Heterogeneous Graph Convolutional Network-Based Dynamic Rumor Detection on Social Media
The development of social media has provided open and convenient platforms for people to express their opinions, which leads to rumors being circulated. Therefore, detecting rumors from massive information becomes particularly essential. Previous methods for rumor detection focused on mining feature...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/8393736 |
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| _version_ | 1832562331930853376 |
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| author | Dingguo Yu Yijie Zhou Suiyu Zhang Chang Liu |
| author_facet | Dingguo Yu Yijie Zhou Suiyu Zhang Chang Liu |
| author_sort | Dingguo Yu |
| collection | DOAJ |
| description | The development of social media has provided open and convenient platforms for people to express their opinions, which leads to rumors being circulated. Therefore, detecting rumors from massive information becomes particularly essential. Previous methods for rumor detection focused on mining features from content and propagation patterns but neglected the dynamic features with joint content and propagation pattern. In this paper, we propose a novel heterogeneous GCN-based method for dynamic rumor detection (HDGCN), mainly composed of a joint content and propagation module and an ODE-based dynamic module. The joint content and propagation module constructs a content-propagation heterogeneous graph to obtain rumor representations by mining and discovering the interaction between post content and propagation structures in the rumor propagation process. The ODE-based dynamic module leverages a GCN integrated with an ordinary differential system to explore dynamic features of heterogeneous graphs. To evaluate the performance of our proposed HDGCN model, we have conducted extensive experiments on two real-world datasets from Twitter. The results of our proposed model have outperformed the mainstream model. |
| format | Article |
| id | doaj-art-a682022eb09845559096d5b01891ca15 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a682022eb09845559096d5b01891ca152025-02-03T01:22:57ZengWileyComplexity1099-05262022-01-01202210.1155/2022/8393736Heterogeneous Graph Convolutional Network-Based Dynamic Rumor Detection on Social MediaDingguo Yu0Yijie Zhou1Suiyu Zhang2Chang Liu3Key Lab of Film and TV Media Technology of Zhejiang ProvinceKey Lab of Film and TV Media Technology of Zhejiang ProvinceCollege of Media EngineeringKey Lab of Film and TV Media Technology of Zhejiang ProvinceThe development of social media has provided open and convenient platforms for people to express their opinions, which leads to rumors being circulated. Therefore, detecting rumors from massive information becomes particularly essential. Previous methods for rumor detection focused on mining features from content and propagation patterns but neglected the dynamic features with joint content and propagation pattern. In this paper, we propose a novel heterogeneous GCN-based method for dynamic rumor detection (HDGCN), mainly composed of a joint content and propagation module and an ODE-based dynamic module. The joint content and propagation module constructs a content-propagation heterogeneous graph to obtain rumor representations by mining and discovering the interaction between post content and propagation structures in the rumor propagation process. The ODE-based dynamic module leverages a GCN integrated with an ordinary differential system to explore dynamic features of heterogeneous graphs. To evaluate the performance of our proposed HDGCN model, we have conducted extensive experiments on two real-world datasets from Twitter. The results of our proposed model have outperformed the mainstream model.http://dx.doi.org/10.1155/2022/8393736 |
| spellingShingle | Dingguo Yu Yijie Zhou Suiyu Zhang Chang Liu Heterogeneous Graph Convolutional Network-Based Dynamic Rumor Detection on Social Media Complexity |
| title | Heterogeneous Graph Convolutional Network-Based Dynamic Rumor Detection on Social Media |
| title_full | Heterogeneous Graph Convolutional Network-Based Dynamic Rumor Detection on Social Media |
| title_fullStr | Heterogeneous Graph Convolutional Network-Based Dynamic Rumor Detection on Social Media |
| title_full_unstemmed | Heterogeneous Graph Convolutional Network-Based Dynamic Rumor Detection on Social Media |
| title_short | Heterogeneous Graph Convolutional Network-Based Dynamic Rumor Detection on Social Media |
| title_sort | heterogeneous graph convolutional network based dynamic rumor detection on social media |
| url | http://dx.doi.org/10.1155/2022/8393736 |
| work_keys_str_mv | AT dingguoyu heterogeneousgraphconvolutionalnetworkbaseddynamicrumordetectiononsocialmedia AT yijiezhou heterogeneousgraphconvolutionalnetworkbaseddynamicrumordetectiononsocialmedia AT suiyuzhang heterogeneousgraphconvolutionalnetworkbaseddynamicrumordetectiononsocialmedia AT changliu heterogeneousgraphconvolutionalnetworkbaseddynamicrumordetectiononsocialmedia |