Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 17-23.doi: 10.11896/JsJkx.190900086

• Artificial Intelligence • Previous Articles     Next Articles

False Message Propagation Suppression Based on Influence Maximization

CHEN Jin-yin, ZHANG Dun-Jie, LIN Xiang, XU Xiao-dong and ZHU Zi-ling   

  1. College of Information Engineering,ZheJiang University of Technology,Hangzhou 310000,China
  • Published:2020-07-07
  • About author:CHEN Jin-yin, Ph.D, associate, professor.Her research interests include evolutionary computing, data mining, and deep learning algorithm.
  • Supported by:
    This work was supported by the ZheJiang Provincial Natural Science Foundation of China (LY19F020025),MaJor Special Funding for “Science and Technology Innovation 2025” in Ningbo(2018B10063),and Engineering Research Center of Cognitive Healthcare of ZheJiang Province(2018KFJJ07).

Abstract: With the wide development of various social media,the security issues caused by news transmission in social networks are becoming increasingly prominent.Especially,the propagation of false messages brings great threat to the security of cyberspace.In order to effectively control the propagation of false messages in cyberspace,and change the network topology as little as possible to suppress the false messages propagation,this paper proposed a false message propagation suppression method based on influence maximization.Firstly,it predicts the message propagation based on information cascade prediction model and puts forward two algorithms named Louvain Clustered Local Degree Centrality and Random Maximum Degree (LCLD,RMD) based on the idea of node influence maximization,to obtain the most influential nodes set,then use TextCNN to classify the false messages and filter out a small number of key nodes in the nodes set that publish false messages.The modified propagation network re-predicts the message propagation by prediction model.It is found that the message propagation is significantly suppressed compared to the network without modification.Finally,the proposed method is verified on the BuzzFeedNews dataset.It is proved by experiments that the prediction model based on information cascade can fit the actual propagation more accurately,and the prediction results of the modified network input prediction model show that the false message propagation can be suppressed.Experimental results show that the influence maximization algorithms can effectively suppress the propagation of false messages by deleting a few nodes containing false messages,which verifies the effectiveness of the proposed method.

Key words: Message propagation, False message recognition, Social network, Influence maximization, Deep learning

CLC Number: 

