Please wait a minute...
Advanced Search
数据分析与知识发现  2018, Vol. 2 Issue (10): 2-8     https://doi.org/10.11925/infotech.2096-3467.2018.0742
  专题 本期目录 | 过刊浏览 | 高级检索 |
在线极端主义和激进化监测技术综述*
王欣(), 冯文刚
中国人民公安大学侦查与反恐怖学院 北京 100038
中国人民公安大学公安情报研究中心 北京 100038
Review of Techniques Detecting Online Extremism and Radicalization
Wang Xin(), Feng Wen’gang
School of Criminal Investigation and Counter Terrorism, People’s Public Security University of China, Beijing 100038, China
Research Center for Public Security Intelligence, People’s Public Security University of China, Beijing 100038, China
全文: PDF (450 KB)   HTML ( 2
输出: BibTeX | EndNote (RIS)      
摘要 

【目的】分析并评述当前在网络上监测极端主义思想传播和个体思想激进化的主要技术。【方法】在文献分析的基础上, 对相关技术的解决思路、数据来源、标注方法、算法进行分析归纳。【结果】在社交网络极端主义检测与发现领域, 研究者更多地借鉴心理学和社会学研究成果, 细化检测指标和检测方法, 构造多样化的检测模式。主要技术分为基于词典和基于机器学习两类, 由于基于机器学习的方法具有准确率高、速度快的优势, 因此使用较基于词典的方法更加频繁, 但是如何科学有效地标注训练数据集是研究难点。【结论】该领域的技术应用尚处于初级探索阶段, 需要将更多的量化研究投入到对激进化过程的分析中。检测技术研究者应更多地与社会学和心理学研究者合作, 以开发出更加精细的模型。需要投入更多的研究资源以提高训练数据集的标注速度和准确性。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
王欣
冯文刚
关键词 知识发现机器学习激进化极端主义    
Abstract

[Objective] This paper reviews the technical solutions for detecting online extremism and radicalization. [Methods] First, we retrieved the needed literature by conducting keyword search with several popular academic databases. Then, we reviewed these papers and summarized their theoretical frameworks, data sources, labelling method, and algorithms. [Results] Researchers have obtained insights from the latest psychology and sociology studies, which helped them refine the detection indicators and methods. The two popular techniques used in this field were based on lexicon method and machine learning algorithm. Although machine-learning methods had the advantages of better accuracy and faster speed, it is very hard for us to construct the training data sets. [Limitations] We did not compare the effectiveness of different solutions. [Conclusions] The reviewed techniques are still developing and more quantitative research is required to analyze the radicalization process. We need to co-operate with sociology and psychology researchers to develop new models and better training data sets.

Key wordsKnowledge Discovery    Machine Learning    Radicalization    Extremism
收稿日期: 2018-07-09      出版日期: 2018-11-12
ZTFLH:  TP393 G35  
基金资助:*本文系国家社会科学基金重大项目“当前我国反恐形势及对策研究”(项目编号: 15ZDA034)的研究成果之一
引用本文:   
王欣, 冯文刚. 在线极端主义和激进化监测技术综述*[J]. 数据分析与知识发现, 2018, 2(10): 2-8.
Wang Xin,Feng Wen’gang. Review of Techniques Detecting Online Extremism and Radicalization. Data Analysis and Knowledge Discovery, 2018, 2(10): 2-8.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0742      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2018/V2/I10/2
类别 方法
Lexicon based Rule-based, Statistic-based
Machine learning SVM, KNN, Adaboost/Decision Tree, Na?ve Bayes, Logistic Regression, Random Forest
  主要计算方法
[1] 王飞跃, 王珏. 情报与安全信息学研究的现状与展望[J]. 中国基础科学, 2005, 7(2): 24-29.
doi: 10.3969/j.issn.1009-2412.2005.02.005
[1] (Wang Feiyue, Wang Jue.Intelligence and Security Informatics: The State of the Art and Outlook[J]. China Basic Science, 2005, 7(2): 24-29.)
doi: 10.3969/j.issn.1009-2412.2005.02.005
[2] Locicero A.Why “Good Kids” Turn into Deadly Terrorists: Deconstructing the Accused Boston Marathon Bombers and Others Like Them[M]. Santa Barbara: Praeger, 2014: 37-53.
