基于用户特征提取的新浪微博异常用户检测方法

doi:10.11896/JsJkx.190700008

计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 364-368.doi: 10.11896/JsJkx.190700008

基于用户特征提取的新浪微博异常用户检测方法

袁得嵛^{1, 2}, 章逸钒¹, 高见^{1, 2}, 孙海春^{1, 2}

1 中国人民公安大学信息技术与网络安全学院北京 102623;
2 安全防范与风险评估公安部重点实验室北京 102623

发布日期:2020-07-07
通讯作者: 高见(gaoJian@ppsuc.edu.cn)
作者简介:yuandeyu@ppsuc.edu.cn
基金资助:
国家自然科学基金(61771072);中国人民公安大学专项项目(2020JWCX01)

Abnormal User Detection Method in Sina Weibo Based on User Feature Extraction

YUAN De-yu^{1, 2}, ZHANG Yi-fan¹, GAO Jian^{1, 2} and SUN Hai-chun^{1, 2}

1 Institute of Information Technology and Cyber Security,People’s Public Security University of China,BeiJing 102623,China
2 Key Laboratory of Safety Precautions and Risk Assessment,Ministry of Public Security,BeiJing 102623,China

Published:2020-07-07
About author:YUAN De-yu, born in 1986, Ph.D, lecturer.His main research interests include cyber security, and complex networks.
GAO Jian, born in 1982, Ph.D, lecturer.His main research interests include botnet, malware analysis and cyber crime.
Supported by:
This work was supported by the National Natural Science Foundation of China (61771072) and Special ProJect of People’s Public Security University of China (2020JWCX01).

摘要/Abstract

摘要： 随着互联网的发展,微博逐渐成为重要的社交媒体。然而,在微博中异常用户通过传播有害信息、发送恶意链接,甚至发起恶意攻击等方式影响用户的行为,从而影响了社交网络的价值。因此,实现对异常用户的检测具有重要的意义。文中以多途径获取的微博异常用户和正常用户数据集为基础,对其进行数据清洗后,提出综合提取并分析用户的多种属性。多种数据挖掘方法建立异常用户检测模型,从而进行异常用户账号的识别。对C4.5决策树、随机森林等算法的实验结果表明,所提方法选取的特征有效,检测异常用户的精度较高。

关键词: 数据挖掘, 特征提取, 微博, 异常用户

Abstract: With the development of the Internet,Weibo has gradually become an important social media.However,in Weibo,abnormal users influence the behaviors of users by spreading harmful information,sending malicious links,and even launching malicious attacks,thus affecting the value of social networks.Therefore,it is important to realize the detection of abnormal users.Based on the Weibo abnormal users and normal user data sets obtained from multiple ways,this paper proposes to comprehensive extract and analyze various attributes of users.An abnormal user detection model is established through various data mining methods to identify abnormal user accounts.Experimental results of C4.5 decision tree and random forest algorithms show that by using the proposed method,the selected features are effective and the detection accuracy of abnormal users is high.

Key words: Abnormal user, Data mining, Feature extraction, Weibo

中图分类号:

TP309

袁得嵛, 章逸钒, 高见, 孙海春. 基于用户特征提取的新浪微博异常用户检测方法[J]. 计算机科学, 2020, 47(6A): 364-368. https://doi.org/10.11896/JsJkx.190700008

YUAN De-yu, ZHANG Yi-fan, GAO Jian and SUN Hai-chun. Abnormal User Detection Method in Sina Weibo Based on User Feature Extraction[J]. Computer Science, 2020, 47(6A): 364-368. https://doi.org/10.11896/JsJkx.190700008

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基于用户特征提取的新浪微博异常用户检测方法

Abnormal User Detection Method in Sina Weibo Based on User Feature Extraction

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