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Heterogeneous Graph Neural Network via Knowledge Relations for Fake News Detection

Published: 27 August 2023 Publication History

Abstract

The proliferation of fake news in social media has been recognized as a severe problem for society, and substantial attempts have been devoted to fake news detection to alleviate the detrimental impacts. Knowledge graphs (KGs) comprise rich factual relations among real entities, which could be utilized as ground-truth databases and enhance fake news detection. However, most of the existing methods only leveraged natural language processing and graph mining techniques to extract features of fake news for detection and rarely explored the ground knowledge in knowledge graphs. In this work, we propose a novel Heterogeneous Graph Neural Network via Knowledge Relations for Fake News Detection (HGNNR4FD). The devised framework has four major components: 1) A heterogeneous graph (HG) built upon news content, including three types of nodes, i.e., news, entities, and topics, and their relations. 2) A KG that provides the factual basis for detecting fake news by generating embeddings via relations in the KG. 3) A novel attention-based heterogeneous graph neural network that can aggregate information from HG and KG, and 4) a fake news detector, which is capable of identifying fake news based on the news embeddings generated by HGNNR4FD. We further validate the performance of our method by comparison with seven state-of-art baselines and verify the effectiveness of the components through a thorough ablation analysis. From the results, we empirically demonstrate that our framework achieves superior results and yields improvement over the baselines regarding evaluation metrics of accuracy, precision, recall, and F1-score on four real-world datasets.

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  • (2025)Rethinking Unsupervised Graph Anomaly Detection With Deep Learning: Residuals and ObjectivesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350130737:2(881-895)Online publication date: Feb-2025
  • (2024)Heterogeneous Subgraph Transformer for Fake News DetectionProceedings of the ACM Web Conference 202410.1145/3589334.3645680(1272-1282)Online publication date: 13-May-2024
  • (2024)Knowledge Graph Enhanced Heterogeneous Graph Neural Network for Fake News DetectionIEEE Transactions on Consumer Electronics10.1109/TCE.2023.332466170:1(2826-2837)Online publication date: Feb-2024
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cover image ACM Other conferences
SSDBM '23: Proceedings of the 35th International Conference on Scientific and Statistical Database Management
July 2023
232 pages
ISBN:9798400707469
DOI:10.1145/3603719
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Publication History

Published: 27 August 2023

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Author Tags

  1. Anomaly detection
  2. Fake news detection
  3. Graph mining
  4. Knowledge graph

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  • Research-article
  • Research
  • Refereed limited

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  • Australian Research Council

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SSDBM 2023

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Overall Acceptance Rate 56 of 146 submissions, 38%

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View all
  • (2025)Rethinking Unsupervised Graph Anomaly Detection With Deep Learning: Residuals and ObjectivesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350130737:2(881-895)Online publication date: Feb-2025
  • (2024)Heterogeneous Subgraph Transformer for Fake News DetectionProceedings of the ACM Web Conference 202410.1145/3589334.3645680(1272-1282)Online publication date: 13-May-2024
  • (2024)Knowledge Graph Enhanced Heterogeneous Graph Neural Network for Fake News DetectionIEEE Transactions on Consumer Electronics10.1109/TCE.2023.332466170:1(2826-2837)Online publication date: Feb-2024
  • (2024)Ensemble Graph Neural Networks for Fake News Detection Using User Engagement and Text FeaturesResults in Engineering10.1016/j.rineng.2024.103081(103081)Online publication date: Oct-2024
  • (2024)Social Media Oriented Fake News Detection Based on Social Context and Cascade GraphKnowledge and Systems Sciences10.1007/978-981-96-0178-3_15(213-224)Online publication date: 9-Nov-2024

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