Social Media Rumor Detection Method Based on Multi-Transmit Influence

doi:10.19678/j.issn.1000-3428.0061592

Abstract

Abstract: Social media rumor detection is a hot topic in current research.Existing methods learn user characteristics by acquiring many user attributes.However, they ignore the influence of potential relationships between users on information propagation, making them inapt for early detection of rumors.This paper proposes a Multi-Transmit Influence(MTI) model for social media rumor detection.The forwarding relationship between the source microblog and its corresponding forwards(comments) is used to construct a text information propagation graph.A graph convolution neural network is used to capture and learn the propagation characteristics of text information.In addition, the influence of users in the communication process is integrated to enrich information detection for early rumor detection.First, the users with forwarding relationships are formed into a user influence propagation graph.A user-node influence learning method is then constructed to capture the user-node influence thatenhances the user characteristic information.Finally, text and user features are fused to detect rumors more accurately.Experiments on three real social media data sets revealthat the proposed method significantly improved automatic and early rumor detection.Compared with the conventional benchmark methods, the accuracy improved by 2.8%, 6.9%, and 3.4% when tested using Weibo, Twitter15, and Twitter16 data sets, respectively.

Key words: rumor detection, transmit influence, graph convolution neural network, information propagation, social media

摘要： 社交媒体谣言检测是当前研究的热点问题，现有方法多数通过获取大量用户属性学习用户特征，但不适用于谣言的早期检测，忽略了用户之间的潜在关系对信息传播的影响。提出一种基于多传递影响力的谣言检测方法，根据源微博及其对应转发（评论）之间的关系构建文本信息传播图，并通过图卷积神经网络来捕获、学习文本信息的传播特征。利用文本信息和用户传播过程中的影响力，丰富可用于谣言检测早期的检测信息。将存在转发关系的用户构成用户影响力传播图，构建一种用户节点影响力学习方法，获取用户节点影响力，以增强用户特征信息。在此基础上，将文本特征与用户特征融合以进行谣言检测，从而提升检测效果。在3个真实社交媒体数据集上的实验结果表明，该方法在谣言自动检测以及早期检测的效果都有显著提升，与目前最好的基准方法相比，在微博、Twitter15、Twitter16数据集上的正确率分别提高了2.8%、6.9%和3.4%。

关键词: 谣言检测, 传递影响力, 图卷积神经网络, 信息传播, 社交媒体

CLC Number:

TP309

DUAN Dagao, BAI Chenyu, HAN Zhongming, XIONG Haitao. Social Media Rumor Detection Method Based on Multi-Transmit Influence[J]. Computer Engineering, 2022, 48(10): 138-145,157.

段大高, 白宸宇, 韩忠明, 熊海涛. 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.

