Toward Approaches to Big Data Analysis for Terroristic Behavior Identification: Child Soldiers in Illegal Armed Groups During the Conflict in the Donbas Region (East Ukraine)

2017 ◽  
Vol 7 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Yuriy V. Kostyuchenko ◽  
Maxim Yuschenko
Keyword(s):  
Big Data ◽  
Armed Conflict ◽  
Child Soldiers ◽  
Big Data Analysis ◽  
Gender Composition ◽  
Stochastic Methods ◽  
Armed Groups ◽  
Conflict Zones ◽  
Analysis Of Dynamics

Paper aimed to consider of approaches to big data (social network content) utilization for understanding of social behavior in the conflict zones, and analysis of dynamics of illegal armed groups. Analysis directed to identify of underage militants. The probabilistic and stochastic methods of analysis and classification of number, composition and dynamics of illegal armed groups in active conflict areas are proposed. Data of armed conflict – antiterrorist operation in Donbas (Eastern Ukraine in the period 2014-2015) is used for analysis. The numerical distribution of age, gender composition, origin, social status and nationality of child militants among illegal armed groups has been calculated. Conclusions on the applicability of described method in criminological practice, as well as about the possibilities of interpretation of obtaining results in the context of study of terrorism are proposed.

Methods and Tools of Big Data Analysis for Terroristic Behavior Study and Threat Identification

2017 ◽  
pp. 52-66
Author(s):  
Yuriy V. Kostyuchenko ◽  
Maxim Yuschenko
Keyword(s):  
Big Data ◽  
Social Network ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Stochastic Methods ◽  
Armed Groups ◽  
Conflict Zones ◽  
Behavior Study ◽  
Analysis Of Dynamics

Paper aimed to consider of approaches to big data (social network content) utilization for understanding of social behavior in the conflict zones, and analysis of dynamics of illegal armed groups. Analysis directed to identify of underage militants. The probabilistic and stochastic methods of analysis and classification of number, composition and dynamics of illegal armed groups in active conflict areas are proposed. Data of armed conflict – antiterrorist operation in Donbas (Eastern Ukraine in the period 2014-2015) is used for analysis. The numerical distribution of age, gender composition, origin, social status and nationality of child militants among illegal armed groups has been calculated. Conclusions on the applicability of described method in criminological practice, as well as about the possibilities of interpretation of obtaining results in the context of study of terrorism are proposed.

Methods and Tools of Big Data Analysis for Terroristic Behavior Study and Threat Identification

2019 ◽  
pp. 525-537
Author(s):  
Yuriy V. Kostyuchenko ◽  
Maxim Yuschenko
Keyword(s):  
Big Data ◽  
Social Network ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Stochastic Methods ◽  
Armed Groups ◽  
Conflict Zones ◽  
Behavior Study ◽  
Analysis Of Dynamics

Paper aimed to consider of approaches to big data (social network content) utilization for understanding of social behavior in the conflict zones, and analysis of dynamics of illegal armed groups. Analysis directed to identify of underage militants. The probabilistic and stochastic methods of analysis and classification of number, composition and dynamics of illegal armed groups in active conflict areas are proposed. Data of armed conflict – antiterrorist operation in Donbas (Eastern Ukraine in the period 2014-2015) is used for analysis. The numerical distribution of age, gender composition, origin, social status and nationality of child militants among illegal armed groups has been calculated. Conclusions on the applicability of described method in criminological practice, as well as about the possibilities of interpretation of obtaining results in the context of study of terrorism are proposed.

Big Data Analysis for Terroristic Behavior Identification and Study Using Social Networks

2019 ◽  
pp. 197-235
Author(s):  
Yuriy V. Kostyuchenko ◽  
Victor Pushkar ◽  
Olga Malysheva ◽  
Maxim Yuschenko
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Stochastic Methods ◽  
Armed Groups ◽  
Conflict Zones ◽  
Analysis Of Dynamics

This chapter aimed to consider of approaches to big data (social network content) utilization for understanding social behavior in the conflict zones, and analysis of dynamics of illegal armed groups. The analysis directed to identify of structure of illegal armed groups, and detection of underage militants. The probabilistic and stochastic methods of analysis and classification of number, composition, and dynamics of illegal armed groups in active conflict areas are proposed. Data of armed conflict in Donbas (Eastern Ukraine) in the period 2014-2015 is used for analysis. The numerical distribution of age, gender composition, origin, social status, and nationality of militants among illegal armed groups has been calculated. Conclusions on the applicability of described method in criminological practice, as well as about the possibilities of interpretation of obtaining results in the context of study of terrorism are proposed.

