通过社会网络结构分析发现网络极端主义

M. Petrovskiy, M. Chikunov
{"title":"通过社会网络结构分析发现网络极端主义","authors":"M. Petrovskiy, M. Chikunov","doi":"10.1109/INFOCT.2019.8711254","DOIUrl":null,"url":null,"abstract":"The activity of extremist organizations on the Internet is continuously growing with the increase of Web’s usage for means of communication. Therefore analysis of radical members in social networks provides important information on how to prevent them propagate ideology and recruiting new members in the future. But nowadays terrorists often use confidential chats and private threads to communicate, thus it’s quite hard to detect them using only the public messages they generate. In fact, it is usually known that some users of social networks are dangerous, another are innocent, and no information is available about the remaining users. In this paper, we propose an approach for detecting radical users of social network among unknown ones by analyzing their relationships and features as of vertices of social graph without usage of any information about text content they generate. We find that the proposed method is very promising and may be efficiently used for real-time monitoring systems and future terrorism and extremism research.","PeriodicalId":369231,"journal":{"name":"2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Online Extremism Discovering through Social Network Structure Analysis\",\"authors\":\"M. Petrovskiy, M. Chikunov\",\"doi\":\"10.1109/INFOCT.2019.8711254\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The activity of extremist organizations on the Internet is continuously growing with the increase of Web’s usage for means of communication. Therefore analysis of radical members in social networks provides important information on how to prevent them propagate ideology and recruiting new members in the future. But nowadays terrorists often use confidential chats and private threads to communicate, thus it’s quite hard to detect them using only the public messages they generate. In fact, it is usually known that some users of social networks are dangerous, another are innocent, and no information is available about the remaining users. In this paper, we propose an approach for detecting radical users of social network among unknown ones by analyzing their relationships and features as of vertices of social graph without usage of any information about text content they generate. We find that the proposed method is very promising and may be efficiently used for real-time monitoring systems and future terrorism and extremism research.\",\"PeriodicalId\":369231,\"journal\":{\"name\":\"2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INFOCT.2019.8711254\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INFOCT.2019.8711254","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

随着网络作为传播手段的日益普及,极端组织在互联网上的活动也在不断增加。因此,对社交网络中激进分子的分析为今后如何防止他们传播意识形态和招募新成员提供了重要的信息。但现在恐怖分子经常使用保密聊天和私人线程进行通信,因此仅使用他们生成的公开信息很难发现他们。事实上,人们通常知道社交网络的一些用户是危险的,另一些是无辜的,并且没有关于其余用户的信息。在本文中,我们提出了一种在不使用他们生成的文本内容的任何信息的情况下,通过分析他们作为社交图顶点的关系和特征,在未知用户中检测激进社交网络用户的方法。我们发现所提出的方法非常有前途,可以有效地用于实时监测系统和未来的恐怖主义和极端主义研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Online Extremism Discovering through Social Network Structure Analysis
The activity of extremist organizations on the Internet is continuously growing with the increase of Web’s usage for means of communication. Therefore analysis of radical members in social networks provides important information on how to prevent them propagate ideology and recruiting new members in the future. But nowadays terrorists often use confidential chats and private threads to communicate, thus it’s quite hard to detect them using only the public messages they generate. In fact, it is usually known that some users of social networks are dangerous, another are innocent, and no information is available about the remaining users. In this paper, we propose an approach for detecting radical users of social network among unknown ones by analyzing their relationships and features as of vertices of social graph without usage of any information about text content they generate. We find that the proposed method is very promising and may be efficiently used for real-time monitoring systems and future terrorism and extremism research.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Utilization of Data Mining for Generalizable, All-Admission Prediction of Inpatient Mortality Development of Navigation Monitoring & Assistance Service Data Model ITIKI Plus: A Mobile Based Application for Integrating Indigenous Knowledge and Scientific Agro-Climate Decision Support for Africa’s Small-Scale Farmers TFDroid: Android Malware Detection by Topics and Sensitive Data Flows Using Machine Learning Techniques Weighted DV-Hop Localization Algorithm for Wireless Sensor Network based on Differential Evolution Algorithm
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1