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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (10): 2-8    DOI: 10.11925/infotech.2096-3467.2018.0742
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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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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     
Received: 09 July 2018      Published: 12 November 2018

Cite this article:

Xin Wang,Wen’gang Feng. Review of Techniques Detecting Online Extremism and Radicalization. Data Analysis and Knowledge Discovery, 2018, 2(10): 2-8.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0742     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/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
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