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Digital Open Source Intelligence and International Security: A Primer

Authors:

Abstract

The traditional understanding of intelligence is the methodical collection of high-value information in a way that yields comparative advantage to decision makers.2 Such information can be on a foreign country’s capabilities, general global events, or a country’s domestic affairs. While most people tend to equate intelligence with military or security affairs, this is a very narrow definition that omits the value of intelligence in trade, finance, culture and educational affairs to render longer-term advantage during peace time. Although this traditional definition of intelligence didn’t become obsolete, it was broadened through the advances in technology and more importantly, through the wide availability of such technology to wider audiences.3 Through history, mastery of intelligence required mastery of both technology and the study of human behavior, both of which eventually rendered intelligence as a force multiplier of other functions (military, political, economic). In addition to its traditional function of enabling less miscalculated decisions, the audience of modern intelligence is growing beyond state or corporation leadership, and is expanding to the public. It is no longer a mere warning mechanism, but also a know-how reservoir and improvisation pool to resolve matters in times of unexpected crises.
Digital Open Source Intelligence and
International Security: A Primer
H. Akın Ünver | EDAM, Oxford CTGA & Kadir Has University
Cyber Governance and Digital Democracy 2018/8
July 2018
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Cyber Governance and Digital Democracy 2018/8
Digital Open Source Intelligence and
International Security: A Primer
H. Akın Ünver | EDAM, Oxford CTGA & Kadir Has Üniversitesi
Intelligence is a key and continually changing practice
of statecraft. While this practice has historically been
dominated by the states, merchants, and the clergy, late-20th
century has witnessed the privatization of intelligence and
surveillance equipment and broadening of the concept of
intelligence. Today, Internet, social media, smartphones and
data analytics have all contributed to the greater exposure
and dissemination of critical information about emergencies
and crisis events, thereby contributing to the faster travelling
of news, secrets and leaks. Broadly speaking, intelligence is
the practice of methodical collection and analysis of critical
information for the purposes of security, or advantage.
Although used synonymously with espionage, or covert
operations, intelligence is mostly focused on the methodical
collection, processing and analysis of information that is
available and ‘out there’, rather than using clandestine
methods to gain such information through stealing. This drive
towards the collection of more and better information has
been the founding block of national security, well-evidenced
in successive political treatises of statecraft, since the oft-
quoted 13th chapter of the Sun Tzu’s ‘Art of War’ - The Use of
Spies: ‘Thus, what enables the wise sovereign and the good
general to strike and conquer, and achieve things beyond
the reach of ordinary men, is foreknowledge.1
The traditional understanding of intelligence is the
methodical collection of high-value information in a way
that yields comparative advantage to decision makers.2
Such information can be on a foreign country’s capabilities,
general global events, or a country’s domestic affairs.
While most people tend to equate intelligence with military
or security affairs, this is a very narrow denition that omits
the value of intelligence in trade, nance, culture and
educational affairs to render longer-term advantage during
peace time. Although this traditional denition of intelligence
didn’t become obsolete, it was broadened through the
advances in technology and more importantly, through the
wide availability of such technology to wider audiences.3
Through history, mastery of intelligence required mastery of
both technology and the study of human behavior, both of
which eventually rendered intelligence as a force multiplier
of other functions (military, political, economic). In addition
to its traditional function of enabling less miscalculated
decisions, the audience of modern intelligence is growing
beyond state or corporation leadership, and is expanding
to the public. It is no longer a mere warning mechanism, but
also a know-how reservoir and improvisation pool to resolve
matters in times of unexpected crises.4
Sun Tzu, The Art Of War (Sterling Publishers Pvt. Ltd, 2005), 92
Loch K. Johnson (ed.), The Oxford Handbook of National Security Intelligence (Oxford University Press, 2010), 4.
Johnson, 229.
Robert Dover, Michael S. Goodman, and Claudia Hillebrand, eds., Routledge Companion to Intelligence Studies (Routledge, 2013), 51
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Cyber Governance and Digital Democracy 2018/8
Despite being one of the most exciting elds of inquiry in
diplomacy, security and politics, the study of intelligence
has consistently been difcult due to the secretive
nature of the practice. Methodical information collection,
establishment and maintenance of collection networks and
a reliable ‘information pipeline’ have been some of the most
crucial areas of security, without a matching scientic and
scholarly rigor.5 This was mostly due to the unavailability
of historical intelligence records, or study data beyond
a narrow intelligence community. However, the eld has
gradually opened to civilian scholarly expertise mainly in
the United States, towards the end of the Cold War. This
owed largely to the 1980s declassication of World War 2
intelligence les in the US and the UK, the most signicant
of which belonged to the Ofce of Strategic Services (OSS)
and British signals intelligence les.6 Previously only able to
work with a small collection of cleared documents, civilian
intelligence scholars now had a far larger data pool to
work with. With this data availability came some of the rst
theories on the changing function of intelligence in national
security and how it could adapt to changing technologies
and communication methods.
Broadly speaking, intelligence implies four main processes.
The rst is collection; primarily, a state’s capacity to reach,
sort and collect meaningful, high-value information related
to security and/or comparative policy advantage. While
historically, intelligence collection capacity overwhelmingly
required a wide human reach and physical access
network, with 20th century, it also began to heavily include
technological capacity and continuous adaptation to
technical advances in communication and informatics.
The second process is transmission, which involves the
establishment and diversication of reliable channels of
critical information ows from the target area, back to the
intelligence core and from there, across domestic security
institutions. Intelligence transmission requires both a highly
qualied human trust network that forms an information
extraction and delivery chain from the ground to the agency,
as well as digital transmission structures that enable a fast
delivery of digital intelligence. In intelligence types that deal
with digital data - imagery, audio, text - transmission requires
high levels of encryption and decryption to secure storage
and transfers of such data. Third is awareness, which implies
the intelligence community’s understanding of the decision-
makers’ needs and decision-makers’ understanding of
the value of intelligence in key decision environments. In
organizational cultures where the priorities of the intelligence
community and the decision-making cohort are mismatched,
or the political leadership doesn’t trust the intelligence
community, the awareness component is jeopardized,
preventing efcient processing and transmission of key
intelligence in crisis scenarios. Finally, agencies have to
have the ability of ‘selective deception’, where it can reliably
mislead competitors into wrong or missing information. This
is necessary to retain comparative advantage against other
intelligence competitors, by consistently distracting them
into wasting resources and time on the ground.7
Intelligence also varies across cultures, since countries
have different threat perception, information seeking and
secrecy processing dynamics. To that end, intelligence
should not be thought of as a monolithic and standard
practice; rather, there are politically and culturally contingent
ways of maximising decision-making advantage using a
multitude of information gathering mechanisms. A primary
determinant of intelligence culture is regime type,8 where
democracies, hybrid states and authoritarian governments
process and manage information through different
bureaucratic mechanisms, as well as legal and legislative
oversight mechanisms.9 In addition, democratic intelligence
services tend to have greater autonomy compared to those
of authoritarian states, and also tend to have a more merit-
based recruitment and promotion scheme, allowing such
agencies to act with greater legitimacy and a more diverse
skillset against a multitude of threats. Strong oversight
mechanisms also tend to minimize corruption, resource
waste and mismanagement – allowing democratically-
checked intelligence agencies to enjoy greater political
legitimacy domestically.10 Furthermore, authoritarian
Dover, Goodman, and Hillebrand, 71.
Dover, Goodman, and Hillebrand, 88.
Dover, Goodman, and Hillebrand, 71–83; Johnson, The Oxford Handbook of National Security Intelligence, 113–19.
Montgomery McFate, “The Military Utility of Understanding Adversary Culture” (Arlington, VA: DTIC, Ofce of Naval Research, January 2005),
http://www.dtic.mil/docs/citations/ADA479862.
Philip H. J. Davies, “Intelligence Culture and Intelligence Failure in Britain and the United States,” Cambridge Review of International Affairs 17, no. 3 (October 1, 2004):
495–520, https://doi.org/10.1080/0955757042000298188.
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Mikael Wigell, “Mapping ‘Hybrid Regimes’: Regime Types and Concepts in Comparative Politics,” Democratization 15, no. 2 (April 1, 2008): 230–50,
https://doi.org/10.1080/13510340701846319.
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Cyber Governance and Digital Democracy 2018/8
states tend to inate domestic and foreign threats, forcing
wasteful intelligence agencies to spread too thin across
multiple, obscure information fronts. Another determinant is
institutional history and culture.11 The intelligence practices
and territorial awareness of post-imperial states (i.e. states
that were once at the core of a former empire) and those that
aren’t, are markedly different. Inheriting a longer tradition
of intelligence, such post-imperial states tend to operate
across a wider territorial space, usually in the current states
of their former imperial territories.12 Finally, proximity to active
conict is crucial. States that are ghting, or adjacent to an
active ongoing domestic conict, operate on a different
institutional culture compared to states that don’t. Most
organizational and bureaucratic models of intelligence differ
according to the country’s engagement with active or frozen
conicts, and/or participation in foreign peace operations.
HUMINT (human intelligence): As the oldest (and up until
late-19th century, the only) school in intelligence, HUMINT
makes up the bulk of intelligence in history. Roughly, it relies
on verbal and non-verbal communicative relations, networks
and interactions between, or concerning individuals
of political, military, economic or cultural importance.
Psychology, cognitive mapping, sociology, anthropology
and humanities are some of the key tools of the HUMINT
community to understand, extract and contextualize critical
security events and processes in foreign countries. Not only
ambassadors, military attaches or state ofcials, but also
traders, tourists and students have also served as a cultural
and national exchange points of HUMINT throughout
history. HUMINT is also by no means at the monopoly of
states. Private companies, banks, research laboratories and
technology rms too, engage in regular HUMINT operations
(covert or overt) to achieve nancial or scientic/technical
advantage against their rivals.13
GEOINT (geospatial intelligence): Although aspects
of geography (weather, terrain, waterways) have always
been important variables in intelligence analysis, GEOINT
has specically beneted from the advent of real-time
(or close enough) aerial imagery provided by satellites,
unmanned aerial vehicles (UAVs), light detection and
ranging (LIDAR) and surveillance aircraft. GEOINT provides
static, or time-frequency image analysis to track and monitor
human activity on a selected geographical area, as well as
resources and sub-terrain conditions. Although geospatial
data was previously at the intersection of MASINT and
SIGINT, the availability of dedicated geospatial tools has led
to the creation of the National Geospatial Agency (NGA).