[3] Borum R.Radicalization into Violent Extremism II: A Review of Conceptual Models and Empirical Research[J]. Journal of Strategic Security, 2011, 4(4): 37-62.
doi: 10.5038/1944-0472.4.4.2
[4] Kundnani A.Radicalisation: The Journey of a Concept[J]. Race & Class, 2012, 54(2): 3-25.
[5] Torok R.Developing an Explanatory Model for the Process of Online Radicalisation and Terrorism[J]. Security Informatics, 2013, 2(1): 1-10.
doi: 10.1186/2190-8532-2-1
[6] UK Government.CONTEST: UK Strategy for Countering Terrorism[R]. 2011.
[7] Argomaniz J.European Union Responses to Terrorist Use of the Internet[J]. Cooperation and Conflict, 2014, 50(2): 250-268.
[8] Brussels. A Europe that Protects: Commission Reinforces EU Response to Illegal Content Online[EB/OL]. [2018-04-01]. .
[9] Berger J M, Morgan J.The ISIS Twitter Census: Defining and Describing the Population of ISIS Supporters on Twitter[A]// The Brookings Project on U.S. Relations with the Islamic World[M]. The Brookings Institution. https://www.brookings. edu/wp-content/uploads/2016/06/isis_Twitter_census_berger_morgan.pdf.
[10] Correa D, Sureka A.Solutions to Detect and Analyze Online Radicalization: A Survey[OL]. arXiv Preprint. arXiv: 1301.4916.
[11] Agarwal S, Sureka A.Applying Social Media Intelligence for Predicting and Identifying On-line Radicalization and Civil Unrest Oriented Threats[OL]. arXiv Preprint. arXiv: 1511.06858.
[12] Last M, Markov A, Kandel A.Multi-lingual Detection of Terrorist Content on the Web[M]. Berlin, Heidelberg: Springer, 2006: 16-30.
[13] Huang C, Fu T, Chen H.Text-based Video Content Classification for Online Video-sharing Sites[J]. Journal of the Association for Information Science and Technology, 2010, 61(5): 891-906.
[14] Greevy E, Smeaton A F.Classifying Racist Texts Using a Support Vector Machine[C]// Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2004: 468-469.
[15] Fu T, Huang C N, Chen H.Identification of Extremist Videos in Online Video Sharing Sites[C]// Proceedings of the 2009 IEEE International Conference on Intelligence and Security Informatics. 2009: 179-181.
[16] Scanlon J R, Gerber M S.Automatic Detection of Cyber- recruitment By Violent Extremists[J]. Security Informatics, 2014, 3(1): 5.
doi: 10.1186/s13388-014-0005-5
[17] Agarwal S, Sureka A.Using KNN and SVM Based One-Class Classifier for Detecting Online Radicalization on Twitter[C]// Proceedings of the 11th International Conference on Distributed Computing and Internet Technology. 2015: 431-442.
[18] Badjatiya P, Gupta,M S, Gupta M, et al. Deep Learning for Hate Speech Detection in Tweets[C]//Proceedings of the 26th International Conference on World Wide Web Companion. 2017: 759-760.
[19] Behr I V, Reding A, Edwards C, et al.Radicalisation in the Digital Era: The Use of the Internet in 15 Cases of Terrorism and Extremism[R]. Rand Corporation, 2013.
[20] Azizan S A, Aziz I A.Terrorism Detection Based Sentiment Analysis Using Machine Learning[J]. Journal of Engineering and Applied Sciences, 2017, 12(3): 691-698.
[21] Ferrara E, Wang W Q, Varol O, et al.Predicting Online Extremism, Content Adopters, and Interaction Reciprocity[C]// Proceedings of the 2016 International Conference on Social Informatics. Springer, 2016: 22-39.
[22] Lara-Cabrera R, Pardo A G, Benouaret K, et al.Measuring the Radicalisation Risk in Social Networks[J]. IEEE Access, 2017(5): 10892-10900.
[23] Saidi F, Trabelsi Z, Salah K, et al.Approaches to Analyze Cyber Terrorist Communities: Survey and Challenges[J]. Computer and Security, 2017, 66: 66-80.
doi: 10.1016/j.cose.2016.12.017
[24] Benigni M C, Joseph K, Carley K M.Online Extremism and the Communities that Sustain It: Detecting the ISIS Supporting Community on Twitter[J]. PLoS ONE, 2017, 12(12): e0181405.
doi: 10.1371/journal.pone.0181405 pmid: 29194446
[25] Wadhwa P, Bhatia M P S. Tracking On-line Radicalization Using Investigative Data Mining[C]// Proceedings of the 2013 National Conference on Communications. 2013: 1-5.
[26] Ross B, Rist M, Carbonell G, et al.Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisi[OL]. arXiv Preprint. arXiv:1701.08118.
[27] Waseem Z.Are You a Racist or am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter[C]// Proceedings of the 2016 EMNLP Workshop on Natural Language Processing and Computational Social Science. 2016: 138-142.
[28] Waseem Z, Hovy D.Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter[C]// Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016: 88-93.
[29] Gitari N D, Zuping Z, Damien H, et al.A Lexicon-based Approach for Hate Speech Detection[J]. International Journal of Multimedia and Ubiquitous Engineering, 2015, 10(4): 215-230.
[30] Thakkar H, Patel D.Approaches for Sentiment Analysis on Twitter: A State-of-Art Study[OL]. arXiv Preprint. arXiv: 1512.01043.
[31] Medhat W, Hassan A, Korashy H.Sentiment Analysis Algorithms and Applications: A Survey[J]. Ain Shams Engineering Journal, 2014, 5(4): 1093-1113.
doi: 10.1016/j.asej.2014.04.011
[1] 陈东,王建冬,李慧颖,蔡思航,黄倩倩,易成岐,曹攀. 融合机器学习算法和多因素的禽肉交易量预测方法研究 *[J]. 数据分析与知识发现, 2020, 4(7): 18-27.
[2] 梁野,李小元,许航,胡伊然. CLOpin:一种面向舆情分析与预警领域的跨语言知识图谱架构*[J]. 数据分析与知识发现, 2020, 4(6): 1-14.
[3] 杨恒,王思丽,祝忠明,刘巍,王楠. 基于并行协同过滤算法的领域知识推荐模型研究*[J]. 数据分析与知识发现, 2020, 4(6): 15-21.
[4] 王若佳,张璐,王继民. 基于机器学习的在线问诊平台智能分诊研究[J]. 数据分析与知识发现, 2019, 3(9): 88-97.
[5] 李纲,周华阳,毛进,陈思菁. 基于机器学习的社交媒体用户分类研究 *[J]. 数据分析与知识发现, 2019, 3(8): 1-9.
[6] 胡佳慧,方安,赵琬清,杨晨柳,任慧玲. 面向知识发现的中文电子病历标注方法研究 *[J]. 数据分析与知识发现, 2019, 3(7): 123-132.
[7] 张金柱,胡一鸣. 融合表示学习与机器学习的专利科学引文标题自动抽取研究*[J]. 数据分析与知识发现, 2019, 3(5): 68-76.
[8] 刘志强,都云程,施水才. 基于改进的隐马尔科夫模型的网页新闻关键信息抽取*[J]. 数据分析与知识发现, 2019, 3(3): 120-128.
[9] 徐红霞,李春旺. 科技文献内容知识点抽取研究综述[J]. 数据分析与知识发现, 2019, 3(3): 14-24.
[10] 吴菊华,王煜,黎明,蔡少云. 基于加权知识网络的在线健康社区用户知识发现*[J]. 数据分析与知识发现, 2019, 3(2): 108-117.
[11] 李静,潘舒笑,李雪岩,贾立静,赵宇卓. 基于多目标量子优化分类器的急诊危重患者关键指标筛选 *[J]. 数据分析与知识发现, 2019, 3(12): 101-112.
[12] 杨磊,王子润,侯贵生. 基于Q-LDA主题模型的网络健康社区主题挖掘研究 *[J]. 数据分析与知识发现, 2019, 3(11): 52-59.
[13] 沈洋,庄伟超,吴清华,钱玲飞. 基于区间模糊VIKOR的监犯特征风险评估研究 *[J]. 数据分析与知识发现, 2019, 3(11): 70-78.
[14] 胡吉颖,谢靖,钱力,付常雷. 基于知识图谱的科技大数据知识发现平台建设*[J]. 数据分析与知识发现, 2019, 3(1): 55-62.
[15] 张紫玄,王昊,朱立平,邓三鸿. 中国海关HS编码风险的识别研究*[J]. 数据分析与知识发现, 2019, 3(1): 72-84.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn