Mao, Q, Wang, Y, Yang, C et al. (5 more authors) (2023) HiGIL: Hierarchical Graph Inference Learning for Fact Checking. In: 2022 IEEE International Conference on Data Mining (ICDM). 22nd IEEE International Conference on Data Mining (ICDM), 28 Nov - 01 Dec 2022, Orlando, Florida, USA. IEEE , pp. 329-377. ISBN 978-1-6654-5099-7
Abstract
Fact-checking is vital for countering fake news. This process requires verifying the truthfulness of a claim by reasoning about multiple pieces of evidence. The current dominant approach depends upon capturing the claim-evidence relations from a claim-evidence interaction graph. Existing solutions utilize phrase-level semantics on a single-granularity but ignore other hierarchical features, such as fact- and sentence-level textual semantics and their logical topology. Since the hierarchical features often provide hints to infer collaborative high-order clues that can be essential for fact-checking, they should not be overlooked. This paper proposes a better method to model the claim-evidence graph in a multi-granularity manner. Doing so allows one to exploit more textual semantics and logical topology between a claim and its evidence. To achieve the target, we first employ a graph inference learning framework to infer graph nodes on different granular semantic units within their hierarchical topology. Then, an inference learning procedure is designed to optimize the global textual similarity and local topological reachability from the claim-evidence graph. We evaluate our approach by applying it to fact-checking on an open dataset, and experimental results show that our technique outperforms existing graph-based techniques by a large margin.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Fact-checking , Graph Inference Learning , Graph Reasoning , Graph Coarsening , Graph Pooling |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Oct 2022 12:34 |
Last Modified: | 02 Jun 2023 15:12 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICDM54844.2022.00043 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:192168 |