Today, commercially available high-resolution imagery
provided by companies such as Planet Labs, Terra Bella,
BlackSky Global, Orthecast or XpressSAR, have all enabled
businesses, aid agencies, and a range of non-state actors
to acquire GEOINT capabilities.14
MASINT (measurement and signature intelligence):
An umbrella term for a wide array of high-technology
detection tools to measure acoustic, radio frequency,
radiation, chemical/biological, spectroscopic and infrared
signature, MASINT is focused on collecting metric,
angular, spatial and modular data through remote-sensing
methods. Prior to 1991, most MASINT systems contained
embedded templates and libraries of signatures to help
human-assisted automated detection. Today, with the help
of articial intelligence, machine learning and big data
libraries of signature detection, most MASINT systems have
grown autonomous to conduct live surveillance without the
Intelligence disciplines are roughly divided into
six primary schools:
Jessica L. Weeks, “Autocratic Audience Costs: Regime Type and Signaling Resolve,” International Organization 62, no. 1 (January 2008): 35–64,
https://doi.org/10.1017/S0020818308080028.
Jeffrey W. Legro, “Culture and Preferences in the International Cooperation Two-Step,” American Political Science Review 90, no. 1 (March 1996): 118–37,
https://doi.org/10.2307/2082802.
Jacqueline R. Evans et al., “Criminal versus HUMINT Interrogations: The Importance of Psychological Science to Improving Interrogative Practice,” The Journal of Psychiatry
& Law 38, no. 1–2 (March 1, 2010): 215–49, https://doi.org/10.1177/009318531003800110; Montgomery McFate and Steve Fondacaro, “Cultural Knowledge and Common
Sense,” Anthropology Today 24, no. 1 (February 1, 2008): 27–27, https://doi.org/10.1111/j.1467-8322.2008.00562.x.
Todd S. Bacastow and Dennis Bellaore, “Redening Geospatial Intelligence,” American Intelligence Journal 27, no. 1 (2009): 38–40; Andy Sanchez, “Leveraging Geospatial
Intelligence (GEOINT) in Mission Command” (Arlington, VA: DTIC, Ofce of Naval Research, March 21, 2009), http://www.dtic.mil/docs/citations/ADA506270.
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Cyber Governance and Digital Democracy 2018/8
assistance of a human operator. Today, MASINT can be
used in a wide array of information environments, from the
detection of missiles, aircraft, or drones, to disaster relief,
refugee aid monitoring, and natural resource - industrial
output measurement.15
FININT (nancial intelligence): With its professional
motto ‘follow the money’, FININT is the discipline of tracking
nancial transactions to infer adversaries’ capabilities,
intentions and networks. Focusing on terrorist nancing,
tax evasion and money laundering, or arms trade, FININT is
primarily interested in how adversaries fund their operations
and assets, as well as mapping the intermediary institutions
and/or persons involved in these operations. FININT is one
of the most diverse schools of discipline, serving multiple
branches of a government, and also one that isn’t necessarily
tied to security or crisis decision-making. Longer term trends
that don’t require a response under time or information
constraints, and can be accessed through open sources,
such as economic growth, industrial production, accounting
policy and econometric data, are under the jurisdiction of
FININT.16
SIGINT (signals intelligence): Although smoke,
pigeons, light or semaphore signals were used as long-rage
communication tools in history, the emergence of SIGINT
owes mainly to the invention of telegraphy. Going as far
back to 1850s as a dedicated intelligence discipline, SIGINT
is primarily concerned about intercepting and processing
an adversary’s messages transmitted over a distance, as
well as encrypting friendly communications so that they
don’t get intercepted by rivals. This includes tapping
into communication networks and signal transmission
channels for the purpose of intercepting enemy electronic
communications, along with cryptographic work to
handle the encryption and decryption of messages. As
communication technologies have rapidly evolved through
the 20th century, SIGINT has also expanded its capabilities
to include TECHINT (technical intelligence), CYBINT (cyber-
intelligence), and DNINT (digital network intelligence).
Today, the information that lies in the vast span of the
Internet, social media platforms and Internet Communication
Technologies, ICTs are also under the jurisdiction of SIGINT.
It is also at the forefront of current Internet-based information
wars, including bots, trolls, digital spoilers and fake news.17
OSINT (open-source intelligence): Although an
intelligence agency’s capacity is primarily measured by how
well it can detect and transmit critical information, its ability to
understand and contextualize what is important requires the
foreknowledge of what is ‘out there’ and easily available. To
distinguish between important and redundant information,
an agency must rst lay the groundwork for its ‘information
environment’. This in turn, has to be done through developing
institutional and organizational skills to cultivate and harvest
information that is ‘legally available in the public domain’, or
intelligence that is ‘hidden in plain sight’. Although historically,
OSINT has been driven by news and information agencies,
cultural and diplomatic exchanges and socialization, it is
increasingly being driven by Internet and ICT-based based
technological developments. To that end, classical OSINT
and digital OSINT has to be differentiated.18
Jeffrey T. Richelson, “MASINT: The New Kid in Town,” International Journal of Intelligence and CounterIntelligence 14, no. 2 (April 1, 2001): 149–92,
https://doi.org/10.1080/088506001300063136; J. Dudczyk, J. Matuszewski, and M. Wnuk, “Applying the Radiated Emission to the Specic Emitter Identication,” in
15th International Conference on Microwaves, Radar and Wireless Communications (IEEE Cat. No.04EX824), vol. 2, 2004, 431–434 Vol.2, https://doi.org/10.1109/
MIKON.2004.1357058.
Donato Masciandaro, “Financial Supervisory Unication and Financial Intelligence Units,” Journal of Money Laundering Control 8, no. 4 (October 1, 2005): 354–70,
https://doi.org/10.1108/13685200510620858; John Frank Thony, “Processing Financial Information in Money Laundering Matters: The Financial Intelligence Units,” European
Journal of Crime, Criminal Law and Criminal Justice 4 (1996): 257.
Matthew M. Aid, “All Glory Is Fleeting: Sigint and the Fight Against International Terrorism,” Intelligence and National Security 18, no. 4 (December 1, 2003): 72–120,
https://doi.org/10.1080/02684520310001688880; Martin Rudner, “Britain Betwixt and Between: Uk SIGINT Alliance Strategy’s Transatlantic and European Connections,”
Intelligence and National Security 19, no. 4 (December 1, 2004): 571–609, https://doi.org/10.1080/0268452042000327528.
Michael Glassman and Min Ju Kang, “Intelligence in the Internet Age: The Emergence and Evolution of Open Source Intelligence (OSINT),” Computers in Human Behavior 28,
no. 2 (March 1, 2012): 673–82, https://doi.org/10.1016/j.chb.2011.11.014; Robert W. Pringle, “The Limits of OSINT: Diagnosing the Soviet Media, 1985-1989,” International
Journal of Intelligence and CounterIntelligence 16, no. 2 (April 1, 2003): 280–89, https://doi.org/10.1080/08850600390198706.
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Cyber Governance and Digital Democracy 2018/8
In the words of Allen Dulles, ‘A proper analysis of the
intelligence obtainable by these overt, normal and
aboveboard means would supply us with over 80 percent, I
should estimate, of the information required for the guidance
of our national policy’.19 Indeed, Dulles emphasizes that
Because of its glamour and mystery, overemphasis is
generally placed on what is called secret intelligence’,20
whereas the bulk of intelligence collection and processing
is usually done through ‘normal methods’ such as explicit
diplomatic interaction, personal relationships, radio, press
and a country’s Diaspora abroad. The same ‘80% rule’ is
also laid down in NATO 2002 and Hulnick 2004, although for
EUROPOL (European Union Agency for Law Enforcement
Cooperation), the British, Swedish and Dutch ministries
of defense, as well as DIA (Defense Intelligence Agency)
OSINT constitutes ‘at least 90%’ of all intelligence activities.21
This means that rather than the popularized and mystied
practice of espionage and spying, the overwhelming majority
of intelligence activities focus exclusively on harvesting
open sources and nding connections and nuances where
others can’t.
OSINT determines the relevance and groundwork of an
agency’s wider functions. To that end, a proper conduct
of OSINT provides two key advantages to an agency. The
rst is context: namely, the spectrum of events, actors
and roles that determine strategic relativity (i.e. how to
dene a country’s interests in relation to ongoing events),
as well as which assets to deploy to achieve them. Without
an understanding of world events, causal mechanisms
between processes and explicit interests of major actors,
agencies can only deal with problems reactively, without
any ability to stop or manage them before they reach the
nation’s borders; or worse, off beyond them.22 Second,
OSINT renders other intelligence functions efcient by
giving an agency an accurate understanding of what types
of information are available and which ones aren’t and
needs dedicated focus to extract. This way, agencies can
use other functions (especially more aggressive extraction
mechanisms such as espionage or stealing) more sparingly,
reducing the likelihood of miscalculation and escalation of
tensions with another country. OSINT also decreases the
costs of other intelligence functions by eliminating much of
the guesswork.23
OSINT grew more important in inuence and impact with the
advances in communication and encryption technologies.
The invention of the alphabet and diplomatic writing brought
about the need for seals and cipher mechanisms; printing
press, for ofciation and modern bureaucracy; telegraph,
for code-makers and code-breakers; radio, for signals
interceptors (SIGINT) and computers, for high-volume
encryption and decryption. The advent of the Internet, digital
interconnectedness and social media platforms have all led
to the growing importance of OSINT and the emergence of
overlapping jurisdictional areas between other schools of
intelligence, but also brought about problems of verication
regarding content and news. The explosion of information
and data has made life both easier and more difcult for
OSINT; easier, because of the widening of the channels of
communication, and hard because of a similar proliferation
of junk, or misleading information. This renders OSINT’s
task not just collection and processing of digital data, but
also developing verication and attribution mechanisms,
and understanding what constitutes as junk content and
what doesn’t. In order for agencies to know which digital
information or data type is important, they need technical
infrastructure and high-quality manpower (or ability to
outsource all of these functions) to grasp the Internet and
its ever-changing patterns of dissemination and storage.
