Detection of rumor conversations in Twitter using graph convolutional networks

With the increasing popularity of the social network Twitter and its use to propagate information, it is of vital importance to detect rumors prior to their dissemination on Twitter. In the present paper, a model to detect rumor conversations is proposed using graph convolutional networks. A reply tree and user graph were extracted for each conversation. The reply trees were created according to the source tweet and the reply tweets. By modeling this graph on graph convolutional networks, structural information of the graph and the contents of conversation tweets were obtained. The user graphs were created based on the users participating in the conversation and the tweets exchanged among them. Information regarding the users and how they interacted in the conversations were obtained through modeling this graph on the graph convolutional networks. The outputs of the two above-mentioned modules were combined to detect the rumor. Experimental results on the public dataset show that the proposed method has a better performance than baseline methods.

[1]  Muhammad Zubair Asghar,et al.  Exploring deep neural networks for rumor detection , 2019, Journal of Ambient Intelligence and Humanized Computing.

[2]  Mohammed Al-Sarem,et al.  Detecting Rumors on Social Media Based on a CNN Deep Learning Technique , 2020, Arabian Journal for Science and Engineering.

[3]  Luís Torgo,et al.  A Survey of Predictive Modelling under Imbalanced Distributions , 2015, ArXiv.

[4]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[5]  Xiaolong Jin,et al.  Towards early identification of online rumors based on long short-term memory networks , 2019, Inf. Process. Manag..

[6]  Qiaozhu Mei,et al.  Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts , 2015, WWW.

[7]  Jun Zhang,et al.  Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection , 2017, ArXiv.

[8]  A. Sharifi,et al.  A Speech Act Classifier for Persian Texts and its Application in Identifying Rumors , 2019 .

[9]  Santhoshkumar Srinivasan,et al.  Earlier detection of rumors in online social networks using certainty-factor-based convolutional neural networks , 2020, Soc. Netw. Anal. Min..

[10]  Yann LeCun,et al.  Convolutional networks and applications in vision , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[11]  Francesco Marcelloni,et al.  A survey on fake news and rumour detection techniques , 2019, Inf. Sci..

[12]  Yixin Chen,et al.  An End-to-End Deep Learning Architecture for Graph Classification , 2018, AAAI.

[13]  L. D. Dhinesh Babu,et al.  Earlier detection of rumors in online social networks using certainty-factor-based convolutional neural networks , 2020, Social Network Analysis and Mining.

[14]  Hermann Ney,et al.  LSTM Neural Networks for Language Modeling , 2012, INTERSPEECH.

[15]  Soroush Vosoughi,et al.  A Human-Machine Collaborative System for Identifying Rumors on Twitter , 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

[16]  Deepayan Bhowmik,et al.  Fake News Identification on Twitter with Hybrid CNN and RNN Models , 2018, SMSociety.

[17]  Jure Leskovec,et al.  Hierarchical Graph Representation Learning with Differentiable Pooling , 2018, NeurIPS.

[18]  Wang Ling,et al.  Generative and Discriminative Text Classification with Recurrent Neural Networks , 2017, ArXiv.

[19]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.

[20]  LiakataMaria,et al.  Detection and Resolution of Rumours in Social Media , 2018 .

[21]  Wei Gao,et al.  Detect Rumors on Twitter by Promoting Information Campaigns with Generative Adversarial Learning , 2019, WWW.

[22]  Kyomin Jung,et al.  Prominent Features of Rumor Propagation in Online Social Media , 2013, 2013 IEEE 13th International Conference on Data Mining.

[23]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

[24]  Xavier Bresson,et al.  CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters , 2017, IEEE Transactions on Signal Processing.

[25]  Arkaitz Zubiaga,et al.  Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media , 2016, ArXiv.

[26]  Soroush Vosoughi,et al.  Rumor Gauge , 2017, ACM Trans. Knowl. Discov. Data.

[27]  Arkaitz Zubiaga,et al.  Detection and Resolution of Rumours in Social Media , 2017, ACM Comput. Surv..

[28]  Jintao Li,et al.  Automatic Rumor Detection on Microblogs: A Survey , 2018, ArXiv.

[29]  Yonghua Zhou,et al.  Parallel computing method of deep belief networks and its application to traffic flow prediction , 2019, Knowl. Based Syst..

[30]  Haiying Jiang,et al.  Effective use of convolutional neural networks and diverse deep supervision for better crowd counting , 2019, Applied Intelligence.

[31]  Yonghong Yu,et al.  Elective future: The influence factor mining of students’ graduation development based on hierarchical attention neural network model with graph , 2020, Applied Intelligence.

[32]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[33]  Si Zhang,et al.  Graph convolutional networks: a comprehensive review , 2019, Computational Social Networks.

[34]  Leysia Palen,et al.  Chatter on the red: what hazards threat reveals about the social life of microblogged information , 2010, CSCW '10.

[35]  M. Cha,et al.  Rumor Detection over Varying Time Windows , 2017, PloS one.

[36]  Matthew Andrews,et al.  Reconstruction and analysis of Twitter conversation graphs , 2012, HotSocial '12.

[37]  Dragomir R. Radev,et al.  Rumor has it: Identifying Misinformation in Microblogs , 2011, EMNLP.

[38]  Cheng-Jian Lin,et al.  Using convolutional neural networks for character verification on integrated circuit components of printed circuit boards , 2019, Applied Intelligence.

[39]  Wei Gao,et al.  Detecting Rumors from Microblogs with Recurrent Neural Networks , 2016, IJCAI.

[40]  Huai Liu,et al.  Metamorphic Testing , 2018, ACM Comput. Surv..

[41]  Jun Zhao,et al.  Multi-attributed heterogeneous graph convolutional network for bot detection , 2020, Inf. Sci..

[42]  George Mohler,et al.  Forecasting Retweet Count during Elections Using Graph Convolution Neural Networks , 2018, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).

[43]  Wenji Mao,et al.  MNRD: A Merged Neural Model For Rumor Detection In Social Media , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[44]  Max Welling,et al.  Variational Graph Auto-Encoders , 2016, ArXiv.

[45]  Bolong Zheng,et al.  Multiple Rumor Source Detection with Graph Convolutional Networks , 2019, CIKM.

[46]  Rui Lv,et al.  Rumors detection in Chinese via crowd responses , 2014, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014).

[47]  Jason R. C. Nurse,et al.  Determining the Veracity of Rumours on Twitter , 2016, SocInfo.