基于C-GRU的微博谣言事件检测方法
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摘要
提出基于卷积-门控循环单元(convolution-gated recurrent unit, C-GRU)的微博谣言事件检测模型。结合卷积神经网络(convolutional neural networks, CNN)和门控循环单元(gated recurrent unit, GRU)的优点,将微博事件博文句向量化,通过CNN中的卷积层学习微博窗口的特征表示,将微博窗口特征按时间顺序拼接成窗口特征序列,将窗口特征序列输入GRU中学习序列特征表示进行谣言事件检测。在真实数据集上的试验结果表明,相比基于传统机器学习方法、CNN和GRU的谣言检测模型,该模型有更好的谣言识别能力。
A microblog rumor events detection model based on convolution-gated recurrent unit(C-GRU) was proposed. Combining the advantages of CNN and GRU, the microblog event?s posts was vectorized. By learning the features representation of the microblog windows through the convolution layer of CNN, the features of microblog windows was spliced into a sequence of window feature according to the time order, and the sequence of window feature was put into the GRU to learn feature representation of sequence for rumor events detection. Experimental results from real data sets showed that this model had better ability to rumor detection than other models based on traditional machine learning, CNN or RNN.
A microblog rumor events detection model based on convolution-gated recurrent unit(C-GRU) was proposed. Combining the advantages of CNN and GRU, the microblog event?s posts was vectorized. By learning the features representation of the microblog windows through the convolution layer of CNN, the features of microblog windows was spliced into a sequence of window feature according to the time order, and the sequence of window feature was put into the GRU to learn feature representation of sequence for rumor events detection. Experimental results from real data sets showed that this model had better ability to rumor detection than other models based on traditional machine learning, CNN or RNN.
引文
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[3] HASSAN A,QAZVINIAN V,RADEV D.What's with the attitude?:identifying sentences with attitude in online discussions[C]//Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing.Massachusetts,USA:Association for Computational Linguistics.ACM,2010:1245-1255.
[4] MA Ben,LIN Dazhen,CAO Donglin.Content representation for microblog rumor detection[M] Advances in Computational Intelligence Systems.Lancaster,UK:Springer International Publishing,2017:245-251.
[5] CASTILLO C,MENDOZA M,POBLET B.Information credibility on Twitter[C]//Proceedings of the 20th international conference on World wide web.Hyderabad,India:ACM,2011:675-684.
[6] MORRIS M R,COUNTS S,ROSEWAY A,et al.Tweeting is believing?understanding microblog credibility perceptions[C]//Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work.Washington,USA:ACM,2012:441-450.
[7] LIANG Gang,HE Wenbo,XU Chun,et al.Rumor identification in microblogging systems based on users’ behavior[J].IEEE Transactions on Computational Social Systems,2015,2(3):99-108.
[8] MENDOZA M,POBLETE B,CASTILLO C.Twitter under crisis:can we trust what we RT?[C]//Proceedings of the First Workshop on Social Media Analytics.Washington,USA:ACM,2010:71-79.
[9] CAI Guoyong,BI Mengying,LIU Jianxing.A novel rumor detection method based on labeled cascade propagation tree[C]//Proceedings of the 2017 13th International Conference on Natural Computation,Fuzzy Systems and Knowledge Discovery.Changsha,China:ACM,2017.
[10] BAO Yuanyuan,YI Chengqi,XUE Yibo,et al.A new rumor propagation model and control strategy on social networks[C]//Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.Ontario,Canada:ACM,2013:1472-1473.
[11] KWON S,CHA M,JUNG K,et al.Prominent features of rumor propagation in online social media[C]//Data Mining (ICDM),2013 IEEE 13th International Conference.Dallas,TX,USA:IEEE,2013:1103-1108.
[12] MA Jing,GAO Wei,WEI Zhongyu,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,Australia:ACM,2015:1751-1754.
[13] 毛二松,陈刚,刘欣,等.基于深层特征和集成分类器的微博谣言检测研究[J].计算机应用研究,2016(11):3369-3373.MAO Ersong,CHEN Gang,LIU Xin,et al.Research on detecting micro-blog rumors based on deep features and ensemble classifier[J].Application Research of Computers.2016(11):3369-3373.
[14] MA Jing,GAO Wei,MITRA P,et al.Detecting rumors from microblogs with recurrent neural networks[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.New York,USA:AAAI Press,2016:3818-3824.
[15] CHEN Tong,WU Lin,LI Xue,et al.Call attention to rumors:deep attention based recurrent neural networks for early rumor detection[J].arXiv Preprint,2017.https://arxiv.org/pdf/1704.05973.pdf
[16] RUCHANSKY N,SEO S,Liu Y.CSI:a hybrid deep model for fake news[C].Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.Singapore:ACM,2017:797-806.
[17] 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 convolution neural network[J].Journal of Computer Applications,2017,37(11):3053-3056.
[18] ZHOU Chunting,SUN Chonglin,LIU Zhiyuan,et al.A C-LSTM neural network for text classification[J].Computer Science,2015.https://arxiv.org/pdf/1704.05973.pdf?tdsourcetag=s-pcqq-aiomsg.
[19] CHO K,MERRIENBOER B V,BAHDANAU D,et al.On the properties of neural machine translation:Encoder-decoder approaches[C]//Proceedings of SSST-8,Eighth Workshop on Syntax,Semantics and Structure in Statistical Translation.Doha,Qatar:ACL,2014,103-111.
[20] KINGMA D,BA J.ADAM:A Method for stochastic optimization[C]//Proceedings of the 3rd International Conference on Learning Representation.San Diego,USA:ICLR,2015
[21] YANG Fan,YU Xiaohui,LIU Yang,et al.Automatic detection of rumor on Sina Weibo[C]//Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics.Beijing,China:ACM,2012.