To that end, most digital OSINT agencies have started to
develop Internet studies units.24 Furthermore, agencies
not only have to compete among themselves as they
historically did, but thanks to the democratization and wide
availability of Internet sources to the mass public, they also
have to compete with citizen analysts and private OSINT
Digital OSINT
Dover, Goodman, and Hillebrand, Routledge Companion to Intelligence Studies, 125.
Dover, Goodman, and Hillebrand, 125.
Johnson, The Oxford Handbook of National Security Intelligence, 221.
Johnson, 45.
Dover, Goodman, and Hillebrand, Routledge Companion to Intelligence Studies, 14.
Edward J. Appel, Cybervetting: Internet Searches for Vetting, Investigations, and Open-Source Intelligence, Second Edition (CRC Press, 2014), 157.
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Cyber Governance and Digital Democracy 2018/8
companies. These two new emerging intelligence actors –
citizens and private analysts – are unbound by the heavy
bureaucratic weight of formal intelligence agencies, and
thus, can adapt to changing technicalities faster and can
undertake collection, storage and analysis functions on their
own initiative, for which agencies require degrees of legal
legitimacy and formal authority. From counterterrorism to
cybersecurity, and from WMD monitoring to protest analysis,
technology companies and civilians alike tap into the same
data and information types that most state OSINT agencies
do. Although non-state analysts lack in nancial resources
of states, they make up for this shortcoming through their
autonomy, speed and improvisation ability.
On top of this widening, add in the popularized variable -
‘Big Data’. There are two main novelties brought about by
the oft-prophesized ‘Big Data Revolution’: rst, data storage
and transmission technologies, the availability of 3G/4G
data networks, mass proliferation of Wi-Fi access and cloud
technologies, we are now able to produce, store and share
historically unprecedented volumes of information. This both
makes a given unit of data (byte) increasingly cheaper to
produce, store or transmit, and also enable highly-granular
social (especially personal) data to be produced and
harvested. Eventually, our social and personal data has
become multi-purpose; our tax and employment data for
example, can be used to prole our purchasing behaviour,
healthcare options, residency choice and electoral
behaviour.25 This multi-purpose social and personal data
then gets even more granular through our digital behavior,
in the form of Facebook friends, likes, Twitter retweets,
Instagram posts, geo-located photo uploads and Snapchat
videos. This allows both state and private OSINT analysts
to tap into the largest, continuously-growing and extremely
detailed behavioural information pool of millions of people.
Finally, when considering the proliferation of ‘Internet of
things’ (IOT) data types, from tness watches to home
appliances, this largest ever pool of social and personal data
becomes enormous, yet detailed enough to prole nations in
high-denition.26 For any analyst – state or private – working
on public morale, political interests, electoral choice and
social forces in an adversary’s society, such proliferation of
data is a historically signicant turn in intelligence capacity.
Yet, not all states can harvest such data efciently. For such
data to be meaningfully distilled into valuable intelligence,
an analyst has to possess a diverse set of competencies
including computer science and data science, which is
where states usually fail to catch up.
The rst of many problems for state agencies is the issue of
talent attraction. With Facebook, Google, Amazon and other
tech companies enabling a vastly freer working environment,
few (visible) hierarchies and better pay, most of the highly-
qualied data analysts turn away from state service.27 This
generates a shift in the centre of gravity of digital intelligence
power, from states to private companies. Second is the
issue of infrastructure development, adaptation and
upgrading which is problematic for the highly bureaucratic
structure of the states. New hardware is always expensive
and smart solutions like technology recycling (refurbishing
old equipment at lower costs) or upgrade streamlining
require smaller quantities and a nimbler decision-making
system. The very business model of technology renders
states as the trailers behind (and dependent) on technology
companies.28 Third, the growing civilianization of OSINT has
created an ‘information-as-resistance’ movement in which
digital activism implies the exposure and dissemination
of state mismanagement, corruption and repression.29
This resistance culture assumed an increasingly better-
organized digital identity following with the exposure of
state surveillance abuses with the Snowden revelations,
Wikileaks and Chelsea Manning exposures. Although states
can theoretically tap into this civilian OSINT pool, the current
culture and identity of this community is mostly anti-state.30
Westin Alan F., “Social and Political Dimensions of Privacy,” Journal of Social Issues 59, no. 2 (April 29, 2003): 431–53, https://doi.org/10.1111/1540-4560.00072.
Feng Chen et al., “Data Mining for the Internet of Things: Literature Review and Challenges,” International Journal of Distributed Sensor Networks 11, no. 8 (August 18, 2015):
431047, https://doi.org/10.1155/2015/431047.
Valerio De Stefano, “The Rise of the Just-in-Time Workforce: On-Demand Work, Crowdwork, and Labor Protection in the Gig-Economy,” Comparative Labor Law & Policy
Journal 37 (2016 2015): 471.
Stefan Tongur and Mats Engwall, “The Business Model Dilemma of Technology Shifts,” Technovation 34, no. 9 (September 1, 2014): 525–35,
https://doi.org/10.1016/j.technovation.2014.02.006.
25
26
27
28
Moonsun Choi, Michael Glassman, and Dean Cristol, “What It Means to Be a Citizen in the Internet Age: Development of a Reliable and Valid Digital Citizenship Scale,”
Computers & Education 107 (April 1, 2017): 100–112, https://doi.org/10.1016/j.compedu.2017.01.002.
29
Zeynep Tufekci, Twitter and Tear Gas: The Power and Fragility of Networked Protest (New Haven ; London: Yale University Press, 2017).
30
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Cyber Governance and Digital Democracy 2018/8
Finally, states can potentially get hurt by OSINT, as much
as they benet from it, as OSINT is by nature, a double
edged sword. A state can suffer from audience costs and
public shaming from the exposure of its mismanagement
and corruption, just as it tries to tap into the OSINT realm to
hurt other states, or domestic opposition groups. Although
civilian data leaks (voter, healthcare, purchasing history
data etc.) hurt individuals, state-level data leaks hurt
governments and agencies more, due to the secretive nature
of most leaks.31 This renders states larger sitting ducks in
digital power parity compared to civilians (unless targeted
specically) and alters the relative power balance between
the state and the society. This shift generates a security
dilemma between state actors as well, as this renewed state-
society power balance enables external actors to exploit
and interfere with the domestic machinations of a nation.
This interference can hurt powerful and weak states alike,
the best example being the Russian involvement in the US
elections via fake news and other publicly available news
and information sources.
S. Landau, “Making Sense from Snowden: What’s Signicant in the NSA Surveillance Revelations,” IEEE Security Privacy 11, no. 4 (July 2013): 54–63,
https://doi.org/10.1109/MSP.2013.90.
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Although OSINT tools are rapidly evolving, most popular methods can be clustered under four main categories: linguistic/
text-based methods, geographic information systems (GIS) - remote sensing, network science, and visual forensics.
a. Linguistic and Text-Based Methods
Glossary
Types and Examples of OSINT
Natural Language Processing (NLP): Tracing its origins back to Alan Turing’s 1950 article ‘ ‘Computing Machinery
and Intelligence’ (from which the ‘Turing test’ is born), NLP is primarily interested in the interaction between human
and machine language. Originally focusing on automated machine translations between human languages, NLP
today is focused on the discovery of patterns within structured and unstructured, multi-linguistic and large-volumes
of text, through entities, keywords, word/phrase relations and semantic/syntactic roles. NLP has paved way to more
contemporary text-based methods such as automatic text summarization, machine-based sentiment analysis, entity
and topic extraction and forms the foundation of modern text-mining tools.
Entity recognition-extraction: Named Entity Recognition is a process where an algorithm takes a string of text (sentence
or paragraph) as input and identies relevant nouns (people, places, and organizations) that are mentioned in that string.
News and publishing houses generate large amounts of online content on a daily basis and managing them correctly is
very important to get the most use of each article. Named Entity Recognition can automatically scan entire articles and
reveal which are the major people, organizations, and places discussed in them. Knowing the relevant tags for each
article help in automatically categorizing the articles in dened hierarchies and enable smooth content discovery.
Text corpus: A corpus is usually the main data pool for text-based OSINT methods. It is a collection of words and
keywords from which statistical analyses are made. n order to make the corpora more useful for doing linguistic
research, they are often subjected to a process known as annotation. An example of annotating a corpus is part-of-
speech tagging, or POS-tagging, in which information about each word’s part of speech (verb, noun, adjective, etc.)
is added to the corpus in the form of tags.
N-Gram: In language processing, an n-gram determines the unit of analysis for the query to be searched in the corpus.
If two words are searched together (i.e. ‘conventional’ + ‘warfare’, or ‘terrorist’ + ‘attack’, this query is called a bi-
gram. A tri-gram on the other hand is a 3-word query that specically searches for the combination of ‘conventional’ +
‘submarine’ + ‘warfare’, or ‘terrorist’ + ‘suicide’ + ‘attack’.
Latent Semantic Indexing (LSI): LSI is a machine learning-based text analytics method, which learns from a sample
text to identify the ‘latent’ concepts in multiple documents. For example, if ‘artillery’, ‘shell’ and ‘bombardement’
texts appear frequently in multiple documents, the system indexes these words into the same semantic context,
simultaneously separating the word ‘shell’ from documents that contain phrases ‘beach’, ‘sand’, or ‘crab’. LSI works
best in large volumes of text, such as archival documents, legislation or judicial documents.
Latent Dirichlet Allocation (LDA): LDA is a text-based machine-learning method similar to LSI, although LDA clusters
words into topic models by itself, rather than into folders determined by the user. LDA checks the frequency and
relation of words in a text based on how frequently they are used together, and in which context.
