基于影响力最大化策略的抑制虚假消息传播的方法

doi:10.11896/JsJkx.190900086

摘要/Abstract

摘要： 随着各种社交媒体不断兴起,社交网络中消息传播所带来的安全问题显得愈发突出。其中,虚假消息的传播给网络空间的安全带来了极大威胁。为了在尽可能小地改变网络拓扑结构的前提下抑制虚假消息在网络空间的肆意传播,提出了一种基于影响力最大化的抑制虚假消息传播的方法。首先基于信息级联预测模型对消息传播进行预测,提出基于节点影响力最大化思想的两种算法Louvain Clustered Local Degree Centrality(LCLD)和Random Maximum Degree(RMD),得到影响力最大的节点集合;然后利用TextCNN对虚假消息进行分类识别,过滤掉节点集合中的少量关键节点。修改后的传播网络重新通过预测模型进行消息传播预测,结果虚假消息的传播相比于网络修改前得到了明显抑制。最后在真实数据集BuzzFeedNews上展开验证,首先通过实验验证基于信息级联的预测模型可以较准确地拟合实际传播;再将修改后的网络输入预测模型进行预测,结果显示虚假消息传播可得到抑制,表明采用影响力最大化算法删减少量包含虚假消息的节点可有效抑制虚假消息的传播,从而验证了所提方法的有效性。

关键词: 社交网络, 深度学习, 消息传播, 虚假消息识别, 影响力最大化

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

中图分类号:

TP391

陈晋音, 张敦杰, 林翔, 徐晓东, 朱子凌. 基于影响力最大化策略的抑制虚假消息传播的方法[J]. 计算机科学, 2020, 47(6A): 17-23. https://doi.org/10.11896/JsJkx.190900086

CHEN Jin-yin, ZHANG Dun-Jie, LIN Xiang, XU Xiao-dong and ZHU Zi-ling. False Message Propagation Suppression Based on Influence Maximization[J]. Computer Science, 2020, 47(6A): 17-23. https://doi.org/10.11896/JsJkx.190900086

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