Dark Web - Exploring and Data Mining the Dark Side of the Web (2012).pdf

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Integrated Series in Information Systems
Volume 30
Series Editors
Ramesh Sharda
Oklahoma State University, Stillwater, OK, USA
Stefan Voß
University of Hamburg, Hamburg, Germany
For further volumes:
http://www.springer.com/series/6157
Hsinchun Chen
Dark Web
Exploring and Data Mining
the Dark Side of the Web
Hsinchun Chen
Department of Management Information Systems
University of Arizona
Tuscon, AZ, USA
hchen@eller.arizona.edu
ISSN 1571-0270
ISBN 978-1-4614-1556-5
e-ISBN 978-1-4614-1557-2
DOI 10.1007/978-1-4614-1557-2
Springer New York Dordrecht Heidelberg London
Library of Congress Control Number: 2011941611
© Springer Science+Business Media, LLC 2012
All rights reserved. This work may not be translated or copied in whole or in part without the written
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Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Aims
The University of Arizona Artificial Intelligence Lab (AI Lab) Dark Web project
is a long-term scientific research program that aims to study and understand the
international terrorism (jihadist) phenomena via a computational, data-centric
approach. We aim to collect “ALL” web content generated by international terrorist
groups, including web sites, forums, chat rooms, blogs, social networking sites,
videos, virtual world, etc. We have developed various multilingual data mining, text
mining, and web mining techniques to perform link analysis, content analysis,web
metrics (technical sophistication) analysis, sentiment analysis, authorship analysis,
and video analysis in our research. The approaches and methods developed in this
project contribute to advancing the field of Intelligence and Security Informatics
(ISI). Such advances will help related stakeholders perform terrorism research and
facilitate international security and peace.
Dark Web research has been featured in many national, international and local
press and media, including: National Science Foundation press, Associated Press,
BBC, Fox News, National Public Radio, Science News, Discover Magazine,
Information Outlook, Wired Magazine, The Bulletin (Australian), Australian
Broadcasting Corporation, Arizona Daily Star, East Valley Tribune, Phoenix ABC
Channel 15, and Tucson Channels 4, 6, and 9. As an NSF-funded research project,
our research team has generated significant findings and publications in major com-
puter science and information systems journals and conferences. We hope our
research will help educate the next generation of cyber/Internet-savvy analysts and
agents in the intelligence, justice, and defense communities.
This monograph aims to provide an overview of the Dark Web landscape, sug-
gest a systematic, computational approach to understanding the problems, and illus-
trate research progress with selected techniques, methods, and case studies developed
by the University of Arizona AI Lab Dark Web team members.
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