Social network analysis as a tool for criminal intelligence: understanding its potential from the perspectives of intelligence analysts

被引:25
|
作者
Burcher, Morgan [1 ]
Whelan, Chad [1 ]
机构
[1] Deakin Univ, Sch Humanities & Social Sci, Geelong, Vic, Australia
关键词
Social network analysis; Criminal networks; Criminal network characteristics; Dark networks; Law enforcement; Law enforcement organisational characteristics; DARK NETWORKS; EVOLUTION; SNA;
D O I
10.1007/s12117-017-9313-8
中图分类号
DF [法律]; D9 [法律];
学科分类号
0301 ;
摘要
Over the past two decades an increasing number of researchers have applied social network analysis (SNA) to various 'dark' networks. This research suggests that SNA is capable of revealing significant insights into the dynamics of dark networks, particularly the identification of critical nodes, which can then be targeted by law enforcement and security agencies for disruption. However, there has so far been very little research into whether and how law enforcement agencies can actually leverage SNA in an operational environment and in particular the challenges agencies face when attempting to apply various network analysis techniques to criminal networks. This paper goes some way towards addressing these issues by drawing on qualitative interviews with criminal intelligence analysts from two Australian state law enforcement agencies. The primary contribution of this paper is to call attention to the organisational characteristics of law enforcement agencies which, we argue, can influence the capacity of criminal intelligence analysts to successfully apply SNA as much as the often citied 'characteristics of criminal networks'.
引用
收藏
页码:278 / 294
页数:17
相关论文
共 50 条
  • [41] Defining artificial intelligence as a policy problem: A discourse network analysis from Germany
    Lemke, Nicole
    Trein, Philipp
    Varone, Frederic
    EUROPEAN POLICY ANALYSIS, 2024, 10 (02) : 162 - 187
  • [42] Global Collaboration in Artificial Intelligence:Bibliometrics and Network Analysis from 1985 to 2019
    Haotian Hu
    Dongbo Wang
    Sanhong Deng
    Journal of Data and Information Science, 2020, 5 (04) : 86 - 115
  • [43] Impact of enterprise artificial intelligence on social responsibility: Evidence from text analysis
    Yang, Ying
    An, Ran
    Song, Jie
    FINANCE RESEARCH LETTERS, 2025, 75
  • [44] Artificial Intelligence Model for the Identification of the Personality of Twitter Users through the Analysis of Their Behavior in the Social Network
    Villegas-Ch, William
    Mauricio Erazo, Daniel
    Ortiz-Garces, Ivan
    Gaibor-Naranjo, Walter
    Palacios-Pacheco, Xavier
    ELECTRONICS, 2022, 11 (22)
  • [45] Study Platform for Complex Data Analysis of Telecommunications and Social Network Applications Using Business Intelligence
    Radescu, Radu
    Muraru, Valentin
    NEW TECHNOLOGIES AND REDESIGNING LEARNING SPACES, VOL I, 2019, : 358 - 365
  • [46] Hypex: A Tool for Extracting Business Intelligence from Sentiment Analysis using Enhanced LSTM
    Sreesurya, Ilayaraja
    Rathi, Himani
    Jain, Pooja
    Jain, Tapan Kumar
    MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 79 (47-48) : 35641 - 35663
  • [47] Can Social Network Analysis be Effective at Improving the Intelligence Community While Ensuring Civil Rights?
    Gumm, Kenneth Earl, Jr.
    INFORMATION SECURITY JOURNAL, 2012, 21 (03): : 115 - 126
  • [48] Analysis of the Links between Social Intelligence and Coping Strategies of Business Managers in Terms of Development of Their Potential
    Zbihlejova, Lucia
    Birknerova, Zuzana
    SOCIETIES, 2022, 12 (06):
  • [49] ANALYSIS OF THE RELATIONSHIP BETWEEN ARTIFICIAL INTELLIGENCE AND ART FROM THE PERSPECTIVE OF SOCIAL PSYCHOLOGY AND ITS IMPACT ON STUDENTS' PSYCHOLOGICAL EMOTIONS
    Jin, Tianxing
    PSYCHIATRIA DANUBINA, 2022, 34 : S782 - S783
  • [50] Social network analysis as a tool for understanding the diffusion of GIS innovations: the Greek GIS community
    Assimakopoulos, DG
    ENVIRONMENT AND PLANNING B-PLANNING & DESIGN, 2000, 27 (04): : 627 - 640