Kavli Affiliate: Wei Gao
| First 5 Authors: Ruichao Yang, Jing Ma, Wei Gao, Hongzhan Lin,
| Summary:
The proliferation of misinformation, such as rumors on social media, has
drawn significant attention, prompting various expressions of stance among
users. Although rumor detection and stance detection are distinct tasks, they
can complement each other. Rumors can be identified by cross-referencing
stances in related posts, and stances are influenced by the nature of the
rumor. However, existing stance detection methods often require post-level
stance annotations, which are costly to obtain. We propose a novel LLM-enhanced
MIL approach to jointly predict post stance and claim class labels, supervised
solely by claim labels, using an undirected microblog propagation model. Our
weakly supervised approach relies only on bag-level labels of claim veracity,
aligning with multi-instance learning (MIL) principles. To achieve this, we
transform the multi-class problem into multiple MIL-based binary classification
problems. We then employ a discriminative attention layer to aggregate the
outputs from these classifiers into finer-grained classes. Experiments
conducted on three rumor datasets and two stance datasets demonstrate the
effectiveness of our approach, highlighting strong connections between rumor
veracity and expressed stances in responding posts. Our method shows promising
performance in joint rumor and stance detection compared to the
state-of-the-art methods.
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