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This book shows how open source intelligence can be a powerful tool
for combating crime by linking local and global patterns to help
understand how criminal activities are connected. Readers will
encounter the latest advances in cutting-edge data mining, machine
learning and predictive analytics combined with natural language
processing and social network analysis to detect, disrupt, and
neutralize cyber and physical threats. Chapters contain
state-of-the-art social media analytics and open source
intelligence research trends. This multidisciplinary volume will
appeal to students, researchers, and professionals working in the
fields of open source intelligence, cyber crime and social network
analytics. Chapter Automated Text Analysis for Intelligence
Purposes: A Psychological Operations Case Study is available open
access under a Creative Commons Attribution 4.0 International
License via link.springer.com.
This book focuses on applications of social network analysis in
predictive policing. Data science is used to identify potential
criminal activity by analyzing the relationships between offenders
to fully understand criminal collaboration patterns. Co-offending
networks-networks of offenders who have committed crimes
together-have long been recognized by law enforcement and
intelligence agencies as a major factor in the design of crime
prevention and intervention strategies. Despite the importance of
co-offending network analysis for public safety, computational
methods for analyzing large-scale criminal networks are rather
premature. This book extensively and systematically studies
co-offending network analysis as effective tool for predictive
policing. The formal representation of criminological concepts
presented here allow computer scientists to think about algorithmic
and computational solutions to problems long discussed in the
criminology literature. For each of the studied problems, we start
with well-founded concepts and theories in criminology, then
propose a computational method and finally provide a thorough
experimental evaluation, along with a discussion of the results. In
this way, the reader will be able to study the complete process of
solving real-world multidisciplinary problems.
This book focuses on applications of social network analysis in
predictive policing. Data science is used to identify potential
criminal activity by analyzing the relationships between offenders
to fully understand criminal collaboration patterns. Co-offending
networks-networks of offenders who have committed crimes
together-have long been recognized by law enforcement and
intelligence agencies as a major factor in the design of crime
prevention and intervention strategies. Despite the importance of
co-offending network analysis for public safety, computational
methods for analyzing large-scale criminal networks are rather
premature. This book extensively and systematically studies
co-offending network analysis as effective tool for predictive
policing. The formal representation of criminological concepts
presented here allow computer scientists to think about algorithmic
and computational solutions to problems long discussed in the
criminology literature. For each of the studied problems, we start
with well-founded concepts and theories in criminology, then
propose a computational method and finally provide a thorough
experimental evaluation, along with a discussion of the results. In
this way, the reader will be able to study the complete process of
solving real-world multidisciplinary problems.
This book shows how open source intelligence can be a powerful tool
for combating crime by linking local and global patterns to help
understand how criminal activities are connected. Readers will
encounter the latest advances in cutting-edge data mining, machine
learning and predictive analytics combined with natural language
processing and social network analysis to detect, disrupt, and
neutralize cyber and physical threats. Chapters contain
state-of-the-art social media analytics and open source
intelligence research trends. This multidisciplinary volume will
appeal to students, researchers, and professionals working in the
fields of open source intelligence, cyber crime and social network
analytics. Chapter Automated Text Analysis for Intelligence
Purposes: A Psychological Operations Case Study is available open
access under a Creative Commons Attribution 4.0 International
License via link.springer.com.
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