基于卷积神经网络的谣言检测

doi:10.11772/j.issn.1001-9081.2017.11.3053

计算机应用 ›› 2017, Vol. 37 ›› Issue (11): 3053-3056.DOI: 10.11772/j.issn.1001-9081.2017.11.3053

• 第十六届中国机器学习会议(CCML 2017) • 上一篇下一篇

基于卷积神经网络的谣言检测

刘政^1,2, 卫志华^1,2, 张韧弦^1,2

1. 同济大学计算机科学与技术系, 上海 201804;
2. 嵌入式系统与服务计算教育部重点实验室(同济大学), 上海 201804

收稿日期:2017-05-16 修回日期:2017-06-05 出版日期:2017-11-10 发布日期:2017-11-11
通讯作者: 卫志华
作者简介:刘政(1992-),男,山东济南人,硕士研究生,CCF会员,主要研究方向:自然语言处理;卫志华(1979-),女,山西晋中人,副教授,博士,CCF会员,主要研究方向:机器学习、文本挖掘、图像内容分析;张韧弦(1976-),男,浙江鄞县人,副教授,博士,主要研究方向:自然语言处理、社交网络、数据挖掘。
基金资助:
国家自然科学基金资助项目（61573259，61673301，61573255，61673299）；上海市中医药三年行动计划重点项目（ZY3-CCCX-3-6002）；上海自然科学基金资助项目（15ZR1443800）。

Rumor detection based on convolutional neural network

LIU Zheng^1,2, WEI Zhihua^1,2, ZHANG Renxian^1,2

1. Department of Computer Science and Technology, Tongji University, Shanghai 201804, China;
2. Key Laboratory of Embedded System and Service Computing of Ministry of Education(Tongji University), Shanghai 201804, China

Received:2017-05-16 Revised:2017-06-05 Online:2017-11-10 Published:2017-11-11
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61573259, 61673301, 61573255, 61673299), the Program of Further Accelerating the Development of Chinese Medicine Three Year Action of Shanghai (ZY3-CCCX-3-6002), the Natural Science Foundation of Shanghai (15ZR1443800).

摘要/Abstract

摘要： 人工检测谣言通常需要耗费大量的人力物力，并且会有很长的检测延迟。目前现存的谣言检测模型一般根据谣言的内容、用户属性、传播方式人工地构造特征，而人工构建特征存在考虑片面、浪费人力等现象。为了解决这个问题，提出了基于卷积神经网络（CNN）的谣言检测模型。将微博中的谣言事件向量化，通过卷积神经网络隐含层的学习训练来挖掘表示文本深层的特征，避免了特征构建的问题，并能发现那些不容易被人发现的特征，从而产生更好的效果。实验结果表明，所提方法能够准确识别谣言事件，在准确率、精确率与F1值指标上优于支持向量机（SVM）与循环神经网络（RNN）等对比算法。

关键词: 微博, 谣言检测, 谣言事件, 卷积神经网络

Abstract: Manual rumor detection often consumes a lot of manpower and material resources, and there will be a long detection delay. At present, the existing rumor detection models construct features manually according to the content, user attributes, and pattern of the rumor transmission, which can not avoid one-sided consideration, waste of human and other phenomena. To solve this problem, a rumor detection model based on Convolutional Neural Network (CNN) was presented. The rumor events in microblog were vectorized. The deep features of text were mined through the learning and training in hidden layer of CNN to avoid the problem of feature construction, and those features that were not easily found could be found to produce better results. The experimental results show that the proposed method can accurately identify rumor events, and it is better than Support Vector Machine (SVM), Recurrent Neural Network (RNN) and other contrast algorithms in accuracy rate, precision rate and F1 score.

Key words: microblog, rumor detection, rumor event, Convolution Neural Network (CNN)

中图分类号:

TP391.41

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 Zhihua, ZHANG Renxian. Rumor detection based on convolutional neural network[J]. Journal of Computer Applications, 2017, 37(11): 3053-3056.

参考文献

[1] 李洋,陈毅恒,刘挺.微博信息传播预测研究综述[J].软件学报,2016,27(2):247-263.(LI Y, CHEN Y H, LIU T. Survey on predicting information propagation in microblogs[J]. Journal of Software, 2016,27(2):247-263)
[2] CASTILLO C, MENDOZA M, POBLETE B. Information credibility on twitter[C]//Proceedings of the 20th International Conference on World Wide Web. New York:ACM, 2011:675-684.
[3] KWON S, CHA M, JUNG K, et al. Prominent features of rumor propagation in online social media[C]//Proceedings of the 2013 IEEE 13th International Conference on Data Mining. Piscataway, NJ:IEEE, 2013:1103-1108.
[4] 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. New York:ACM, 2015:1751-1754.
[5] 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:ACM, 2015:1395-1405.
[6] 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. San Francisco, CA:Morgan Kaufmann, 2016:3818-3824.
[7] 李彦冬, 郝宗波, 雷航. 卷积神经网络研究综述[J]. 计算机应用, 2016, 36(9):2508-2515.(LI Y D, HAO Z B, LEI H. Survey of convolutional neural network[J]. Journal of Computer Applications, 2016, 36(9):2508-2515.)
[8] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Cambridge, MA:MIT Press,2012:1097-1105.
[9] DENG J, DONG W, SOCHER R, et al. ImageNet:a large-scale hierarchical image database[C]//Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2009:248-255.
[10] DONAHUE J, HENDRICKS L A, GUADARRAMA S, et al. Long-term recurrent convolutional networks for visual recognition and description[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE,2015:2625-2634.
[11] VINYALS O, TOSHEV A, BENGIO S, et al. Show and tell:a neural image caption generator[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2015:3156-3164.
[12] YIH W, HE X, MEEK C. Semantic parsing for single-relation question answering[C]//Proceedings of the 2014 Annual Meeting of the Association for Computational Linguistics. Stroudsburg:ACL, 2014:643-648.
[13] SHEN Y, HE X, GAO J, et al. Learning semantic representations using convolutional neural networks for Web search[C]//Proceedings of the 23rd International Conference on World Wide Web. New York:ACM, 2014:373-374.
[14] KIM Y. Convolutional neural networks for sentence classification[EB/OL].[2016-11-20]. http://www.aclweb.org/anthology/D14-1181.pdf.
[15] KALCHBRENNER N, GREFENSTETTE E, BLUNSOM P. A convolutional neural network for modelling sentences[EB/OL].[2016-11-20].http://anthology.aclweb.org/P/P14/P14-1062.pdf.
[16] COLLOBERT R, WESTON J. A unified architecture for natural language processing:deep neural networks with multitask learning[C]//Proceedings of the 25th International Conference on Machine Learning. New York:ACM, 2008:160-167.
[17] COLLOBERT R, WESTON J, KARLEN M, et al. Natural language processing (almost) from scratch[J]. Journal of Machine Learning Research, 2011, 12(1):2493-2537.
[18] 刘知远, 张乐, 涂存超, 等. 中文社交媒体谣言统计语义分析[J]. 中国科学:信息科学, 2015, 45(12):1536-1546.(LIU Z Y, ZHANG L, TU C C, et al. Statistical and semantic analysis of rumors in Chinese social media[J]. Scientia China:Informationis Sciences, 2015, 45(12):1536-1546.)

基于卷积神经网络的谣言检测

Rumor detection based on convolutional neural network

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