Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2025

Abstract

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 Multiple Instance Learning (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 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.

Keywords

Multiple Instance Learning, Rumor Detection, Stance Detection, Propaga-tion Structure, Hierarchical Attention Mechanism

Discipline

Data Storage Systems

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

ACM Transactions on Intelligent Systems and Technology

Volume

16

Issue

2

First Page

1

Last Page

27

ISSN

2157-6904

Identifier

10.1145/3716856

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://dl.acm.org/doi/pdf/10.1145/3716856

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