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| Identifying Fake News with External Knowledge and User Interaction Features |
Liu Shuai1,Fu Lifang2( ) |
1College of Engineering, Northeast Agricultural University, Harbin 150038, China
2College of Letters and Science, Northeast Agricultural University, Harbin 150038, China |
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Abstract [Objective] This paper proposes a multidimensional-data classification model to improve the efficiency of fake news detection. The new model incorporates external knowledge features and user interaction features to reduce fake news spreading in social media. [Methods] First, we extracted the background knowledge of fake news. Then, we introduced external knowledge through the Wikipedia knowledge graph to detect the consistency between the news content and the existing knowledge system. Third, we analyzed the user interaction on the communication chain according to the psychological “similarity effect”. Finally, we improved the connection edge weight of the graph convolutional network to reflect the interaction between users. [Results] We examined the new model’s performance with two public datasets, Twitter15 and Twitter16. Compared with the other five similar models, our model’s accuracy reached 0.901 and 0.927. [Limitations] We did not consider features like knowledge information and language expression hidden in the additional news content. The model’s interpretability needs to be further improved. [Conclusions] By integrating news content, external knowledge, and user interaction characteristics of the communication chain, the proposed model can effectively detect fake news.
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Received: 01 November 2022
Published: 28 April 2023
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Corresponding Authors:
Fu Lifang，ORCID：0000-0003-2298-2378，E-mail：lifangfu@neau.edu.cn。
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| [1] |
Popat K. Assessing the Credibility of Claims on the Web[C]// Proceedings of the 26th International Conference on World Wide Web Companion. 2017: 735-739.
|
| [2] |
张国标, 李洁. 融合多模态内容语义一致性的社交媒体虚假新闻检测[J]. 数据分析与知识发现, 2021, 5(5):21-29.
|
| [2] |
(Zhang Guobiao, Li Jie. Detecting Social Media Fake News with Semantic Consistency Between Multi-model Contents[J]. Data Analysis and Knowledge Discovery, 2021, 5(5):21-29.)
|
| [3] |
段大高, 白宸宇, 韩忠明, 等. 基于多传递影响力的社交媒体谣言检测方法[J]. 计算机工程, 2022, 48(10):138-145,157.
doi: 10.19678/j.issn.1000-3428.0061592
|
| [3] |
(Duan Dagao, Bai Chenyu, Han Zhongming, et al. Social Media Rumor Detection Method Based on Multi-Transmit Influence[J]. Computer Engineering, 2022, 48(10):138-145,157.)
doi: 10.19678/j.issn.1000-3428.0061592
|
| [4] |
Shu K, Wang S, Liu H. Understanding User Profiles on Social Media for Fake News Detection[C]// Proceedings of the 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2018: 430-435.
|
| [5] |
刘鹏飞. 基于多模态特征及语义增强的虚假新闻检测算法的研究与应用[D]. 青岛: 山东科技大学, 2020.
|
| [5] |
(Liu Pengfei. Research and Application of Fake News Detection Algorithm Based on Multi-modal Feature and Semantic Enhancement[D]. Qingdao: Shandong University of Science and Technology, 2020.)
|
| [6] |
Lu Y J, Li C T. GCAN: Graph-aware Co-attention Networks for Explainable Fake News Detection on Social Media[OL]. arXiv Preprint, arXiv: 2004.11648.
|
| [7] |
Przybyla P. Capturing the Style of Fake News[C]// Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(1): 490-497.
|
| [8] |
Karimi H, Tang J. Learning Hierarchical Discourse-level Structure for Fake News Detection[OL]. arXiv Preprint, arXiv: 1903.07389.
|
| [9] |
刘华玲, 陈尚辉, 乔梁, 等. 多模态混合注意力机制的虚假新闻检测研究[J]. 计算机工程与应用, 2023, 59(9): 95-103.
doi: 10.3778/j.issn.1002-8331.2202-0204
|
| [9] |
(Liu Hualing, Chen Shanghui, Qiao Liang, et al. Multimodal False News Detection Based on Fusion Attention Mechanism[J]. Computer Engineering and Applications, 2023, 59(9): 95-103..)
doi: 10.3778/j.issn.1002-8331.2202-0204
|
| [10] |
亓鹏, 曹娟, 盛强. 语义增强的多模态虚假新闻检测[J]. 计算机研究与发展, 2021, 58(7):1456-1465.
|
| [10] |
(Qi Peng, Cao Juan, Sheng Qiang. Semantics-Enhanced Multi-modal Fake News Detection[J]. Journal of Computer Research and Development, 2021, 58(7):1456-1465.)
|
| [11] |
刘赏, 沈逸凡. 基于新闻标题-正文差异性的虚假新闻检测方法[J]. 数据分析与知识发现, 2023, 7(2):97-107.
|
| [11] |
(Liu Shang, Shen Yifan. Detecting Fake News Based on Title-Content Difference[J]. Data Analysis and Knowledge Discovery, 2023, 7(2):97-107.)
|
| [12] |
Bian T, Xiao X, Xu T, et al. Rumor Detection on Social Media with Bi-directional Graph Convolutional Networks[C]// Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(1): 549-556.
|
| [13] |
Chen Z, Freire J. Proactive Discovery of Fake News Domains from Real-time Social Media Feeds[C]// Companion Proceedings of the Web Conference 2020. 2020: 584-592.
|
| [14] |
Liang G, He W, Xu C, et al. Rumor Identification in Microblogging Systems Based on Users’ Behavior[J]. IEEE Transactions on Computational Social Systems, 2015, 2(3): 99-108.
doi: 10.1109/TCSS.2016.2517458
|
| [15] |
Shen W, Wang J, Han J. Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions[J]. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(2): 443-460.
doi: 10.1109/TKDE.2014.2327028
|
| [16] |
朱彤. 人际交往中的心理学[M]. 北京: 金城出版社出版, 2009.
|
| [17] |
Ma J, Gao W, Wong K F. Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1:Long Papers). 2017.
|
| [18] |
Lai Y, Zhang L, Han D, et al. Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution Networks[J]. World Wide Web: Internet and Web Information Systems, 2019, 23(5):2771-2787.
|
| [19] |
Kipf T N, Welling M. Semi-supervised Classification with Graph Convolutional Networks[C]// Proceedings of the International Conference on Learning Representations (ICLR 2017). 2016.
|
| [20] |
Castillo C, Mendoza M, Poblete B. Information Credibility on Twitter[C]// Proceedings of the 20th International Conference on World Wide Web. 2011: 675-684.
|
| [21] |
Ma J, Gao W, Wei Z, et al. Detect Rumors Using Time Series of Social Context Information on Microblogging Websites[C]// Proceedings of the 24th ACM International Conference on Information and Knowledge Management. 2015: 1751-1754.
|
| [22] |
Ma J, Gao W, Mitra P, et al. Detecting Rumors from Microblogs with Recurrent Neural Networks[C]// Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2016: 3818-3824.
|
| [23] |
Ruchansky N, Seo S, Liu Y. CSI: A Hybrid Deep Model for Fake News Detection[C]// Proceedings of the 26th ACM International Conference on Information and Knowledge Management. 2017: 797-806.
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