Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 5
1362-1367

Knowledge graph based multi-feature fusion rumor detection

Liu Xiaoyang
Li Hui
Zhang Kangqi
Duan Di
Wen Guiling
School of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
DOI: 10.19734/j.issn.1001-3695.2023.10.0425

Abstract

In order to solve the problem that it is difficult for the model to perceive implicit information due to the lack of external knowledge in rumor detection, which limits the ability of the model to mine deep information, this paper proposed knowledge graph based multi-feature fusion rumor detection(KGMRD) method. Firstly, for each event, it constructed posts and comments together into a text sequence and used a classifier to extract the emotional features. This paper constructed a knowledge graph based on text using ConceptNet and aggregated the entity representation in the knowledge graph with the semantic features of text using the attention mechanism, so as to obtain the enhanced semantic feature representation. Secondly, in terms of communication structure, for each event, this paper built its communication structure diagram based on the propagation and forwarding relationship of the post, and used DropEdge to prune the communication structure diagram, so as to obtain more effective communication structure characteristics. Finally, it fused the obtained features to get a new representation and compared seven models including SVM-RBF on three real datasets of Weibo, Twitter15 and Twitter16. The experimental results show that compared with the current baseline with the best effect, the KGMRD method has the best ACC on the Weibo dataset and improves the ACC by 1.1%, and there is a 2.2% improvement on Twitter15 and Twitter16 dataset in ACC. The experiment proves that the KGMRD method is reasonable and effective.

Foundation Support

重庆市社科联重点资助项目(2023NDZD09)
重庆理工大学研究生创新基金资助项目(gzlcx20232069)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.10.0425
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 5
Section: Algorithm Research & Explore
Pages: 1362-1367
Serial Number: 1001-3695(2024)05-012-1362-06

Publish History

[2023-12-05] Accepted Paper
[2024-05-05] Printed Article

Cite This Article

刘小洋, 李慧, 张康旗, 等. 基于知识图谱的多特征融合谣言检测方法 [J]. 计算机应用研究, 2024, 41 (5): 1362-1367. (Liu Xiaoyang, Li Hui, Zhang Kangqi, et al. Knowledge graph based multi-feature fusion rumor detection [J]. Application Research of Computers, 2024, 41 (5): 1362-1367. )

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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