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journal article Jul 22, 2021

Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks for Fake News Detection

Shengsheng Qian ORCID Jun Hu ORCID Quan Fang ORCID Changsheng Xu ORCID
ACM Transactions on Multimedia Computing, Communications, and Applications Vol. 17 No. 3 pp. 1-23 · Association for Computing Machinery (ACM)
View at Publisher Save 10.1145/3451215
Abstract
In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.
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Showing 50 of 66 references

Cited By
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Deep Multimodal Data Fusion

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ACM Computing Surveys
Metrics
86
Citations
66
References
Details
Published
Jul 22, 2021
Vol/Issue
17(3)
Pages
1-23
License
View
Authors
S
Shengsheng Qian

National Lab of Pattern Recognition, Institute of Automation, CAS, China, and University of Chinese Academy of Sciences, China

J
Jun Hu

National Lab of Pattern Recognition, Institute of Automation, CAS, China, and University of Chinese Academy of Sciences, China

Q
Quan Fang

National Lab of Pattern Recognition, Institute of Automation, CAS, China, and University of Chinese Academy of Sciences, China

C
Changsheng Xu

National Lab of Pattern Recognition, Institute of Automation, CAS, China,School of Artificial Intelligence, University of Chinese Academy of Sciences, China, and Peng ChengLaboratory, China

Funding
National Natural Science Foundation of China Award: 62036012, 61721004, 61720106006, 61802405, 62072456, 61832002, 61936005 and U1705262
National Key Research and Development Program of China Award: 2017YFB1002804
Key Research Program of Frontier Sciences, CAS Award: QYZDJSSWJSC039
Cite This Article
Shengsheng Qian, Jun Hu, Quan Fang, et al. (2021). Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks for Fake News Detection. ACM Transactions on Multimedia Computing, Communications, and Applications, 17(3), 1-23. https://doi.org/10.1145/3451215
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