计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 17-23.doi: 10.11896/JsJkx.190900086
陈晋音, 张敦杰, 林翔, 徐晓东, 朱子凌
CHEN Jin-yin, ZHANG Dun-Jie, LIN Xiang, XU Xiao-dong and ZHU Zi-ling
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。
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