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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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[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.

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[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.
[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.)
[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.
[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.
[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.
[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.
[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.
[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.
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