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Language and sentiment analysis has been one of the
oldest practices of OSINT. Inferring leadership psychology,
policy intent and organizational cohesion through speech
and writing have been a core practice of historical versions
of OSINT, enabling diplomats and other intermediaries to
synthesize crucial information. Indeed, through the Cold
War, the harvesting of newspapers, leadership statements
and even scientic journals has been commonplace in
countries on both sides of the conict.32 Furthermore, since
World War I, linguistics, anthropology and area studies have
grown signicantly popular from an intelligence point of view,
evidenced by the establishment of dedicated departments
in top universities and their receipt of signicant government
funding.33
Digitization of text and the popularization of text-as-data
methods in social sciences had a direct impact on linguistic
OSINT analysis. Although quantitative linguistics became a
popular eld as far back as 1960s, mass digitization and
standardization of text les through computer-based word
processors, have all contributed to the signicant advances
in open-source harvesting such as text categorization,
text clustering, entity extraction and computational
summarization. Thanks to such mass digitization, entire
national historical archives, political texts and memoirs
have become digitized for word-processing purposes,
providing linguists and content/discourse analysts with an
unprecedented data size and fast processing tools. These
tools have been especially valuable for Internet-based text-
mining, such as websites, blogs and social media posts. In
addition to the existence of 644 million websites in existence,
vast volumes of social media data pour in on a daily basis,
which means that an overwhelming majority of the world’s
text-based interactions are now searchable, sortable and
measurable - some of them in real-time.
Although text-based OSINT can be done through
programming standards such as Python, R, MatLab and
Ruby, there are dedicated text-based OSINT applications
as well. Some of the popular ones are WordStat, RapidMiner,
KHCoder and NVivo that allow users to detect and visualize
connections, patterns and themes in large volumes of text. In
addition, natural language processing applications based on
statistical topic modelling, such as Latent Dirichlet Allocation
(LDA), text segmentation, Latent Semantic Analysis and
Pachinko Allocation, enable a machine-learning approach
for pattern detection, and sentiment analysis. Furthermore,
entity-recognition and extraction applications make it far
easier to catalogue, sort and process large volume of social
media text data in order to do retrospective or real-time
analysis.
Several promising applications of OSINT include behavioural
prediction/detection, evidenced by Asghar (et. al.) work on
pattern detection on Youtube comment videos to measure
their level of radicalization,34 or Hsinchun Chen’s seminal
work on text mining of the Dark Web,35 and extremism
networks that lie within. Singh et. al. have took this a step
further and harvested Indian diplomats tweets to analyse
popularity dynamics between Indian Foreign Service and
Narenda Modi, giving a clear idea on diplomatic capital and
support for leadership.36 On prediction on the other hand
Mueller and Rauch have used newspaper text mining to
forecast imminent protests and conicts, coming up with
a clear model in using large amounts of text-as-data for
forecasting purposes.37
Johnson, The Oxford Handbook of National Security Intelligence, 144.
Osamah F. Khalil, America’s Dream Palace: Middle East Expertise and the Rise of the National Security State (Cambridge, Massachusetts: Harvard University Press, 2016).
Muhammad Zubair Asghar et al., “Sentiment Analysis on YouTube: A Brief Survey,” ArXiv 1511.09142 (November 29, 2015), http://arxiv.org/abs/1511.09142.
Hsinchun Chen, Dark Web: Exploring and Data Mining the Dark Side of the Web, Integrated Series in Information Systems (New York: Springer-Verlag, 2012),
//www.springer.com/gp/book/9781461415565.
V. K. Singh, D. Mahata, and R. Adhikari, “Mining the Blogosphere from a Socio-Political Perspective,” in 2010 International Conference on Computer Information Systems and
Industrial Management Applications (CISIM), 2010, 365–70, https://doi.org/10.1109/CISIM.2010.5643634.
Hannes Mueller and Christopher Rauh, “Reading Between the Lines: Prediction of Political Violence Using Newspaper Text,” American Political Science Review, December
2017, 1–18, https://doi.org/10.1017/S0003055417000570.
32
33
34
35
36
37
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b. Geospatial Intelligence and Remote Sensing Tools
Glossary
Vector and raster data: In GIS software, geographical information is stored into two main types of data. Vector data
is a representation of the world using points, lines, and polygons. Vector models are useful for storing data that
has discrete boundaries, such as country borders, land parcels, and streets. Raster data on the other hand, is a
representation of the world as a surface divided into a regular grid of cells. Raster models are useful for storing data
that varies continuously, as in an aerial photograph, a satellite image, a surface of chemical concentrations, or an
elevation surface.
GIS: A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and
present all types of geographical data. The key word to this technology is Geography – this means that some portion of
the data is spatial. In other words, data that is in some way referenced to locations on the earth. Coupled with this data
is usually tabular data known as attribute data. Attribute data can be generally dened as additional information about
each of the spatial features.
LIDAR: LIDAR, which stands for Light Detection and Ranging, is a remote sensing method that uses light in the form of a
pulsed laser to measure ranges (variable distances) to the Earth. These light pulses—combined with other data recorded
by the airborne system— generate precise, three-dimensional information about the shape of the Earth and its surface
characteristics. A LIDAR instrument principally consists of a laser, a scanner, and a specialized GPS receiver. Airplanes
and helicopters are the most commonly used platforms for acquiring LIDAR data over broad areas.
Landsat: The LANDSAT program is the oldest, functional satellite imagery program, which consists of a series of optical/
infrared remote sensing satellites for land observation. The program was rst started by The National Aeronautics and
Space Administration (NASA) in 1972, then turned over to the National Oceanic and Atmospheric Administration (NOAA)
after it became operational.
Remote sensing: Remote sensing is the science of obtaining information without physically being there. For example,
the 3 most common remote sensing methods is by airplane, satellite and drone. Remote sensing instruments are of two
primary types—active and passive. Active sensors, provide their own source of energy to illuminate the objects they
observe. An active sensor emits radiation in the direction of the target to be investigated. The sensor then detects and
measures the radiation that is reected or backscattered from the target. Passive sensors, on the other hand, detect
natural energy (radiation) that is emitted or reected by the object or scene being observed. Reected sunlight is the
most common source of radiation measured by passive sensors.
Basemap: A basemap provides a user with context for a map. Vector or raster data can be added to a basemap by
overlaying on top of it. Basemaps contain reference information that may provide different geospatial information
based on what the cartographer is trying to communicate.
Geocoding-geofencing: Geocoding is the process of transforming a description of a location—such as a pair of
coordinates, an address, or a name of a place—to a location on the earth’s surface. An analyst can geocode by entering
one location description at a time or by providing many of them at once in a table. The resulting locations are output as
geographic features with attributes, which can be used for mapping or spatial analysis. Geofencing on the other hand,
is a location-based service in which an app or other software uses GPS, RFID, Wi-Fi or cellular data to trigger a pre-
programmed action when a mobile device or RFID tag enters or exits a virtual boundary set up around a geographical
location, known as a geofence. Depending on how a geofence is congured it can prompt mobile push notications,
trigger text messages or alerts, send targeted advertisements on social media, allow tracking on vehicle eets, disable
certain technology or deliver location-based marketing data.
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Like language, cartography too, is an old school of intelligence
and strategic analysis, working primarily on geopolitical
and geographical variables, as well as the political impact
of borders and terrain. The combination of geographic
information systems – or GIS – and Internet-based location
data (check-ins, location designations) has allowed analysts
to harness a wider range of social and spatial dynamics of
human behaviour, including mobilization, mass movement
and conicts.38 With the additional variables of altitude,
topography, elevation, resources, transportation and
infrastructure, small and large-scale human behaviour can
be analysed and mapped into meaningful patterns through
the use of geospatial intelligence – or GEOINT.39 Although
there are dedicated GIS platforms for this kind of analysis –
ArcGis, QGis – programming platforms such as Python and
R (even Excel) also have GIS packages, or extensions to
integrate mapping, geostatistics and proximity analysis. With
the additional imagery power of Planet Labs, Terra Bella,
BlackSky Global and XpressSAR, a multitude of layers, time-
frames and granularity of geographical information can now
be utilized by citizen GEOINT analysts.
In GEOINT, there are two main types of data: vector and
raster. Vector data is the combination of the set of polygons
and coordinates to designate a specic location or area
on a map. Raster data on the other hand, include imagery,
elevation models and map renders to make 3D analysis.
With the increasing popularity of GIS, there are signicantly
more geospatial databases on the Internet. These datasets
are also supplemented by LiDAR (Light Detection and
Ranging), UAVs, GPS and satellites to increase the
granularity and size of geographic datasets. Regardless
of technique, some of the best applications of GEOINT,
not only supply and visualize spatial data, but also tell a
policy story or see a strategic gap where other methods
can’t. Harvard Humanitarian Initiative for example, is one of
the earlier examples of a university-led GEOINT approach.
Having been established in 1999, HHI has partnered with
NGOs, UN relief agencies and refugee aid organizations to
map crises and conicts in Darfur, Sudan, Chad and Congo
in close partnership with ground assets.40 During Hurricane
Katrina on the other hand, both US government and non-
governmental analysts have adopted different GIS methods
for relief and disaster response.41 Ushahidi - a non-prot
technology company - is another notable non-state OSINT
initiative, which focuses on election monitoring, disaster
relief and humanitarian aid in Haiti, Chile, Kenya and Italy.
Ushahidi used a ‘crowdmap’ - a crowdsourced map event
data platform in order to crowdsource crisis events.42
Crowdmap was deployed in a number of protests around the
world, including Occupy movements, 2011 London protests,
in addition to the company’s famous event monitoring of
the 2007-2008 Kenyan crisis. Later on, Ushahidi provided
the infrastructure for crisis event data collection based on
witness accounts and was deployed to monitor the elections
in Italy and India.43
Thomas Zeitzoff, “How Social Media Is Changing Conict,” Journal of Conict Resolution 61, no. 9 (October 1, 2017): 1970–91,
https://doi.org/10.1177/0022002717721392; Seva Gunitsky, “Corrupting the Cyber-Commons: Social Media as a Tool of Autocratic Stability,” Perspectives on Politics 13,
no. 1 (March 2015): 42–54, https://doi.org/10.1017/S1537592714003120.
