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Fake News Detection via Knowledge-driven Multimodal Graph Convolutional Networks

Published: 08 June 2020 Publication History

Abstract

Nowadays, with the rapid development of social media, there is a great deal of news produced every day. How to detect fake news automatically from a large of multimedia posts has become very important for people, the government and news recommendation sites. However, most of the existing approaches either extract features from the text of the post which is a single modality or simply concatenate the visual features and textual features of a post to get a multimodal feature and detect fake news. Most of them ignore the background knowledge hidden in the text content of the post which facilitates fake news detection. To address these issues, we propose a novel Knowledge-driven Multimodal Graph Convolutional Network (KMGCN) to model the semantic representations by jointly modeling the textual information, knowledge concepts and visual information into a unified framework for fake news detection. Instead of viewing text content as word sequences normally, we convert them into a graph, which can model non-consecutive phrases for better obtaining the composition of semantics. Besides, we not only convert visual information as nodes of graphs but also retrieve external knowledge from real-world knowledge graph as nodes of graphs to provide complementary semantics information to improve fake news detection. We utilize a well-designed graph convolutional network to extract the semantic representation of these graphs. Extensive experiments on two public real-world datasets illustrate the validation of our approach.

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    cover image ACM Conferences
    ICMR '20: Proceedings of the 2020 International Conference on Multimedia Retrieval
    June 2020
    605 pages
    ISBN:9781450370875
    DOI:10.1145/3372278
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    Published: 08 June 2020

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    Author Tags

    1. fake news detection
    2. graph neural networks
    3. social media

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    • (2025)Fake News Detection: Extendable to Global Heterogeneous Graph Attention Network with External KnowledgeTsinghua Science and Technology10.26599/TST.2023.901010430:3(1125-1138)Online publication date: Jun-2025
    • (2025)Lurker: Backdoor attack-based explainable rumor detection on online mediaScience Progress10.1177/00368504241307816108:1Online publication date: 6-Jan-2025
    • (2025)A unified multimodal classification framework based on deep metric learningNeural Networks10.1016/j.neunet.2024.106747181(106747)Online publication date: Jan-2025
    • (2025)Short Video Rumor Detection based on Causal GraphInformation Sciences10.1016/j.ins.2025.121941(121941)Online publication date: Feb-2025
    • (2025)ADA-UDAExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125487261:COnline publication date: 1-Feb-2025
    • (2024)Fake News Detection Based on Knowledge-Guided Semantic AnalysisElectronics10.3390/electronics1302025913:2(259)Online publication date: 5-Jan-2024
    • (2024)Multi-Modal Co-Attention Capsule Network for Fake News DetectionOptical Memory and Neural Networks10.3103/S1060992X2401004133:1(13-27)Online publication date: 25-Mar-2024
    • (2024)Research on Fake News Detection Method Using Heterogeneous Graph Fusion with Background KnowledgeComputer Science and Application10.12677/csa.2024.14306814:03(178-185)Online publication date: 2024
    • (2024)Multi-modal Misinformation Detection: Approaches, Challenges and OpportunitiesACM Computing Surveys10.1145/369734957:3(1-29)Online publication date: 22-Nov-2024
    • (2024)Research on Cross-domain Fake News detection based on Multi-space Fusion and Knowledge Graph EmbeddingProceedings of the 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy10.1145/3672919.3672941(113-117)Online publication date: 1-Mar-2024
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