Spotting the Islamist Radical within: Religious Extremists Profiling in the United State

Al-Zewairi, Malek; Naymat, Ghazi Al

doi:10.1016/j.procs.2017.08.336

Cited by 14 publications

(14 citation statements)

References 8 publications

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“…Another limitation was the inclusion of all ideological backgrounds despite that Far Right, Far Left, Islamist, and Single-Issue extremists differ from each other in multiple ways ( Jensen et al, 2015 ; Al-Zewairi and Naymat, 2017 ; Freilich et al, 2018 ). We studied all extremists together because this allowed the comparison of ideologies within the same analysis.…”

Section: Discussionmentioning

confidence: 99%

“…The aims of studies was to predict: terrorism risk by analyzing internet communications ( Cheong and Lee, 2011 ; Iskandar, 2017 ; Pelzer, 2018 ; Kalaiarasi et al, 2019 ), the location, timing and/or the type of future attacks ( Ding et al, 2017 ; Verma et al, 2018 ; Hao et al, 2019 ; Singh et al, 2019 ; Uddin et al, 2020 ), the terrorist group responsible for an attack ( Tolan and Soliman, 2015 ; Talreja et al, 2017 ; Alfatih et al, 2019 ), the weapons which may be used in forthcoming attacks ( Verma et al, 2018 ; Narula et al, 2020 ) and global social determinants of terrorism ( Gassebner and Luechinger, 2011 ). Two studies used the PIRUS database to identify Islamist radicals based on data unrelated to ideological backgrounds ( Al-Zewairi and Naymat, 2017 ) and to predict the use of chemical/biological weapons ( Guarrieri and Meisel, 2019 ). Prediction accuracy was around ∼80%.…”

Section: Introductionmentioning

confidence: 99%

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Risk Matrix for Violent Radicalization: A Machine Learning Approach

Ivaskevics

Haller

2022

Front. Psychol.

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Hypothesis-driven approaches identified important characteristics that differentiate violent from non-violent radicals. However, they produced a mosaic of explanations as they investigated a restricted number of preselected variables. Here we analyzed without a priory assumption all the variables of the “Profiles of Individual Radicalization in the United States” database by a machine learning approach. Out of the 79 variables considered, 19 proved critical, and predicted the emergence of violence with an accuracy of 86.3%. Typically, violent extremists came from criminal but not radical backgrounds and were radicalized in late stages of their life. They were followers in terrorist groups, sought training, and were radicalized by social media. They belonged to low social strata and had problematic social relations. By contrast, non-violent but still criminal extremists were characterized by a family tradition of radicalism without having criminal backgrounds, belonged to higher social strata, were leaders in terrorist organizations, and backed terrorism by supporting activities. Violence was also promoted by anti-gay, Sunni Islam and Far Right, and hindered by Far Left, Anti-abortion, Animal Rights and Environment ideologies. Critical characteristics were used to elaborate a risk-matrix, which may be used to predict violence risk at individual level.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk Matrix for Violent Radicalization: A Machine Learning Approach

Ivaskevics

Haller

2022

Front. Psychol.

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“…In 2015 alone, there were more than eleven thousand terrorist attacks globally, as a result of which more than twenty-eight thousand people were killed [3]. While in 2016, a study by Peace Tech Lab showed that 1,441 terrorist attacks occurred worldwide with more than fourteen thousand deaths [4]. While in the first half of 2017, the number of terrorist attacks reached 520, as a result of which, according to the map of terrorist incidents, 3565 people were killed, and that year, terrorism was responsible for 0.05% of global deaths [5].…”

Section: Introductionmentioning

confidence: 99%

Applying Machine Learning Techniques for Religious Extremism Detection on Online User Contents

Mussiraliyeva¹,

Omarov²,

Yoo³

et al. 2022

Computers, Materials &Amp; Continua

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In this research paper, we propose a corpus for the task of detecting religious extremism in social networks and open sources and compare various machine learning algorithms for the binary classification problem using a previously created corpus, thereby checking whether it is possible to detect extremist messages in the Kazakh language. To do this, the authors trained models using six classic machine-learning algorithms such as Support Vector Machine, Decision Tree, Random Forest, K Nearest Neighbors, Naive Bayes, and Logistic Regression. To increase the accuracy of detecting extremist texts, we used various characteristics such as Statistical Features, TF-IDF, POS, LIWC, and applied oversampling and undersampling techniques to handle imbalanced data. As a result, we achieved 98% accuracy in detecting religious extremism in Kazakh texts for the collected dataset. Testing the developed machine learning models in various databases that are often found in everyday life "Jokes", "News", "Toxic content", "Spam", "Advertising" has also shown high rates of extremism detection.

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“…Debates about the perpetrators and motives of terrorism often lead to the ideological or religious identification of terrorist actors, because ideology or religion is a source of legitimacy for their actions (Alexander, 2013;Rokhmad, 2017;Triandis, 2013). The state gets the legitimacy of violence or terror because of its constitutional sovereignty or authority, while community groups usually get legitimacy from the ideology or religion they want to fight for (Al-zewairi & Naymat, 2017;Rokhmad, 2017). The issue of Islamic terrorism needs to be understood and emphasized further, whether Islam legitimizes terror or terrorist actors, Islamic state or not, and those who have hijacked Islam (Arifin, 2017;Khairil, 2017b;Sinaulan, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…The issue of Islamic terrorism needs to be understood and emphasized further, whether Islam legitimizes terror or terrorist actors, Islamic state or not, and those who have hijacked Islam (Arifin, 2017;Khairil, 2017b;Sinaulan, 2016). The biased perception of terrorism from some circles in the Western world and non-Muslim communities in Indonesia has placed Islam as a threat (Al-zewairi & Naymat, 2017;Törnberg & Törnberg, 2016;Warren, 2019). The impact of this perception seems to be stronger since the terrorist attacks on World Trade Center and Bali were broadcast by the mass media.…”

Section: Introductionmentioning

confidence: 99%

Legal Actions of Terrorism Case in Central Sulawesi

Khairil

Sulbadana

Bakri

2022

lhqr

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This study focused on the legal action of terrorism cases in Poso, Central Sulawesi. The focus of the analysis is to accentuate the contents of the legislation related to the legal handling of terrorism cases in Central Sulawesi. Thus, the study was carried out through statute, comparative law and conceptual approach, as well as using qualitative data analysis method. Data were processed by first describing all the legal materials that had been collected through inventory, identification, classification and systematization according to the problems in this study. Material reconstruction was next, including rearranging legal materials sequentially and logically. The last step was analyzing legal materials systematically based on the sequence of problems. National and global scale terrorism groups have well understood the consequences and legal actions of terror acts. The maximum, multiple layers of punishment have been stated in Law No. 15 of 2003 concerning the Eradication of Criminal Acts of Terrorism as an effort by the Indonesian government to suppress the movement of terrorism groups. The legal action process, especially in the Santoso’ Group, is based on Law no. 15 of 2003 with a sentence of 6 to 15 years in prison and a 6 (six) months subsidiary confinement with reference to the Theory of Retribution and Prevention.

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Spotting the Islamist Radical within: Religious Extremists Profiling in the United State

Cited by 14 publications

References 8 publications

Risk Matrix for Violent Radicalization: A Machine Learning Approach

Risk Matrix for Violent Radicalization: A Machine Learning Approach

Applying Machine Learning Techniques for Religious Extremism Detection on Online User Contents

Legal Actions of Terrorism Case in Central Sulawesi

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