Bacastow and Bellaore, “Redening Geospatial Intelligence.”
Steve Lohr, “In Relief Work, Online Mapping Yet to Attain Full Potential,” The New York Times, March 28, 2011, sec. Business Day,
https://www.nytimes.com/2011/03/28/business/28map.html.
Jeffrey Gettleman, “Congo: Rapes by Civilians Rise Sharply, Study Says,” The New York Times, April 14, 2010, sec. Africa,
https://www.nytimes.com/2010/04/15/world/africa/15briefs-congo.html.
Anand Giridharadas, “Ushahidi - Africa’s Gift to Silicon Valley: How to Track a Crisis,” The New York Times, March 13, 2010, sec. Week in Review,
https://www.nytimes.com/2010/03/14/weekinreview/14giridharadas.html.
Sarah Wheaton, “New Technology Generates Database on Spill Damage,” The New York Times, May 4, 2010, sec. U.S.,
https://www.nytimes.com/2010/05/05/us/05brigade.html.
38
39
40
41
42
43
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c. Connections and Networks
Relations, groups and networks have always been popular
for OSINT. Organizational leadership, political decision-
making circles, terrorist inner circles have been central topics
of inquiry for intelligence analysis. Classical network theory
focuses on social networks among individuals (friendships,
advice-seeking..) and formal contractual relationships
(alliances, trade, security community). What makes network
theory important to social science, politics and IR is its
ability to conceptualize and theorize relations at the micro,
meso and macro-levels of analysis in political processes,
offering a structure to seemingly complex interactions.
Accordingly, network theory stipulates that relations and
internal-external pressures on those relations have the
ability to affect beliefs and behaviors. Instead of adopting
IR’s mainstream levels of analysis approach, network
theory focuses on the interactions between these levels of
analyses, aiming to conceptualize how these interactions
lead to policy and behavior.44 A variety of applications such
as Gephi, NetMiner and iGraph have made it easier to work
with larger networks and measure them by betweenness,
homophilly and centrality using quantitative methods. This
enables extremism and radicalization networks easier to
visualize and contextualize the role of hierarchies and
inuencers much clearer compared to traditional methods.45
Computational network analysis on the other hand, expands
classical network theory to far larger size and complexity
levels, not only designating relations between them, but
also use articial intelligence, machine learning and neural
networks approaches to automatically generate real-time
changes in these relations. Today, one of the most popular
uses of network analysis in digital OSINT is social media
analysis; namely, follow, like and share relations between
very large groups.46 Compared to older methods, social
network analysis enabled inuencers and hierarchies in
these systems more successfully.
Johnson, The Oxford Handbook of National Security Intelligence, 26.
Matt Apuzzo, “Who Will Become a Terrorist? Research Yields Few Clues,” The New York Times, December 21, 2017, sec. World,
https://www.nytimes.com/2016/03/28/world/europe/mystery-about-who-will-become-a-terrorist-dees-clear-answers.html.
Jytte Klausen, “Tweeting the Jihad: Social Media Networks of Western Foreign Fighters in Syria and Iraq,” Studies in Conict & Terrorism 38, no. 1 (January 2, 2015): 1–22,
https://doi.org/10.1080/1057610X.2014.974948.
44
45
46
Glossary
Network nodes: In a communications network, a network node is a connection point that can receive, create, store
or send data along distributed network routes. Each network node -- whether it’s an endpoint for data transmissions
or a redistribution point -- has either a programmed or engineered capability to recognize, process and forward
transmissions to other network nodes.
Homophily: Network homophily is a theory, which argues that similar nodes are more likely to attach to each other than
dissimilar ones. In dense and large social networks, homophily measure enables an analyst to identify a community
or a group within a larger population pool easily. Homophily is a key topic in network science as it can determine the
speed of the diffusion of information and ideas.
Density: The density statistic represents the proportion of possible relationships in the network that are actually present.
The value ranges from 0 to 1, with the lower limit corresponding to networks with no relationships and the upper limit
representing networks with all possible relationships. The closer the value is to 1, the denser is the network and the
more cohesive are the nodes in the network. Information in dense networks can ow more easily than information in
sparse networks.
Centrality (betweenness): In network analysis, centrality designates the most important nodes in a graph, with regard
to the number of connection to other nodes. In OSINT, network centrality studies usually focus on the most important,
or best-connected members of a large group. In social network analysis, high-centrality gures are those that assume
inuencer status.
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d. Image and Video Forensics
As wi and phone data network services became faster and
cheaper, online human communication has rapidly evolved
from text-based to media-based. We usually nd it easier
to send a voice message on Whatsapp instead of texting,
or to send a photo or a video to express longer sentences
and paragraphs. The same logic works for crises and
emergencies. Under stress, people tend to share images
and videos to document, or call for help, instead of texting
and typing long messages online. To that end, although we
tweet, share and blog, the increasing majority of our digital
communication (especially during crises) has become
media-based. While studying photographs for strategic gain
or emergency communication goes back to the late-19th
century, ‘video intelligence’ as a common practice, is mostly
a post-World War 2h endavor. Today, such visual media
can be digitally analysed, interpreted and used to extract
key information from the ground - especially in conict,
protest or disaster areas where physical access is limited.
Images and videos can be used for verication, statement,
propaganda and counter-propaganda purposes on the
battleeld, or in crisis episodes; they can be shared as an
evidence of relations, interests and capabilities. Due to the
value of emergency media for OSINT, this is also one of the
most vulnerable areas for manipulation and forgery. Images
and videos alike can be faked, doctored, and old media can
be shared as new. This in turn allows state and non-state
actors to mislead, distract and intimidate their rivals during
emergencies.
Glossary
Artefact: An artefact is a visible distortion and visual error in a media (video, audio or image). Artefact homogeneity
is a media forensics tool that measures whether a media type is manipulated by measuring the extent to which these
distortions are even throughout the media. Artefact unevenness is usually associated with manipulation and doctoring,
and can be noticed through machine-learning-based media forensics tools.
Metadata: Media les contain properties that describe the contents of the le. These properties can be categorized
as follows:
a) Media-type attributes that specify the encoding parameters, such as the encoding algorithm (media subtype), video
frame size, video frame rate, audio bit rate, and audio sample rate.
b) Metadata contains descriptive information for the media content, such as title, artist, photographer, and genre.
Metadata can also describe encoding parameters. It can be faster to access this information through metadata than
through media-type attributes.
c) DRM properties, which contain information on usage restrictions. Currently Media Foundation does not support
DRM properties through metadata, with the exception of the PKEY_DRM_IsProtected property.
Photogrammetric Analysis: Originally a tool for MASINT, photogrammetry is the science of extracting measurements
from photographs. Such measurements can be exact coordinates, or distance between images on the media. Currently,
OSINT analysts can conduct digital photogrammetric analysis through 2-D and 3-D images collected through satellite,
drone or LIDAR imagery. Algorithms for photogrammetry typically attempt to minimize the sum of the squares of errors
over the coordinates and relative displacements of the reference points.
Digital Forensics: The area of digital media forensics is both the vocation of nding deleted or hidden data and also
the mastery of the underlying technologies behind the various tools used and the ability to present scientically valid
information. Digital media forensics is a growing science that allows governments and corporations to assess the
genuineness of digital evidence.
Photographic Comparison: As an image forensics tool, photographic comparison tests the genuineness or alterations
across multiple versions of the same image. On the Internet, photographic comparison is required to sort through large
quantities of similar images to nd the original version. Especially in images related to crisis events, or photographs that
have high political value, automated comparison software can be used to detect unevenness that can’t be measured
by the human eye.
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Cyber Governance and Digital Democracy 2018/8
Several private initiatives have embarked on a dedicated
study of web-based images and videos to form a
crowdsourced OSINT network, the most famous being
Bellingcat – the online investigation platform. Bellingcat
has published several tutorials on how to conduct media-
based OSINT, and some of its famous investigations include
Russian troop movements, Syrian chemical weapons use
verication and protestor-riot police dynamics in a number
of incidents.47 Another example is Forensic Architecture - an
academic-activist platform headquartered at the University
of London - which uses photos, videos and aerial imagery to
reconstruct poorly documented incidents that contain political
importance.48 Both Bellingcat and Forensic Architecture aim
to verify critical events through a methodical study of media,
as well as stitching scattered visual evidence together
through diverse sources in order to create evidence. Initially
viewed as an enthusiast’s hobby, media forensics OSINT
initiatives have grown more relevant and efcient compared
to state intelligence agencies, evidenced by the fact that
both Bellingcat and Forensic Architecture initiatives have
provided court evidence, as well as data for UN and state-
led human rights reports.49
With so many plentiful and publicly accessible critical
data types, it is quite tempting to make the case that the
‘secrets are over’, or that we are entering a ‘post-secret’
world order. Indeed, when Sean P. Larkin heralded the
‘Age of Transparency’ in his famous Foreign Affairs piece,
he was adamant that the proliferation of commercially-
available satellite imagery, drone sensing, automated crisis
reports, citizen journalists and open source bloggers would
render secrets meaningless.50 His point was that due to
the decreasing costs of publicly available surveillance, the
costs of acquiring and protecting secrets were increasing.
States’ ability to create and sustain frames and narratives
(ontological security) during crises, diplomatic escalations
and protests has been substantially hampered by technology.
Especially since the global discovery of the power of social
media during key events, states had to compete with new
sources of narratives and framing beyond the conventional
news sources.
Global interconnectedness and the emergence of ‘citizen-
led reporting’ has brought about a new analyst caste:
crowdsourced intelligence network, aiming to harness
the labor of like-minded digital activists to challenge and
counter state narratives. In fact, it was the United States
(DARPA) that had rst tried to use crowdsourcing for
intelligence analysis, through its 2009 digital exercise
titled ‘Network Challenged’.51 During this crowdsourcing
exercise, a multitude of challenges faced by state-led
efforts in OSINT (such as fast verication, event data
generation, measurement) could be better managed by a
semi-autonomous network of users, working through social
networking tools. This exercise has demonstrated that
‘amateurs’ (meaning civilians that had little or no formal
background in intelligence and policy planning) were
both useful and not so useful from different perspectives.
