|
|
New inductive microblog rumor detection method based on graph convolutional network |
You-wei WANG1( ),Shuang TONG1,Li-zhou FENG2,Jian-ming ZHU1,Yang LI1,Fu CHEN1 |
1. School of Information, Central University of Finance and Economics, Beijing 100081, China 2. School of Statistics, Tianjin University of Finance and Economics, Tianjin 300222, China |
|
|
Abstract A new inductive microblog rumor detection method based on graph convolutional networks (GCN) was proposed to solve the problems faced by traditional GCN in rumor detection, such as the insufficient consideration of word semantic information and the difficulty of selecting pooling methods. Firstly, the semantic relationship between words was considered. A microblog event graph construction method based on word semantic correlation was proposed by combining the traditional word co-occurrence based graph construction method, and the node information aggregation was realized by combining GCN and gate recurrent unit (GRU). Then, in order to effectively fuse the feature information of different nodes, a multiple pooling methods fusion strategy based on attention mechanism, which fused max-pooling, average-pooling and global-pooling, was proposed to obtain the final graph level vector. Finally, in order to improve the efficiency of microblog rumor detection, the influence of microblog comment time on detection results was explored, and the best comment utilization time threshold for model training was obtained. Experimental results show that the performance of the proposed method is generally better than that of Text-CNN, Bi-GCN, TextING and other typical methods on the given datasets, verifying its effectiveness in the field of microblog rumor detection.
|
Received: 14 November 2021
Published: 31 May 2022
|
|
Fund: 国家自然科学基金资助项目(61906220); 教育部人文社科资助项目(19YJCZH178); 国家社科基金资助项目(18CTJ008); 天津市自然科学基金资助项目(18JCQNJC69600); 内蒙古纪检监察大数据实验室2020-2021年度开放课题资助项目(IMDBD202002, IMDBD202004); 中央财经大学新兴交叉学科建设项目;中国高校产学研创新基金项目(2021FNA01002) |
基于图卷积网络的归纳式微博谣言检测新方法
为了解决传统图卷积神经网络在进行谣言检测时面临的未充分考虑单词语义信息以及池化方法选择困难的问题,提出基于图卷积网络(GCN)的归纳式微博谣言检测新方法. 考虑单词之间的语义关系,结合传统词共现建图方法提出基于词语义相关性的微博事件建图方法,并结合图卷积网络和门循环单元(GRU)实现节点信息聚合;为了有效融合不同节点状态的特征信息,提出基于注意力机制的多池化方法融合策略融合最大池、平均池和全局池以获取最终的图级向量;为了提高微博谣言检测效率,探究微博评论时间对检测结果的影响,获得用于模型训练的最佳评论利用时间阈值. 实验结果表明,本研究方法在给定数据集上的表现普遍优于Text-CNN、Bi-GCN、TextING等典型方法,验证了其在微博谣言检测领域的有效性.
关键词:
谣言检测,
图卷积网络,
微博事件,
门循环单元,
注意力机制
|
|
[1] |
ZUBIAGA A, AKER A, BONTCHEVA K, et al Detection and resolution of rumours in social media: a survey[J]. ACM Computing Surveys (CSUR), 2018, 51 (2): 1- 36
|
|
|
[2] |
新浪微博虚假消息辟谣官方账号. 2020年度微博辟谣数据报告[EB/OL]. (2020-02-07) [2021-11-05]. https://weibo.com/1866405545/K0QaImwsK.
|
|
|
[3] |
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 on Conference on Information and Knowledge Management. Melbourne: CIKM, 2015.
|
|
|
[4] |
ZHAO Z, RESNICK P, MEI Q. Enquiring minds: early detection of rumors in social media from enquiry posts [C]// Proceedings of the 24th International Conference on World Wide Web. New York: WWW, 2015.
|
|
|
[5] |
张仰森, 彭媛媛, 段宇翔, 等 基于评论异常度的新浪微博谣言识别方法[J]. 自动化学报, 2020, 46 (8): 1689- 1702 ZHANG Yang-sen, PENG Yuan-yuan, DUAN Yu-xiang, et al The method of Sina Weibo rumor detecting based on comment abnormality[J]. Acta Automatica Sinica, 2020, 46 (8): 1689- 1702
|
|
|
[6] |
曾子明, 王婧 基于LDA和随机森林的微博谣言识别研究: 以2016年雾霾谣言为例[J]. 情报学报, 2019, 38 (1): 89- 96 ZENG Zi-ming, 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
doi: 10.3772/j.issn.1000-0135.2019.01.010
|
|
|
[7] |
CAI G, BI M, LIU J. A novel rumor detection method based on labeled cascade propagation tree [C]// Proceedings of the 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. Guilin: ICNC-FSKD, 2017.
