微博垃圾用户行为研究  

Research on the behavior of Weibo spammers

作　　者：高郭威 赵卫东[1] 孙中全[1] GAO Guowei;ZHAO Weidong;SUN Zhongquan(Chuzhou Polytechnic,Chuzhou 239000,China)

机构地区：[1]滁州职业技术学院,安徽滁州239000

出　　处：《无线互联科技》2024年第22期92-97,共6页Wireless Internet Science and Technology

基　　金：2022年安徽省高校自然科学研究重大项目:项目编号:2022AH040332;安徽省职成教项目:项目名称:后疫情时代基于OBE理念的高职公共基础课程混合式教学模式的构建与实施:项目编号:Azcj2022178;安徽省职业与成人教育学会教育科研规划课题:项目编号:Azcj2022180。

摘　　要：垃圾用户作为垃圾信息的传播者,一直是微博反垃圾研究的重点,现有的垃圾用户检测研究还局限于传统的二值分类问题上,只是将用户简单地判断为垃圾用户和正常用户。然而,微博平台中的垃圾用户类型多种多样,将各类垃圾用户按照单一类别垃圾用户进行处理,会存在各类垃圾用户之间特征相互影响的问题,从而降低整体检测效果。为了解决上述问题,文章对各类垃圾用户行为进行了分析。首先,根据垃圾用户的行为目的和行为模式,将垃圾用户分为4类;其次,通过爬虫程序获取数据集,构造特征分析样本集并进行标注,计算用户的各项统计特征;最后,对4类垃圾用户的特征进行定量分析,归纳总结出每类用户的特点。实验结果表明,各类垃圾用户与正常用户之间存在区分度较高的相关特征,利用这些特征能够有效区分各种垃圾用户与正常用户,提升检测精度。Spammer,as a disseminator of spam,has become the focus of Weibo’s anti-spam research.Existing research on spammer detection is confined to traditional binary classification problem,which is simply to determine the user for spammer and non-spammer.However,there are many types of spammers in the Weibo platform,if all kinds of spammers are considered as the same category,there will be the problem that spammers’characteristics can affect each other,so that the overall detection performance decreases.To solve this problem,the behavior of many kinds of spammers is analyzed in this thesis.First of all,according to spammers’behavior purposes and behavior patterns,spammers are classified into four categories.Secondly,the data sets are obtained by the crawler program,and a set of samples for analyzing the characteristics are constructed and labeled,then the statistical characteristics of users are calculated.Finally,the characteristics of the four types of spammers are analyzed quantitatively,and the characteristics of each type of users are summarized.The experimental results show that there are highly distinguishable features between various types of spammers and non-spammer,which can effectively distinguish various types of spammers and non-spammer and improve the detection accuracy.

关 键 词：微博 垃圾用户 用户行为 用户分类 

分 类 号：TP393.092[自动化与计算机技术—计算机应用技术]