Crowdsourced OSINT was denitely fast, unbound by the
constraints of bureaucracy and strict policy. On the other
hand, most of these OSINT enthusiasts lacked sufcient
intelligence training, policy organization and coherence
in preparing policy options for decision-makers. In other
words, crowdsourced OSINT was deemed to be good at
challenging state narratives during a focused incident (like
a crisis), but lacked capacity to monitor and harvest regular,
daily Internet data to designate political patterns and come
up with policy suggestions.52
Crowdsourced OSINT
Pablo Gutierrez and Paul Torpey, “How Digital Detectives Say They Proved Ukraine Attacks Came from Russia,” The Guardian, February 17, 2015, sec. World news,
http://www.theguardian.com/world/2015/feb/17/ukraine-russia-crossborder-attacks-satellite-evidence.
Rowan Moore, “Forensic Architecture: The Detail behind the Devilry,” The Observer, February 25, 2018, sec. Art and design,
http://www.theguardian.com/artanddesign/2018/feb/25/forensic-architects-eyal-weizman.
Dylan Collins, “A US Airstrike Which Killed 38 People Allegedly Hit a Peaceful Mosque in a Syrian Village,” Business Insider, April 18, 2017,
http://www.businessinsider.com/us-airstrike-allegedly-hit-a-peaceful-mosque-in-a-syrian-village-2017-4.
Sean P. Larkin, “The Age of Transparency,” Foreign Affairs, April 18, 2016, https://www.foreignaffairs.com/articles/world/2016-04-18/age-transparency.
Mark Harris, “How A Lone Hacker Shredded the Myth of Crowdsourcing,” WIRED, September 2, 2015,
https://www.wired.com/2015/02/how-a-lone-hacker-shredded-the-myth-of-crowdsourcing/.
47
48
49
50
51
Larry Greenemeier, “DARPA Verigames Crowdsourced Formal Verication (CSFV) Project,” Scientic American, June 9, 2015,
https://www.scienticamerican.com/citizen-science/darpa-verigames-crowdsourced-formal-verication-csfv-project/.
52
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Cyber Governance and Digital Democracy 2018/8
In addition, it is also politically hard for states to harness
the power of crowdsourcing in OSINT during crisis events.
Given how most digital OSINT tools became globally
commonplace with 2011 following the Occupy and Arab
Spring movements, the overall tone of the practice became
anti-hegemonic and oppositional.53 Most earlier forms of
crowdsourced OSINT focused on steering protest crowds,
organizing protest logistics and circumventing the police,
or state intelligence agencies. To that end, a wide chasm
emerged between state intelligence agencies that mistrust
OSINT, and citizen-led analytics that mistrust the motives
of the state. This mutual mistrust has so far prevented
a workable model for state-led efforts in cultivating a
crowdsourced OSINT environment. Since such a model
unforthcoming, states and citizen-led efforts use their own
tools and networks during emergencies.
Crowdsourcing involves ground-based event data
producers, near-ground-based data curators and off-
site, remote location data analyzers. One the earlier good
examples is the Ushahidi (means ‘witness’ in Swahili)
platform that mapped election-related violent activities in
Kenya between 2007-2008. Ushahidi’s event data detection
performance did a better job than state intelligence actors
in monitoring the conict there, as it still is the primary data
source on Kenyan election violence as of today.54 Ushahidi
later switched to a GeoCommons mapping platform to
mobilize and crowdsource event data on the 2010 Haiti
earthquake, considerably helping aid and relief agencies in
their efforts to respond to as many incidents as possible.
Ushahidi would later grow to a level of importance that the
United Nations Ofce for the Coordination of Humanitarian
Affairs (OCHA) partnered with the platform to create a Libya
Crisis Map, to gather ground data in war-stricken areas that
need aid drop.55 In one of the earlier examples of how such
civilian-led relief and aid OSINT platforms could be exploited
by state actors, some NATO air assets used this aid map to
rene ground targets and schedule aerial bombardments.56
Bellingcat and LiveUAMap are two of the newer additions
to crowdsourced intelligence. Bellingcat appeared from
its humble beginnings in 2012 as a blog and LiveUAMap
during the earlier phases of the Russian military involvement
in Ukraine in 2014. Bellingcat rose to fame in 2014, when
its crowdsourced analysts used open-source tools to
discover which Russian unit shot down the MH17 ight in
Ukraine.57 This investigation was a turning point in OSINT, as
its use of publicly available information generated stronger
evidence against Russian involvement in the MH17 shooting
compared to all other state-produced evidence reports.
Ultimately, it was Bellingcat’s report that was incorporated
into the indictment at the Dutch court that was handling
the investigation.58 Later in 2014, Bellingcat would publish
successive online reports on the use of cluster munitions
and other internationally banned area-of-effect weapons,
demonstrating how the Syrian Army was producing,
transporting and deploying these banned weapons.59
Then in 2015, Bellingcat became the rst OSINT outlet
that discovered the shifting drone tactics of ISIS and their
invention of the grenade-dropping UAVs.60 The initiative has
since grown considerably in fame and volunteers, building
up a network of crowdsourced event data producers, video
and image analysts and GIS mappers around the world.
The group has also begun teaching their OSINT methods to
enable more citizen-led intelligence production.
LiveUAMap works slightly differently than Bellingcat.
LiveUAMap harvests social media data in near-real-time in
order to display and map conict events on their interactive
world map. Although the group initially started as an outlet
Tufekci, Twitter and Tear Gas.
Giridharadas, “Ushahidi - Africa’s Gift to Silicon Valley.”
John D. Sutter, “Ushahidi: How to ‘crowdmap’ a Disaster,” CNN Labs, October 25, 2010, http://www.cnn.com/2010/TECH/innovation/10/25/crowdmap.disaster.internet/index.html.
Ian Traynor, “Libya: Nato Bombing of Gadda Forces ‘Relying on Information from Rebels,’” The Guardian, May 18, 2011, sec. World news,
http://www.theguardian.com/world/2011/may/18/libya-nato-bombing-benghazi-rebel-leaders.
Max Fisher, “Did Ukraine Rebels Take Credit for Downing MH17?,” Vox.com, July 17, 2014,
https://www.vox.com/2014/7/17/5913089/did-this-ukrainian-rebel-commander-take-credit-for-shooting-down-the.
Mark Gibney, “The Downing of MH17: Russian Responsibility?,” Human Rights Law Review 15, no. 1 (March 1, 2015): 17, https://doi.org/10.1093/hrlr/ngu036.
53
54
55
56
57
58
Martin Chulov, “Syria Attack: Nerve Agent Experts Race to Smuggle Bodies out of Douma,” The Guardian, April 12, 2018, sec. World news,
http://www.theguardian.com/world/2018/apr/12/syria-attack-experts-check-signs-nerve-agent.
Ben Sullivan, “The Islamic State Conducted Hundreds of Drone Strikes in Less Than a Month,” Motherboard, February 21, 2017,
https://motherboard.vice.com/en_us/article/vvxbp9/the-islamic-state-conducted-hundreds-of-drone-strikes-in-less-than-a-month.
59
60
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Cyber Governance and Digital Democracy 2018/8
to monitor specically Russian activities in Ukraine in 2014,
it has grown in scope to include Syria, Iraq, and then, rest
of the world. LiveUAMap is a truly crowdsourced conict
monitoring platform that uses social media data in multiple
languages to create real-time alerts, as well as a database
of events that go as far back to 2013. Similar initiatives are
FlightTracker, which maps and displays the code information
and destination of commercial, as well as government
ights, TankerTracker, which tracks oil and natural gas
tankers across the world’s main ports, and DroneDeploy,
which provides a real-time visualization of major combat
and reconnaissance drones deployed by militaries and non-
state actors across the world.
In November 2017, the tness-tracking app and gadget
maker Strava, has released its users’ dataset containing 13
trillion GPS location data points.61 Initially thought of as a way
to help people socialize through their tness performance
(i.e. how much, or fast they ran) by sharing personal scores
on social media, the release turned out to be an operations
security disaster. While these individual location data points
revealed popular running routes in major cities, they also
revealed unidentied military bases via soldiers’ Strava
tracker use. While the commercialization of drone and
satellite imagery have already led to the discovery of most
major military installations in the world, Strava data took this
one step further: exposure of secret military installations
(especially those in combat zones) and the time, date
and trajectory of runners in those military bases. Although
unintended, this was such a major security breach that
Colonel John Thomas, a spokesperson for the US Central
Command gave a statement to the Washington Post that the
military was ‘looking into the implications of the map’.62
Then in mid-March 2018 the data analytics company
Cambridge Analytica’s extra-judicial dealings with the
Trump campaign were exposed, revealing how 50 million
Facebook proles were harvested without consent.63
Facebook was directly involved as an active actor in the
scandal, by willingly exposing 50 million prole raw data
to Aleksandr Kogan, a senior Analytica data scientist,
who had close contacts with Steve Bannon, who was a
major leader within the Trump campaign. Kogan had built
‘thisismydigitallife’ – a quiz app on Facebook – which pro
led an initial 270,000 Facebook users who took the quiz,
without the knowledge of this data to be used in a political
campaign.64 Through network analysis methods (friends,
interests, likes) Kogan was able to access 50 million users’
data through this initial 270,000. More recently, a group of
political scientists have used text-based machine learning
methods to analyze classication patterns of US State
Department cables since 1971.65 These cables contained
correspondence between the State Department and a US
diplomatic mission in a foreign country. By studying the
content of millions of cables, the researchers have identied
which word combinations are likely to be in cables that
are agged as ‘secret’, ‘condential’, ‘limited ofcial use’,
or ‘unclassied’. The study has revealed that human error
plays a considerable role in the misclassication of secrets,
leading to the declassication of a large number of secret
documents. Most critically, the study has discovered that
there are uneven rules that govern whether a document is
‘secret’. This, researchers argue, can both allow other states
to use machine learning tools to extract secret information
through declassied US archives, and also for civilian
analysts to tap into the same reservoir to leak secrets to
International Political Implications of OSINT:
Democracy and Security Dilemma
Andrew Liptak, “Strava’s Fitness Tracker Heat Map Reveals the Location of Military Bases,” The Verge, January 28, 2018,
https://www.theverge.com/2018/1/28/16942626/strava-tness-tracker-heat-map-military-base-internet-of-things-geolocation.
