Jointly multi-source information and local-global relations of heterogeneous network for rumor detection
The widespread rumors on social media seriously disturb the social order, and we urgently need practical methods to detect rumors. Most existing deep learning methods focus on mining news text content, user information, and propagation features but ignore the rumor diffusion structural features. Rum...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2022.1056207/full |
| _version_ | 1797956379504279552 |
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| author | Xiaohong Han Mengfan Zhao Mengfan Zhao Yutao Zhang Yutao Zhang Tingzhao Zhao |
| author_facet | Xiaohong Han Mengfan Zhao Mengfan Zhao Yutao Zhang Yutao Zhang Tingzhao Zhao |
| author_sort | Xiaohong Han |
| collection | DOAJ |
| description | The widespread rumors on social media seriously disturb the social order, and we urgently need practical methods to detect rumors. Most existing deep learning methods focus on mining news text content, user information, and propagation features but ignore the rumor diffusion structural features. Rumors spread in a vertical chain and diffusion in a horizontal network. Both are essential features of rumors. In addition, existing models need more effective methods to extract higher-order features of multiple resource information. To address these problems, we propose a multi-source information heterogeneous graph model in this paper, called jointly Multi-Source information and Local-Global relationship of heterogeneous network model named MSLG. It extracts multi-source information such as rumors content, user information, propagation, and diffusion structure. Firstly, we extract the higher order semantic representation of rumors content by graph convolution network and integrate local relational attention to strengthen the critical semantic. At the same time, we construct the rumors and users as heterogeneous graphs to capture the propagation and diffusion structure of the rumors. We are finally fusing global relational attention to measure submodules’ importance. Experiments on two real-world datasets show that the proposed method achieves state-of-the-art results in fake news detection. |
| first_indexed | 2024-04-10T23:48:05Z |
| format | Article |
| id | doaj.art-6fea82953b5a472db6911feb4e537e81 |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-04-10T23:48:05Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-6fea82953b5a472db6911feb4e537e812023-01-10T22:29:06ZengFrontiers Media S.A.Frontiers in Physics2296-424X2023-01-011010.3389/fphy.2022.10562071056207Jointly multi-source information and local-global relations of heterogeneous network for rumor detectionXiaohong Han0Mengfan Zhao1Mengfan Zhao2Yutao Zhang3Yutao Zhang4Tingzhao Zhao5School of Information and Electrical Engineering, Hebei University of Engineering, Handan, ChinaSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan, ChinaCollege of Intelligence and Computing, Tianjin University, Tianjin, ChinaSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan, ChinaCollege of Intelligence and Computing, Tianjin University, Tianjin, ChinaScience College, Shijiazhuang University, Shijiazhuang, ChinaThe widespread rumors on social media seriously disturb the social order, and we urgently need practical methods to detect rumors. Most existing deep learning methods focus on mining news text content, user information, and propagation features but ignore the rumor diffusion structural features. Rumors spread in a vertical chain and diffusion in a horizontal network. Both are essential features of rumors. In addition, existing models need more effective methods to extract higher-order features of multiple resource information. To address these problems, we propose a multi-source information heterogeneous graph model in this paper, called jointly Multi-Source information and Local-Global relationship of heterogeneous network model named MSLG. It extracts multi-source information such as rumors content, user information, propagation, and diffusion structure. Firstly, we extract the higher order semantic representation of rumors content by graph convolution network and integrate local relational attention to strengthen the critical semantic. At the same time, we construct the rumors and users as heterogeneous graphs to capture the propagation and diffusion structure of the rumors. We are finally fusing global relational attention to measure submodules’ importance. Experiments on two real-world datasets show that the proposed method achieves state-of-the-art results in fake news detection.https://www.frontiersin.org/articles/10.3389/fphy.2022.1056207/fullmulti-source informationpropagation diffusion structureattention mechanismheterogeneous graphrumor detection |
| spellingShingle | Xiaohong Han Mengfan Zhao Mengfan Zhao Yutao Zhang Yutao Zhang Tingzhao Zhao Jointly multi-source information and local-global relations of heterogeneous network for rumor detection Frontiers in Physics multi-source information propagation diffusion structure attention mechanism heterogeneous graph rumor detection |
| title | Jointly multi-source information and local-global relations of heterogeneous network for rumor detection |
| title_full | Jointly multi-source information and local-global relations of heterogeneous network for rumor detection |
| title_fullStr | Jointly multi-source information and local-global relations of heterogeneous network for rumor detection |
| title_full_unstemmed | Jointly multi-source information and local-global relations of heterogeneous network for rumor detection |
| title_short | Jointly multi-source information and local-global relations of heterogeneous network for rumor detection |
| title_sort | jointly multi source information and local global relations of heterogeneous network for rumor detection |
| topic | multi-source information propagation diffusion structure attention mechanism heterogeneous graph rumor detection |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2022.1056207/full |
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