  • TP391
[1] VOSOUGHI S,ROY D,ARAL S.The spread of true and false news online.Science,2018,359(6380):1146-1151.
[2] ZHANG W,YE Y Q,TAN H L,et al.Information diffusion model based on social network//Proceedings of the 2012 International Conference of Modern Computer Science and Applications.Berlin Heidelberg:Springer-Verlag,2013:145-150.
[3] LI C,MA J,GUO X,et al.Deepcas:An end-to-end predictor of information cascades//Proceedings of the 26th international conference on World Wide Web.International World Wide Web Conferences Steering Committee,Perth,2017:577-586.
[4] KIM S B,HAN K S,RIM H C,et al.Some effective techniques for naive bayes text classification.IEEE Transactions on Knowledge and Data Engineering,2006,18(11):1457-1466.
[5] GENKIN A,LEWIS D D,MADIGAN D.Large-scale Bayesian logistic regression for text categorization.Technometrics,2007,49(3):291-304.
[6] COLAS F,BRAZDIL P.Comparison of SVM and some older classification algorithms in text classification tasks//IFIP International Conference on Artificial Intelligence in Theory and Practice.Boston:Springer,2006:169-178.
[7] KIM Y.Convolutional neural networks for sentence classification.arXiv:1408.5882,2014.
[8] LIU P,QIU X,HUANG X.Recurrent neural network for text classification with multi-task learning.arXiv:1605.05101,2016.
[9] BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by Jointly learning to align and translate.arXiv:1409.0473,2014.
[10] POTTHAST M,KIESEL J,REINARTZ K,et al.A stylometric inquiry into hyperpartisan and fake news.arXiv:1702.05638,2017.
[11] ZHENG M H,LV L Y,ZHAO M.Spreading in online social networks:the role ofsocial reinforcement.Physical Review E:Statistical Nonlinear and Soft Matter Physics,2013,88(1).
[12] ZAN Y L,WU J L,LI P,et al.SICR rumor spreading model in complex networks:counterattack and self-resistance.Physica A:Statistical Mechanics and Its Applications,2014,405(1):159-170.
[13] WANG X L,ZHAO L J.Rumor spreading model with skepticism mechanism in social networks.Journal of University of Shanghai for Science and Technology,2012,34(5):424-428.
[14] ZHANG Y M,TANG C S,Li W G.Research on interest attenuation and social reinforcement mechanism for rumor spreading in online social networks.Journal of the China Society for Scientific and Technical Information,2015,34(8):833-844.
[15] KAN J Q,XIE J R,ZHANG H F.Impacts of Social Reinforcement and Edge Weight on the Spreading of Information in Networks.Journal of University of Electronic Science and Technology of China,2014,43(1):21-25.
[16] JENDERS M,KASNECI G,NAUMANN F.Analyzing and predicting viral tweets//Proc.of WWW.2013:173-182
[17] CHENG J,ADAMIC L,DOW P A,et al.Can cascades be predicted//Proc.of WWW.2014:42-34.
[18] LERMAN K,GHOSH R.Information contagion:n empirical study of the spread of news on digg and twitter social networks//ICWSM.2010:54-32.
[19] YANG Y,TANG J,LEUNG C W K,et al.Rain:social role-aware information diffusion//Proc.of AAAI.2015:34-35.
[20] CHENG J,ADAMIC L,DOW P A,et al.Can cascades be predicted//Proc.of WWW.2014:43-55.
[21] MUKHERJEE A,VENKATARAMAN V,LIU B,et al.What yelp fake review filter might be doing?//Seventh International AAAI Conference on Weblogs and Social Media.Bellevue,2013.
[22] JING Y P.Reacher of deceptive opinions spam recognition based on deep learning.Shanghai:East China Normal University,2014.
[23] LI J,OTT M,CARDIE C,et al.Towards a general rule for identifying deceptive opinion spam//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.2014:1566-1576.
[24] LAU R Y K,LIAO S Y,KWOK R C W,et al.Text mining and probabilistic language modeling for online review spam detecting.ACM Transactions on Management Information Systems,2011,2(4):1-30.
[25] OTT M,CHOI Y,CARDIE C,et al. Finding deceptive opinion spam by any stretch of the imagination//Proceedings of the 49th annual meeting of the association for computational linguistics:Human language technologies.Association for Computational Linguistics,Oregon,2011:309-319.
[26] JINDAL N,LIU B,LIM E P.Finding unusual review patterns using unexpected rules//Proceedings of the 19th ACM International Conference on Information and Knowledge Management.ACM,Toronto,2010:1549-1552.
[27] JOULIN A,GRAVE E,BOJANOWSKI P,et al.Bag of Tricks for Efficient Text Classification.arXiv:1607.01759,2016.
[28] SUTSKEVERI,VINYALS O,LE Q V.Sequence to Sequence Learning with Neural Networks//Advances in Neural Information Processing Systems.2014.
[29] BAHDANAU D,CHO K,BENGIO Y.Neural Machine Translation by Jointly Learning to Align and Translate.arXiv:1409.0473.
[30] BLONDEL V D,GUILLAUME J L,LAMBIOTTE R,et al.Fast unfolding of communities in large networks.arXiv:0803.0476,2008.
[1] WANG Rui-ping, JIA Zhen, LIU Chang, CHEN Ze-wei, LI Tian-rui. Deep Interest Factorization Machine Network Based on DeepFM [J]. Computer Science, 2021, 48(1): 226-232.
[2] YU Wen-jia, DING Shi-fei. Conditional Generative Adversarial Network Based on Self-attention Mechanism [J]. Computer Science, 2021, 48(1): 241-246.
[3] TONG Xin, WANG Bin-jun, WANG Run-zheng, PAN Xiao-qin. Survey on Adversarial Sample of Deep Learning Towards Natural Language Processing [J]. Computer Science, 2021, 48(1): 258-267.
[4] DING Yu, WEI Hao, PAN Zhi-song, LIU Xin. Survey of Network Representation Learning [J]. Computer Science, 2020, 47(9): 52-59.
[5] MA Li-bo, QIN Xiao-lin. Topic-Location-Category Aware Point-of-interest Recommendation [J]. Computer Science, 2020, 47(9): 81-87.
[6] HE Xin, XU Juan, JIN Ying-ying. Action-related Network:Towards Modeling Complete Changeable Action [J]. Computer Science, 2020, 47(9): 123-128.
[7] YE Ya-nan, CHI Jing, YU Zhi-ping, ZHAN Yu-liand ZHANG Cai-ming. Expression Animation Synthesis Based on Improved CycleGan Model and Region Segmentation [J]. Computer Science, 2020, 47(9): 142-149.
[8] DENG Liang, XU Geng-lin, LI Meng-jie, CHEN Zhang-jin. Fast Face Recognition Based on Deep Learning and Multiple Hash Similarity Weighting [J]. Computer Science, 2020, 47(9): 163-168.
[9] BAO Yu-xuan, LU Tian-liang, DU Yan-hui. Overview of Deepfake Video Detection Technology [J]. Computer Science, 2020, 47(9): 283-292.
[10] YUAN Ye, HE Xiao-ge, ZHU Ding-kun, WANG Fu-lee, XIE Hao-ran, WANG Jun, WEI Ming-qiang, GUO Yan-wen. Survey of Visual Image Saliency Detection [J]. Computer Science, 2020, 47(7): 84-91.
[11] WANG Wen-dao, WANG Run-ze, WEI Xin-lei, QI Yun-liang, MA Yi-de. Automatic Recognition of ECG Based on Stacked Bidirectional LSTM [J]. Computer Science, 2020, 47(7): 118-124.
[12] LIU Yan, WEN Jing. Complex Scene Text Detection Based on Attention Mechanism [J]. Computer Science, 2020, 47(7): 135-140.
[13] ZHANG Zhi-yang, ZHANG Feng-li, TAN Qi, WANG Rui-jin. Review of Information Cascade Prediction Methods Based on Deep Learning [J]. Computer Science, 2020, 47(7): 141-153.
[14] JIANG Wen-bin, FU Zhi, PENG Jing, ZHU Jian. 4Bit-based Gradient Compression Method for Distributed Deep Learning System [J]. Computer Science, 2020, 47(7): 220-226.
[15] CHENG Zhe, BAI Qian, ZHANG Hao, WANG Shi-pu and LIANG Yu. Improving Hi-C Data Resolution with Deep Convolutional Neural Networks [J]. Computer Science, 2020, 47(6A): 70-74.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .