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数据分析与知识发现  2018, Vol. 2 Issue (10): 2-8    DOI: 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
Xin Wang(),Wen’gang Feng
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
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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。

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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     
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.
Xin Wang,Wen’gang Feng. Review of Techniques Detecting Online Extremism and Radicalization. Data Analysis and Knowledge Discovery, DOI:10.11925/infotech.2096-3467.2018.0742.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0742
类别 方法
Lexicon based Rule-based, Statistic-based
Machine learning SVM, KNN, Adaboost/Decision Tree, Na?ve Bayes, Logistic Regression, Random Forest
表1  主要计算方法
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