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

CLC Number: 

  • TP391
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