Detecting Weibo Rumors Based on Hierarchical Semantic Feature Learning Model
Huang Xuejian1,2,Ma Tinghuai1(),Wang Gensheng3
1College of Software, Nanjing University of Information Science & Technology, Nanjing 210044, China 2VR College of Modern Industry, Jiangxi University of Finance and Economics, Nanchang 330013, China 3College of Humanities, Jiangxi University of Finance and Economics, Nanchang 330013, China
[Objective] This paper tries to improve the accuracy and timeliness of Weibo rumor detection. [Methods] We proposed a rumor detection method based on the hierarchical semantic feature learning model (BCGA). Firstly, we extracted the semantic features of a single text in an event based on the BERT model. Secondly, we dynamically grouped the event propagation data based on the time domain. Next, we used the convolutional neural network to learn the semantic correlation features of the text sets in each time domain. Fourth, we input the semantic correlation features in each time domain into the deep bidirectional gated recurrent neural network to learn the deep semantic temporal features of the event propagation process. Finally, we integrated the attention mechanism to make the model focus on the rumor feature in semantic temporal features. [Results] Experiments on the Weibo public data sets show that the detection accuracy of the model reached 95.39%, while the detection delay was within 12 hours. [Limitations] The model requires a certain amount of forwarding and commenting information and the detection effect is not prominent when the event is not popular enough. [Conclusions] The hierarchical semantic feature learning model achieves a learning process from local to global semantics, improving the performance of Weibo rumor detection.
(Gao Yujun, Liang Gang, Jiang Fangting, et al. Social Network Rumor Detection: A Survey[J]. Acta Electronica Sinica, 2020, 48(7): 1421-1435.)
doi: 10.3969/j.issn.0372-2112.2020.07.023
[2]
Castillo C, Mendoza M, Poblete B. Information Credibility on Twitter[C]// Proceedings of the 20th International Conference on World Wide Web. 2011: 675-684.
[3]
Yang F, Liu Y, Yu X H, et al. Automatic Detection of Rumor on Sina Weibo[C]// Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics. 2012: 1-7.
(He Gang, Lv Xueqiang, Li Zhuo, et al. Automatic Rumor Identification on Microblog[J]. Library and Information Service, 2013, 57(23): 114-120.)
doi: 10.7536/j.issn.0252-3116.2013.23.019
[5]
Ma J, Gao W, Wei Z Y, 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.
(Zu Kunlin, Zhao Mingwei, Guo Kai, et al. Research on the Detection of Rumor on Sina Weibo[J]. Journal of Chinese Information Processing, 2017, 31(3): 198-204.)
(Ma Ming, Liu Yun, Liu Dijun, et al. Rumor Detection in Microblogs Based on Topic and Prevention Model[J]. Transactions of Beijing Institute of Technology, 2020, 40(3): 310-315.)
[8]
Wu K, Yang S, Zhu K Q. False Rumors Detection on Sina Weibo by Propagation Structures[C]// Proceedings of 2015 IEEE 31st International Conference on Data Engineering. 2015: 651-662.
[9]
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: 708-717.
(Zeng Ziming, Wang Jing. Research on Microblog Rumor Identification Based on LDA and Random Forest[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(1): 89-96.)
[11]
Yu F, Liu Q, Wu S, et al. A Convolutional Approach for Misinformation Identification[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017: 3901-3907.
[12]
Ajao O, Bhowmik D, Zargari S. Fake News Identification on Twitter with Hybrid CNN and RNN Models[C]// Proceedings of the 9th International Conference on Social Media and Society. 2018: 226-230.
(Li Ao, Dan Zhiping, Dong Fangmin, et al. An Improved Generative Adversarial Network for Rumor Detection[J]. Journal of Chinese Information Processing, 2020, 34(9): 78-88.)
[14]
Ma J, Gao W, Wong K F. Detect Rumors on Twitter by Promoting Information Campaigns with Generative Adversarial Learning[C]// Proceeding of the 2019 World Wide Web Conference. 2019: 3049-3055.
(Huang Xuejian, Wang Gensheng, Luo Yuansheng, et al. Weibo Rumors Real-time Detection Model Based on Fusion of Multi User Features and Content Features[J]. Journal of Chinese Computer Systems, 2022, 38(12): 2518-2527.)
[16]
Tu K F, Chen C, Hou C Y, et al. Rumor2vec: A Rumor Detection Framework with Joint Text and Propagation Structure Representation Learning[J]. Information Sciences, 2021, 560: 137-151.
doi: 10.1016/j.ins.2020.12.080
[17]
Ke Z W, Li Z, Zhou C Z, et al. Rumor Detection on Social Media via Fused Semantic Information and a Propagation Heterogeneous Graph[J]. Symmetry, 2020, 12(11): 1806.
doi: 10.3390/sym12111806
[18]
Ma T H, Zhou H H, Tian Y, et al. A Novel Rumor Detection Algorithm Based on Entity Recognition, Sentence Reconfiguration, and Ordinary Differential Equation Network[J]. Neurocomputing, 2021, 447: 224-234.
doi: 10.1016/j.neucom.2021.03.055
(Yin Pengbo, Pan Weimin, Peng Cheng, et al. Research on Early Detection of Weibo Rumors Based on User Characteristics Analysis[J]. Journal of Intelligence, 2020, 39(7): 81-86.)
(Xie Bailin, Jiang Shengyi, Zhou Yongmei, et al. Misinformation Detection Based on Gatekeepers’ Behaviors in Microblog[J]. Chinese Journal of Computers, 2016, 39(4): 730-744.)
(Liu Zhiyuan, Zhang Le, Tu Cunchao, et al. Statistical and Semantic Analysis of Rumors in Chinese Social Media[J]. Scientia Sinica(Informationis), 2015, 45(12): 1536-1546.)
[22]
Vosoughi S, Roy D, Aral S. The Spread of True and False News Online[J]. Science, 2018, 359(6380): 1146-1151.
doi: 10.1126/science.aap9559
pmid: 29590045
[23]
Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, Volume 1 (Long and Short Papers). 2019: 4171-4186.
[24]
Liu F G, Zheng J Z, Zheng L L, et al. Combining Attention Based Bidirectional Gated Recurrent Neural Network and Two Dimensional Convolutional Neural Network for Document-Level Sentiment Classification[J]. Neurocomputing, 2020, 371: 39-50.
doi: 10.1016/j.neucom.2019.09.012
[25]
Dey R, Salem F M. Gate-variants of Gated Recurrent Unit (GRU) Neural Networks[C]// Proceedings of 2017 IEEE 60th International Midwest Symposium on Circuits and Systems. 2017: 1597-1600.
[26]
Parikh A, Täckström O, Das D, et al. A Decomposable Attention Model for Natural Language Inference[C]// Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016: 2249-2255.
[27]
Ho Y, Wookey S. The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling[J]. IEEE Access, 2019, 8: 4806-4813.
doi: 10.1109/Access.6287639
[28]
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.
(Li Lizhao, Cai Guoyong, Pan Jiao. A Microblog Rumor Events Detection Method Based on C-GRU[J]. Journal of Shandong University(Engineering Science), 2019, 49(2): 102-106, 115.)