通信学报2017，Vol.38Issue(1)：44-53,10.DOI:10.11959/j.issn.1000-436x.2017006
基于贝叶斯模型的微博网络水军识别算法研究
Weibo spammers' identification algorithm based on Bayesian model
摘要
Abstract
In order to distinguish the spammers efficiently, a classifier based on the behavior characteristics was estab-lished. By analyzing the previous research, the ratio of followers, total number of blog posts, the number of friends, com-prehensive quality evaluation and favorites according to latest data set, the Weibo spammers' identification algorithm was realized based on Bayesian model and genetic algorithm. The experiment result based on the real-time data of Sina Weibo verify that the Bayesian model recognition algorithm can ensure spammers recognition accuracy without sacrificing rec-ognition rate of non-spammers, and the proposed threshold value matrix proposed optimization can significantly improve recognition accuracy navy.关键词
网络水军/水军识别/微博/贝叶斯模型/遗传算法Key words
network spammer/spammer identification/Weibo/Bayesian model/genetic algorithm分类
信息技术与安全科学引用本文复制引用
张艳梅,黄莹莹,甘世杰,丁熠,马志龙..基于贝叶斯模型的微博网络水军识别算法研究[J].通信学报,2017,38(1):44-53,10.基金项目
国家自然科学基金资助项目(No.61602536, No.61273293, No.61309029) （No.61602536, No.61273293, No.61309029）
北京市社会科学重点基金资助项目(No.16YJA001) （No.16YJA001）
网络与数据安全四川省重点实验室开放课题基金资助项目(No.NDSMS201605) （No.NDSMS201605）
中央财经大学学科建设基金资助项目 The National Natural Science Foundation of China (No.61602536, No.61273293, No.61309029), Beijing Mu-nicipal Social Science Foundation (No.16YJA001), The Open Projectof Network and Data Security Key Laboratory of Sichuan Province (No.NDSMS201605), The Discipline Construction Foundation of the Central University of Finance and Economics （No.61602536, No.61273293, No.61309029）
