, accuracy, and early rate of 0.830, 0.837, and 0.220 on the Twitter15 dataset, and 0.837, 0.844, and 0.205 on the Twitter16 dataset, respectively.

External IDs:dblp:journals/access/ShuTXC25