Alvin Chang, “The Facebook and Cambridge Analytica Scandal, Explained with a Simple Diagram,” Vox, March 23, 2018,
https://www.vox.com/policy-and-politics/2018/3/23/17151916/facebook-cambridge-analytica-trump-diagram.
62
63
Alex Hern, “Fitness Tracking App Strava Gives Away Location of Secret US Army Bases,” The Guardian, January 28, 2018, sec. Technology,
http://www.theguardian.com/world/2018/jan/28/tness-tracking-app-gives-away-location-of-secret-us-army-bases.
61
Carole Cadwalladr, “‘I Made Steve Bannon’s Psychological Warfare Tool’: Meet the Data War Whistleblower,” The Guardian, March 18, 2018, sec. News,
http://www.theguardian.com/news/2018/mar/17/data-war-whistleblower-christopher-wylie-faceook-nix-bannon-trump.
Renato Rocha Souza et al., “Using Articial Intelligence to Identify State Secrets,” ArXiv 1611.00356 (November 1, 2016), http://arxiv.org/abs/1611.00356.
64
65
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Cyber Governance and Digital Democracy 2018/8
the public. All three incidents demonstrate how states and
civilians alike can be the victims of OSINT and neither ‘side’
has the real upper hand in the vast analytic ocean.
Following the Weberian logic that states are the sole
legitimate wielders of organized violence, the same
can be applied to the eld of secrecy. States are usually
thought of as the sole legitimate wielders of organized,
institutional secrecy. Most voters in democracies and
authoritarian systems alike, think that states should be
capable of collecting and reliably processing large troves
of intelligence concerning national security, as well as be
able to protect those secrets from rival access. Yet, what
separates democracies from authoritarian systems is the
issue of intelligence oversight and safeguards against
abuses of such secrecy.66 Citizens and domestic targets
are often the most vulnerable and easiest targets against
such abuse, since the very counterintelligence and secrecy-
accumulation tools that states use to achieve security can
be used to track and suppress domestic dissent.67 On the
other hand, greater transparency and accountability saps
intelligence agencies’ speed and operational range of
intelligence agencies, having a negative effect on national
security. This produces an inherent dilemma for those
seeking to achieve a middle ground between privacy and
security: on the one hand, unchecked intelligence agencies
do and will impair a country’s democratic functioning by
abusing the vast surveillance apparatus. Most policies that
successfully render intelligence activities more transparent
end up disabling intelligence services’ effectiveness, scope
and deterrence capability.68
The prevalent argument against oversight in democracies
is that intelligence is not a ‘regular’ policy area that can be
restrained through normal judicial and legislative means.69
By rendering intelligence activities subject to lengthy legal
and parliamentary fact-nding and supervision, countries
may a) miss a critical intelligence interception, b) lose
the comparative advantage of sensitive information to the
intelligence services of authoritarian countries, and c) fail to
prevent an attack, which will generate far more signicant
public backlash compared to intelligence abuse.70 An
authoritarian government that has none (or few) of these
democratic constraints can become nimbler and faster over
the short-term to meet the requirements of global intelligence
rivalry and score an advantage against democracies – or so
the primary argument goes. There are two main problems
with this argument. First, as Desch,71 and Reiter (et. al.)72
have demonstrated, democracies too, can keep large
amounts of information hidden from the public eye and can
also successfully avoid oversight mechanisms. As recurring
examples show, democracies are as likely as autocracies
to go to unilateral or diversionary wars by misleading public
opinion.73 Secondly, there is no evidence to support the
claim that oversight mechanisms or safeguards against
intelligence abuse render democracies strategically
less advantageous than autocracies. The general trend
established in the literature (with few outliers) still remains
robust: due to open information and a wider ‘marketplace of
ideas’, democracies tend to miscalculate and misperceive
less, don’t ght with each other, tend to suffer less from civil
wars and domestic disturbances, and end up winning most
of the wars they get into.74 So, what’s the problem?
The causal mechanism between intelligence oversight and
strategic disadvantage is weak at best, due to fact that it is
Michael P. Colaresi, Democracy Declassied: The Secrecy Dilemma in National Security (Oxford, UK: Oxford University Press, 2014).
Zachary K. Goldman and Samuel J. Rascoff, Global Intelligence Oversight: Governing Security in the Twenty-First Century (Oxford: Oxford University Press, 2016), 14.
Daniel Baldino, ed., Democratic Oversight of Intelligence Services (Sydney: Federation Press, 2010), 3.
Johnson, The Oxford Handbook of National Security Intelligence, 80.
Goldman and Rascoff, 72.
66
67
69
70
68
Michael C. Desch, “Democracy and Victory: Why Regime Type Hardly Matters,” International Security 27, no. 2 (October 1, 2002): 5–47,
https://doi.org/10.1162/016228802760987815.
Dan Reiter, Allan C. Stam, and Alexander B. Downes, “Another Skirmish in the Battle over Democracies and War,” International Security 34, no. 2 (September 30, 2009):
194–204, https://doi.org/10.1162/isec.2009.34.2.194.
Erich Weede, “Democracy and War Involvement,” Journal of Conict Resolution 28, no. 4 (December 1, 1984): 649–64, https://doi.org/10.1177/002200278402800400;
Kenneth A. Schultz, “Do Democratic Institutions Constrain or Inform? Contrasting Two Institutional Perspectives on Democracy and War,” International Organization 53,
no. 2 (ed 1999): 233–66, https://doi.org/10.1162/002081899550878.
Johnson, The Oxford Handbook of National Security Intelligence, 167; Baldino, Democratic Oversight of Intelligence Services, 45; David Lyon, Kirstie Ball, and Kevin D. Haggerty,
Routledge Handbook of Surveillance Studies (New York: Routledge, 2012), 51.
71
72
73
74
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Cyber Governance and Digital Democracy 2018/8
rarely safeguards that render intelligence ineffective.75 Fast
and good intelligence are two different things, as well as
the fact that fast intelligence doesn’t always lead to good
policy. Although democracies may lose time and range
with their intelligence operations through the constraints
set by safeguards, they more than make up for this short-
coming in two areas. First, due to intelligence safeguards
and oversight mechanisms, agencies have to pass through
a review system that tests the rationale, reasoning and
strategic utility of surveillance practices.76 This additional
layer of oversight has a likelihood of spotting mistakes or
misjudgements early on, preventing agencies to get sucked
up into a costly mistake or an international incident that will
lead to diplomatic escalation with another country. Second,
democracies tend to be less concerned with the ideological
purity of the intelligence community and more with its
technical level of skill and capacity. In most authoritarian
regimes, inuential positions in intelligence are lled with
commissar-type appointees, or relatives that have little, or
insufcient operational/technical expertise.77 In ideologically-
driven intelligence agencies, where capability is a secondary
consideration in appointments, fast decision-making usually
ends up in costly miscalculations, offsetting the speed
and range benets of not having oversight mechanisms or
safeguards. Therefore, although democracies may make
slower intelligence decisions, these are usually made by
a more technocratically-oriented community, with better
interaction between the decision-making, judicial organs
and technocrats, ultimately leading to better-formulated
and less crisis-prone policies. This eventually renders
democratic intelligence practices more likely to lead to good
national security policy, compared to authoritarian systems.
In the same capacity, the ‘intelligence dilemma’ – namely,
the notion that states are ‘secrecy maximizing’ actors that
operate in a zero-sum information environment – may be less
important than argued. First of all, states collect, process
and store intelligence commensurate with their technical,
human and bureaucratic infrastructure.78 States cannot be
intelligence-maximizing actors, simply because once they
accumulate secrets beyond their infrastructure limits, they
end up becoming unable to protect them against foreign
spying. To that end, states are secrecy-optimizing actors
that have to prioritize the type of information they spend their
infrastructure on, so that they can process them meaningfully
for decision-making and to protect such secrets at a pareto-
optimal cost against foreign prying.
OSINT has changed this equation substantially. High-quality
intelligence is no longer in the hands of a small monopoly
of states and powerful corporations. Journalists, NGOs, and
citizens too, now have the tools access, harvest, process
and disseminate previously classied information. The
marketization of intelligence - surveillance equipment, social
media analytics services and the programming revolution -
led to the emergence of new power sources in international
intelligence competition. Hackers are old news - these non-
state actors have already grown into a regular variable in
strategic competition, be it independent, or state-supported.
Emerging power sources in OSINT don’t have to possess
the coding ingenuity of hackers. Availability of commercial
satellite imagery, over-the-counter drones, social media
analytics platforms and a bit of free time have all contributed
to the advent of the global OSINT caste, with disproportionate
inuence over information politics. Today, enthusiasts with
modest levels of technical knowledge, less-than-basic
programming ability and a keen eye for exploring digital
media data can become a part of the global crowdsourced
OSINT network.
Now states not only have to think of other states, or big
corporations as intelligence competitors, nor hackers,
but also this global network of citizen journalists, OSINT
enthusiasts and civilian data analytics initiatives. This network
is becoming increasingly more inuential on challenging or
supporting state-led information operations, propaganda
and political communication warfare, often yielding major
international evidence, as illustrated by the Bellingcat’s
MH17 ight forensics work.79 How states should respond
to the advent of digital crowdsourced OSINT is largely a
regime-type question, due to the role of secrecy in state-
society relations. Normally, it is expected that authoritarian
Baldino, Democratic Oversight of Intelligence Services, 89.
Johnson, The Oxford Handbook of National Security Intelligence, 243.
Colaresi;, Democracy Declassied, 51.
76
77
78
“MH17 - The Open Source Investigation, Three Years Later,” Bellingcat, July 17, 2017,
https://www.bellingcat.com/news/uk-and-europe/2017/07/17/mh17-open-source-investigation-three-years-later/.
