Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 7
2032-2036

Rumor detection with knowledge representation and graph convolutional network

Guo Qiushi
Li Chenxi
Liu Jinshuo
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
DOI: 10.19734/j.issn.1001-3695.2022.01.0003

Abstract

Existing research methods have not effectively expressed the structural features of the heterogeneous graph of rumors and not introduced external knowledge as a means of content verification. Therefore, this paper proposed a rumor detection method with knowledge representation and graph convolutional network. It introduced the knowledge graph as additional prior knowledge to verify the authenticity of the content. After applying pre-training word embedding and knowledge graph embedding models to obtain text representation and fusing graph convolutional network simultaneously, it could extract features well in the topological graph of rumor propagation to improve the accuracy of rumor detection. Compared with the baseline methods, experimental results show that the proposed model reaches 96.1% at precision on Weibo, 3.1% improvement at F1 on Twitter15 and 3.3% improvement at F1 on Twitter16 respectively. The ablation experiment also shows this method has a significant improvement on rumor detection, and simultaneously verifies the effectiveness and progressiveness of this model.

Foundation Support

国家自然科学基金资助项目(U193607)
国家重点研发计划资助项目(2020FYA0607902)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.01.0003
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 7
Section: Algorithm Research & Explore
Pages: 2032-2036
Serial Number: 1001-3695(2022)07-017-2032-05

Publish History

[2022-03-07] Accepted Paper
[2022-07-05] Printed Article

Cite This Article

郭秋实, 李晨曦, 刘金硕. 引入知识表示的图卷积网络谣言检测方法 [J]. 计算机应用研究, 2022, 39 (7): 2032-2036. (Guo Qiushi, Li Chenxi, Liu Jinshuo. Rumor detection with knowledge representation and graph convolutional network [J]. Application Research of Computers, 2022, 39 (7): 2032-2036. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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