Toward Approaches to Big Data Analysis for Terroristic Behavior Identification

2020 ◽  
pp. 1016-1028
Author(s):  
Yuriy V. Kostyuchenko ◽  
Maxim Yuschenko
Keyword(s):  
Big Data ◽  
Social Network ◽  
Data Analysis ◽  
Social Behavior ◽  
Big Data Analysis ◽  
Stochastic Methods ◽  
Armed Groups ◽  
Conflict Zones ◽  
Analysis Of Dynamics

Paper aimed to consider of approaches to big data (social network content) utilization for understanding of social behavior in the conflict zones, and analysis of dynamics of illegal armed groups. Analysis directed to identify of underage militants. The probabilistic and stochastic methods of analysis and classification of number, composition and dynamics of illegal armed groups in active conflict areas are proposed. Data of armed conflict – antiterrorist operation in Donbas (Eastern Ukraine in the period 2014-2015) is used for analysis. The numerical distribution of age, gender composition, origin, social status and nationality of child militants among illegal armed groups has been calculated. Conclusions on the applicability of described method in criminological practice, as well as about the possibilities of interpretation of obtaining results in the context of study of terrorism are proposed.

Interview with Mira Kusumarini

2019 ◽  
Vol 101 (911) ◽  
pp. 437-444
Keyword(s):  
Civil Society ◽  
Executive Director ◽  
Armed Conflict ◽  
Civil Society Organizations ◽  
Armed Groups ◽  
Conflict Zones ◽  
Violent Extremism ◽  
Peace And Security ◽  
Women And Children ◽  
Image Position

Even during armed conflict and other situations of violence, all children are entitled to their rights and protections as children without distinction based on their age, gender, religion, or whether they are associated with an armed group. Despite this, millions of children in conflict zones face discrimination, ostracization and stigmatization. This is particularly true for children affiliated with groups designated as “terrorist”, who face a range of challenges in reintegrating into society.Civil society can play an important role at the international, regional and domestic levels in helping children formerly associated with armed groups, or otherwise affected by armed conflict, to rejoin communities. Mira Kusumarini is a professional in the peace and security field in Indonesia who works to address the problems of women and children who have been associated with armed groups, and to help them reintegrate them into society. She is the Executive Director of the Coalition of Civil Society Against Violent Extremism (C-SAVE), a collaborative network of civil society organizations.In this interview, she discusses the challenges involved in the reintegration of children who have been associated with extremist groups in Indonesia and the stigma they face, as well as the importance of empathy in helping communities to heal.

Rebel Rule of Law and FARC Justice

2021 ◽  
pp. 29-100
Author(s):  
René Provost
Keyword(s):  
Rule Of Law ◽  
Armed Conflict ◽  
Armed Groups ◽  
Legal Concept ◽  
Conflict Zones ◽  
The Rule Of Law ◽  
Legal Norms ◽  
Exclusive Jurisdiction ◽  
Community Justice ◽  
Administration Of Justice

Chapter 1 considers the compatibility of the rebel administration of justice with the concept of the rule of law, using the FARC in Colombia as a case study. The Fuerzas Armadas Revolucionarias de Colombia—Ejército del Pueblo (FARC) was the largest non-state armed group during five decades of civil war in that country. At its peak, it wielded dominant territorial authority in more than half of Colombian municipalities. While it generally did not establish standing institutions to administer justice, it imposed legal norms, co-opted existing community justice mechanisms, and established informal and hybrid practices to settle legal disputes in the civil and criminal law fields. FARC justice practices are used to explore the concept of the rule of law, an essentially contested legal concept that cannot be exclusively attached to the modern state. The rule of law is shown to be a concept with a flexible content, modulated by circumstances such as the onset of armed conflict. Elements of a rebel rule of law adapted to the nature of non-state armed groups and context of armed conflict are articulated based on applicable international humanitarian and human rights law. Finally, the principle of state sovereignty is analysed to show that it does not impart exclusive jurisdiction to the state over the administration of justice, but instead can accommodate justice practices by a diversity of actors, including non-state armed groups in conflict zones.