/ / Recommend / Download Citations

URL: http://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0061592

http://www.ecice06.com/EN/Y2022/V48/I10/138

Figures/Tables 13

References

[1] 高玉君, 梁刚, 蒋方婷, 等.社会网络谣言检测综述[J].电子学报, 2020, 48(7):1421-1435. GAO Y J, LIANG G, JIANG F T, et al.Social network rumor detection:a survey[J].Acta Electronica Sinica, 2020, 48(7):1421-1435.(in Chinese)
[2] ALLCOTT H, GENTZKOW M.Social media and fake news in the 2016 election[J].Journal of Economic Perspectives, 2017, 31(2):211-236.
[3] CASTILLO C, MENDOZA M, POBLETE B.Information credibility on twitter[C]//Proceedings of the 20th International Conference on World Wide Web.New York, USA:ACM Press, 2011:675-684.
[4] KWON S, CHA M, JUNG K, et al.Prominent features of rumor propagation in online social media[C]//Proceedings of the 13th IEEE International Conference on Data Mining.Washington D.C., USA:IEEE Press, 2013:1103-1108.
[5] YANG F, LIU Y, YU X H, et al.Automatic detection of rumor on sinaweibo[C]//Proceedings of ACM SIGKDD Workshop on Mining Data Semantics.New York, USA:ACM Press, 2012:1-7.
[6] JIN Z W, CAO J, ZHANG Y D, et al.Novel visual and statistical image features for microblogs news verification[J].IEEE Transactions on Multimedia, 2017, 19(3):598-608.
[7] 马鸣, 刘云, 刘地军, 等.基于主题和预防模型的微博谣言检测[J].北京理工大学学报, 2020, 40(3):310-315. MA M, LIU Y, LIU D J, et al.Rumor detection in microblogs based on topic and prevention model[J].Transactions of Beijing Institute of Technology, 2020, 40(3):310-315.(in Chinese)
[8] 段大高, 盖新新, 韩忠明, 等.基于梯度提升决策树的微博虚假消息检测[J].计算机应用, 2018, 38(2):410-414, 420. DUAN D G, GAI X X, HAN Z M, et al.Micro-blog misinformation detection based on gradient boost decision tree[J].Journal of Computer Applications, 2018, 38(2):410-414, 420.(in Chinese)
[9] 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.New York, USA:ACM Press, 2015:1751-1754.
[10] 何力, 郑灶贤, 项凤涛, 等.基于深度学习的文本分类技术研究进展[J].计算机工程, 2021, 47(2):1-11. HE L, ZHENG Z X, XIANG F T, et al.Research progress of text classification technology based on deep learning[J].Computer Engineering, 2021, 47(2):1-11.(in Chinese)
[11] MA J, GAO W, MITRA P, et al.Detecting rumors from microblogs with recurrent neural networks[J].Artificial Intelligence 2016, 16(8):3818-3824.
[12] 陶霄, 朱焱, 李春平.基于注意力和多模态混合融合的谣言检测方法[J].计算机工程, 2021, 47(12):71-77. TAO X, ZHU Y, LI C P.Rumor detection method based on attention and multi-modal hybrid fusion[J].Computer Engineering, 2021, 47(12):71-77.(in Chinese)
[13] CHEN T, LI X, YIN H Z, et al.Call attention to rumors:deep attention based recurrent neural networks for early rumor detection[C]//Proceedings of Workshop on Trends and Applications in Knowledge Discovery and Data Mining.Berlin, Germany:Springer, 2018:40-52.
[14] 潘德宇, 宋玉蓉, 宋波.一种新的考虑注意力机制的微博谣言检测模型[J].小型微型计算机系统, 2021, 42(2):348-353. PAN D Y, SONG Y R, SONG B.New microblog rumor detection model based on attention mechanism[J].Journal of Chinese Computer Systems, 2021, 42(2):348-353.(in Chinese)
[15] 李奥, 但志平, 董方敏, 等.基于改进生成对抗网络的谣言检测方法[J].中文信息学报, 2020, 34(9):78-88. LI A, DAN Z P, DONG F M, et al.An improved generative adversarial network for rumor detection[J].Journal of Chinese Information Processing, 2020, 34(9):78-88.(in Chinese)
[16] MA J, GAO W, WONG K F.Detect rumors on twitter by promoting information campaigns with generative adversarial learning[C]//Proceedings of World Wide Web Conference.Washington D.C.USA:IEEE Press, 2019:3049-3055.
[17] MA J, GAO W, WONG K F.Rumor detection on twitter with tree-structured recursive neural networks[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics.Washington D.C., USA:IEEE Press, 2018:2074-2085.
[18] LIU Y, WU Y F.Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks[C]//Proceedings of AAAI Conference on Artificial Intelligence.New York, USA:AAAI Press, 2018:354-361.
[19] MIKOLOV T, CHEN K, CORRADO G, et al.Efficient estimation of word representations in vector space[EB/OL].[2021-04-01].https://arxiv.org/abs/1301.3781.
[20] KIPF T N, WELLING M.Semi-supervised classification with graph convolutional networks[EB/OL].[2021-04-01].https://arxiv.org/abs/1609.02907.
[21] ZHAO Z, RESNICK P, MEI Q Z.Enquiring minds:early detection of rumors in social media from enquiry posts[C]//Proceedings of the 24th International Conference on World Wide Web.Washington D.C.USA:IEEE Press, 2015:1395-1405.
[22] 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.Washington D.C.USA:IEEE Press, 2017:161-171.
[23] KINGMA D, BA J.Adam:a method for stochastic optimization[EB/OL].[2021-04-01].https://arxiv.org/pdf/1412.6980.pdf.

Please choose a citation manager

Content to export