|
|
|
[8] |
MA J, GAO W, MITRA P, et al. Detecting rumors from microblogs with recurrent neural networks [C]// International Joint Conference on Artificial Intelligence. New York: IJCAI, 2016.
|
|
|
[9] |
WANG Z, GUO Y, WANG J, et al Rumor events detection from chinese microblogs via sentiments enhancement[J]. IEEE Access, 2019, 7: 103000- 103018
doi: 10.1109/ACCESS.2019.2928044
|
|
|
[10] |
尹鹏博, 潘伟民, 彭成, 等 基于用户特征分析的微博谣言早期检测研究[J]. 情报杂志, 2020, 39 (7): 81- 86 YIN Peng-bo, PAN Wei-min, 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
doi: 10.3969/j.issn.1002-1965.2020.07.014
|
|
|
[11] |
SONG C, YANG C, CHEN H, et al CED: credible early detection of social media rumors[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 33 (8): 3035- 3047
|
|
|
[12] |
刘政, 卫志华, 张韧弦 基于卷积神经网络的谣言检测[J]. 计算机应用, 2017, 37 (11): 3053- 3056 LIU Zheng, WEI Zhi-hua, ZHANG Ren-xian Rumor detection based on convolutional neural network[J]. Journal of Computer Applications, 2017, 37 (11): 3053- 3056
|
|
|
[13] |
胡斗, 卫玲蔚, 周薇, 等 一种基于多关系传播树的谣言检测方法[J]. 计算机研究与发展, 2021, 58 (7): 1395- 1411 HU Dou, WEI Ling-wei, ZHOU Wei, et al A rumor detection approach based on multi-relational propagation tree[J]. Journal of Computer Research and Development, 2021, 58 (7): 1395- 1411
doi: 10.7544/issn1000-1239.2021.20200810
|
|
|
[14] |
WU Z, PI D, CHEN J, et al Rumor detection based on propagation graph neural network with attention mechanism[J]. Expert Systems with Applications, 2020, 158: 113595
doi: 10.1016/j.eswa.2020.113595
|
|
|
[15] |
杨延杰, 王莉, 王宇航 融合源信息和门控图神经网络的谣言检测研究[J]. 计算机研究与发展, 2021, 58 (7): 1412- 1424 YANG Yan-jie, WANG Li, WANG Yu-hang Rumor detection based on source information and gating graph neural network[J]. Journal of Computer Research and Development, 2021, 58 (7): 1412- 1424
doi: 10.7544/issn1000-1239.2021.20200801
|
|
|
[16] |
YANG X, LYU Y, TIAN T, et al. Rumor detection on social media with graph structured adversarial learning [C]// Proceedings of the 29th International Conference on International Joint Conferences on Artificial Intelligence. Montreal: IJCAI, 2021.
|
|
|
[17] |
HU L, YANG T, SHI C, et al. Heterogeneous graph attention networks for semi-supervised short text classification [C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Hong Kong: EMNLP-IJCNLP, 2019.
|
|
|
[18] |
YAO L, MAO C, LUO Y. Graph convolutional networks for text classification [C]// Proceedings of the AAAI Conference on Artificial Intelligence. New Orleans: AAAI, 2019.
|
|
|
[19] |
LIU X, YOU X, ZHANG X, et al. Tensor graph convolutional networks for text classification [C]// Proceedings of the AAAI Conference on Artificial Intelligence. New York: AAAI, 2020.
|
|
|
[20] |
ZHANG Y, YU X, CUI Z, et al. Every document owns its structure: inductive text classification via graph neural networks [C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.[s.l.]:ACL, 2020.
|
|
|
[21] |
LI Y, TARLOW D, BROCKSCHMIDT M, et al. Gated graph sequence neural networks [C]// Proceedings of the 4th International Conference on Learning Representations. Puerto Rico: ICLR, 2016.
|
|
|
[22] |
米源, 唐恒亮 基于图卷积网络的谣言鉴别研究[J]. 计算机工程与应用, 2021, 57 (13): 161- 167 MI Yuan, TANG Heng-liang Rumor identification research based on graph convolutional network[J]. Computer Engineering and Applications, 2021, 57 (13): 161- 167
doi: 10.3778/j.issn.1002-8331.2003-0357
|
|
|
[23] |
KIM Y. Convolutional neural networks for sentence classification [C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Doha: EMNLP, 2014.
|
|
|
[24] |
SHU K, CUI L, WANG S, et al. dEFEND: explainable fake news detection [C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Anchorage: KDD, 2019.
|
|
|
[25] |
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. New York: AAAI, 2020.
|
|
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|