scholarly journals TRANSITIONAL JUSTICE: THE REINTEGRATION OF CHILD SOLDIERS THROUGH POST-CONFLICT METHODS OF JUSTICE

2015 ◽  
Vol 20 (2) ◽  
pp. 474
Author(s):  
Ana Paula Barbosa-Fohrmann
Keyword(s):  
United Nations ◽  
International Law ◽  
Transitional Justice ◽  
Armed Conflict ◽  
Child Soldiers ◽  
Secretary General ◽  
Armed Conflicts ◽  
Armed Groups ◽  
Post Conflict ◽  
Made In

<p>This paper examines the problematic of child soldiers, based on inter alia the strategy of research <br />and study of the United Nations Office of the Special Representative of the Secretary-General for <br />Children and Armed Conflict and on the priorities of the Machel Study. Here, national and international <br />law will be applied on countries where children are recruited by armed groups. Concerning domestic <br />jurisdiction alternative or traditional methods of justice as well as formal legal methods will be <br />addressed. Specifically, this paper will focus on three main subjects: 1) the possibility of prosecution <br />and judgment of adolescents, who participated in armed conflicts; 2) prosecution and judgment of war lords <br />and 3) civil reparation proportional to the damage caused by an armed conflict. These three subjects will <br />be construed according to (traditional or alternative and formal) national and international law. Finally, <br />some recommendations will be made in order to improve the system of reintegration of child soldiers in <br />post-conflict countries.</p>

On Experience of Social Networks Exploration for Comparative Analysis of Narratives of Foreign Members of Armed Groups

2020 ◽  
pp. 1656-1671
Author(s):  
Yuriy V. Kostyuchenko ◽  
Maxim Yuschenko ◽  
Igor Artemenko
Keyword(s):  
Social Networks ◽  
Comparative Analysis ◽  
Information Dissemination ◽  
Stochastic Methods ◽  
Islamic State ◽  
Armed Groups ◽  
Traditional Media ◽  
Analysis Of Narratives

This article contains a comparison of narratives of foreign members of armed groups of The Islamic State of Iraq; the Levant; the Lugansk and Donetsk People's Republics in Syria and Ukraine. This data was collected in 2015-2016 using social networks and telecommunications which are presented in the article. The probabilistic and stochastic methods of analysis and classification of data from social networks were used for the identification of active members of illegal armed groups, and for analysis its number, composition and dynamics in active conflict areas. Some structural, figurative and lexical features of 83 stories are discussed. Key similarities, anomalies and differences are determined. Ways of information dissemination using social networks and traditional media are described. Role of narratives is shown. Conclusions are proposed.

Big data analysis with artificial intelligence technology based on machine learning algorithm

2020 ◽  
Vol 39 (5) ◽  
pp. 6733-6740
Author(s):  
Zeliang Zhang
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Statistical Data ◽  
Big Data Analysis ◽  
Data Set ◽  
Artificial Intelligence Technology ◽  
One To One ◽  
Multi Classification

Artificial intelligence technology has been applied very well in big data analysis such as data classification. In this paper, the application of the support vector machine (SVM) method from machine learning in the problem of multi-classification was analyzed. In order to improve the classification performance, an improved one-to-one SVM multi-classification method was creatively designed by combining SVM with the K-nearest neighbor (KNN) method. Then the method was tested using UCI public data set, Statlog statistical data set and actual data. The results showed that the overall classification accuracy of the one-to-many SVM, one-to-one SVM and improved one-to-one SVM were 72.5%, 77.25% and 91.5% respectively in the classification of UCI publication data set and Statlog statistical data set, and the total classification accuracy of the neural network, decision tree, basic one-to-one SVM, directed acyclic graph improved one-to-one SVM and fuzzy decision method improved one-to-one SVM and improved one-to-one SVM proposed in this study was 83.98%, 84.55%, 74.07%, 81.5%, 82.68% and 92.9% respectively in the classification of fault data of transformer, which demonstrated the improved one-to-one SVM had good reliability. This study provides some theoretical bases for the application of methods such as machine learning in big data analysis.

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