A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning

Ruichao Yang, Jing Ma*, Hongzhan Lin, Wei Gao

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contribution

Abstract

The diffusion of rumors on social media generally follows a propagation tree structure, which provides valuable clues on how an original message is transmitted and responded by users over time. Recent studies reveal that rumor verification and stance detection are two relevant tasks that can jointly enhance each other despite their differences. For example, rumors can be debunked by cross-checking the stances conveyed by their relevant posts, and stances are also conditioned on the nature of the rumor. However, stance detection typically requires a large training set of labeled stances at post level, which are rare and costly to annotate. Enlightened by Multiple Instance Learning (MIL) scheme, we propose a novel weakly supervised joint learning framework for rumor verification and stance detection which only requires bag-level class labels concerning the rumor's veracity. Specifically, based on the propagation trees of source posts, we convert the two multi-class problems into multiple MIL-based binary classification problems where each binary model is focused on differentiating a target class (of rumor or stance) from the remaining classes. Then, we propose a hierarchical attention mechanism to aggregate the binary predictions, including (1) a bottom-up/top-down tree attention layer to aggregate binary stances into binary veracity; and (2) a discriminative attention layer to aggregate the binary class into finer-grained classes. Extensive experiments conducted on three Twitter-based datasets demonstrate promising performance of our model on both claim-level rumor detection and post-level stance classification compared with state-of-the-art methods.
Original languageEnglish
Title of host publicationSIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery (ACM)
Pages1761–1772
Number of pages12
ISBN (Electronic)9781450387323
ISBN (Print)9781450387323
DOIs
Publication statusPublished - Jul 2022
Event45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022 - Madrid, Spain
Duration: 11 Jul 202215 Jul 2022
https://sigir.org/sigir2022/

Publication series

NameSIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022
Country/TerritorySpain
CityMadrid
Period11/07/2215/07/22
Internet address

Scopus Subject Areas

  • Software
  • Information Systems
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • hierarchical attention mechanism
  • mil
  • propagation tree
  • rumor verification
  • stance detection

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