79
Hans Born and Ms Marina Caparini, Democratic Control of Intelligence Services: Containing Rogue Elephants (Ashgate Publishing, Ltd., 2013), 4.
75
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Cyber Governance and Digital Democracy 2018/8
regimes should be the most vulnerable to the effects of
growing democratization of critical information. After all,
such regimes withhold the most amount of information from
public eye, have little state-society interaction in sharing
political information, and have virtually no oversight against
intelligence abuse. These regimes frequently spy on their own
citizens with the explicit purpose of suppressing dissent and
opposition, and due to the absence of safeguards, checks
and balances, they suffer from structural mismanagement
and corruption in national security and intelligence affairs.
In contrast, although democracies will also suffer from
drawbacks of exposure and leaks, such damage is thought
to be minimal due to the existing democratic structures,
including free and fair elections, a functioning parliament
and public oversight and shaming mechanisms.
The biggest criticism of intelligence oversight and
safeguard mechanisms is that they lack the technical
knowledge and background to properly evaluate what
their intelligence agencies are doing with technology.80
This was best evidenced by some of the archaic and
tone-deaf questions posed against Mark Zuckerberg
during the Facebook testimony.81 One major way OSINT
can contribute to oversight is to provide readily-available
analysis that most oversight mechanisms cannot conduct
by themselves. Through a methodical analysis of open-
source tools, a technically procient networked crowd can
aid more established, but slower safeguard institutions with
data, evidence and monitoring metrics on the abuses of
secrecy. But will OSINT expedite, or enable democratization
of authoritarian regimes? This is unlikely, as there are more
variables in this equation in real life. Although authoritarian
states lose more substantial amounts of policy secrets to
OSINT, this doesn’t necessarily lead to a call to replace
the regime, or government, or mobilize sufciently to
enable this transition. Most of the time, democratic leaks
and exposures - as small as they can be - are more likely
to lead in government resignations or substantial drop in
government support, compared to autocracies. Regardless
of their loss, authoritarian states are more likely to rely on
brute-force tactics of imprisoning and intimidating potential
blowback effects against exposure.82 Although criticism and
public reaction against exposures of mismanagement and
miscalculations are similar across regime types, their ability
to turn into political pressure and shake up a government
are structurally different.
How about, then, the relationship between regime type
and foreign policy effectiveness in the age of OSINT? The
mainstream argument goes that the advent of OSINT makes
it difcult for states to deceive the public or the international
audience, given the availability of alternative information.
Ideally, OSINT should enable a better ow of accurate
information and proper fact-checking across the Internet,
offsetting any propaganda effects of state-led misinformation
attempts. This turn, is thought to make foreign policy more
carefully-crafted and less likely to be based on deliberate
misinformation, given their ultimate exposure through
OSINT. However, this doesn’t always turn out to be the
case. One reason for this is the ‘rallying effect’; an electoral
reex that translates into greater support and mobilization
in support of the government and leadership during times
of crisis and escalation. The rallying effect minimizes
public reaction or resistance against lack of oversight and
increases short-term tolerance against miscalculations.83
This enables democracies and autocracies alike to make
fast and potentially miscalculated decisions over the short-
term; since most crises are inherently short-term, all regime
types become more likely to make misjudgements, despite
the fact that they operate in an OSINT-driven information
environment. Evidenced by the empirical studies,
authoritarian states too, suffer from audience costs in foreign
policy, and democratic foreign policies are not necessarily
more effective under information constraints compared to
autocracies.84
Amy B. Zegart, “The Domestic Politics of Irrational Intelligence Oversight,” Political Science Quarterly 126, no. 1 (March 1, 2011): 1–25,
https://doi.org/10.1002/j.1538-165X.2011.tb00692.x.
Emily Stewart, “Lawmakers Seem Confused about What Facebook Does — and How to Fix It,” Vox, April 10, 2018,
https://www.vox.com/policy-and-politics/2018/4/10/17222062/mark-zuckerberg-testimony-graham-facebook-regulations.
80
81
Michael M. Andregg and Peter Gill, “Comparing the Democratization of Intelligence,” Intelligence and National Security 29, no. 4 (July 4, 2014): 487–97,
https://doi.org/10.1080/02684527.2014.915174.
John R. Oneal and Anna Lillian Bryan, “The Rally ’round the Flag Effect in U.S. Foreign Policy Crises, 1950–1985,” Political Behavior 17, no. 4 (December 1, 1995): 379–401,
https://doi.org/10.1007/BF01498516.
Weeks, “Autocratic Audience Costs”; Branislav L. Slantchev, “Politicians, the Media, and Domestic Audience Costs,” International Studies Quarterly 50, no. 2 (June 1, 2006):
445–77, https://doi.org/10.1111/j.1468-2478.2006.00409.x; Jessica Chen Weiss, “Authoritarian Signaling, Mass Audiences, and Nationalist Protest in China,” International
Organization 67, no. 1 (January 2013): 1–35, https://doi.org/10.1017/S0020818312000380.
82
83
84
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Cyber Governance and Digital Democracy 2018/8
The wider digital OSINT debate concerns how technology is
changing the nature of state secrets and role of secrecy in
statecraft. Until an equilibrium is established, communication
technologies remain a battleground between states and
their respective societies, as well as among states. As
with past technological advances in communication –
printing press, radio, television, satellites – Internet-based
communication too, will enable signicant social forces
to push for greater liberties, and states, to repress such
forces. From the state point of view, OSINT will lead to two
outcomes. The short-term outcome will be a review of military
and intelligence policies to prevent leaks and exposures
through new communication tools. This will include simple
behavioural adjustments, from smart phone use, to social
media presence, including changing the way important
political secrets are encrypted and stored. Over the long-
term however, citizen-led crowdsourced OSINT initiatives
will continue to expose government secrets and especially
prevent states to dominate the narrative during crises and
emergencies. Democracies and authoritarian governments
alike will try to assert their own version of events, but will
nd it increasingly hard to establish a monopoly over
the framing and narrative of important events. This will
force governments either to suppress and block public
mechanisms of alternative information, or change the way
they utilize secrecy in statecraft. One example is how the
press, internal leaks and public pressure combined have
forced Bush-era detention facilities to be closed down under
the Obama administration, resulting in the 2015 outlawing
of the US Congress of all such facilities. However, similar
pressures over the exposure of the Russian downing of the
MAS17 airline in 2014 hasn’t changed Russian behaviour
– with the exception of restricting soldiers’ cell phone use
in combat zones. Similarly, the exposure of Russian soldier
seles in Crimea in 2014 had no effect on Russia’s wider
ambitions and operational course in Ukraine.
Therefore, it is unlikely that the advent of mass open-source
analytics will have the same effect on all states. Nor is
there evidence that OSINT will force all states to rely less
on secrecy. Most likely, digital OSINT will create a ‘secrecy
asymmetry’ between states – between those that have high
tolerance to audience costs (i.e. autocracies) and those that
are more responsive to them (democracies). Autocracies
will nd digital crowdsourced OSINT increasingly irrelevant
in the wider scheme of things (except perhaps in critical
operations) as leaks, exposure and citizen-led efforts can
be offset through domestic tools of repression; arrest, jailing
and censorship. Democracies on the other hand, will have
to follow a different trajectory. This trajectory consists of
alternative policy options that have to do with;
- Reforming public diplomacy agencies from a
unidirectional posture (i.e. delivery of state position to
the wider audience) to a multi-directional one, which
involves disseminating public view and sentiment to
government agencies, driving their adaptation to the
digital open-source environment.
- Co-opting a degree of civilian crowdsourced OSINT
into state intelligence efforts. This is less risky for more
representative and freer political systems, where the
amount of secrets that aren’t already public knowledge
is low. In contrast, this is hard for authoritarian
governments that tend to be ‘secrecy hoarders’ and
have much to lose (leak) through cooperating with
public OSINT platforms
- Yield to greater judicial and legislative oversight
in intelligence practice. By rendering intelligence
operations more open to, and cooperative with
safeguards, agencies can suffer less from audience
costs in case some of their secrets are exposed
through OSINT tools.
Over the long-term, the Internet and social media platforms
will settle into a business equilibrium where the states will
reassert their dominance over the ow of information, either
through controlling the big technology companies, or reaching
a power-sharing agreement that clearly denes jurisdictional
areas to minimize leaks and exposures. Until then, such
leaks and exposures will continue and will render states at
a disadvantage against civilian-led analytics initiatives, and
also create a new layer of security dilemma that will fuel
international security competition and intelligence agencies’
‘secrecy wars’. However, even in democracies, audience
costs must not be exaggerated given the fact that online
audience attention span is always limited and not directly
linked to policy engagement. Social media engagement
very rarely translates into actual political mobilization, and
it is only when such social media engagement ends up
creating a political, judicial or legislative momentum that
OSINT efforts lead to real change. To that end, OSINT will
increasingly nd it more useful to pick its ghts sparingly
and focus its efforts on issues that are likely to generate
wider public attention and policy momentum.
Conclusion: Implications for International Security
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Cyber Governance and Digital Democracy 2018/8
Ultimately, secrecy is not ending, but how we understand
and think about it is rapidly changing due to open-
information platforms. Events and facts that the states and
societies used to think as secrets, are no longer secrets.
This naturally brings about the necessity to rethink what to
hide and what to disclose on the Internet, along with how
to contain damage once these secrets are out. Until states
and citizens adapt to new communication and information-
extraction platforms, secrecy will remain a highly-blurred
concept and will affect all sides of the state-society debate.
22
Cyber Governance and Digital Democracy 2018/8
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Cyber Governance and Digital Democracy 2018/8
July 2018
H. Akın Ünver | EDAM, Oxford CTGA & Kadir Has University
Cyber Governance and Digital Democracy 2018/8
Digital Open Source Intelligence and
International Security: A Primer
... Human IntelligenceHuman intelligence (HUMINT) is a key component of tracking criminals on the Dark Web. HUMINT involves gathering information from human sources, such as informants or undercover agents, rather than relying solely on technical tools and analysis[99]. Here are some ways that HUMINT can be used to track criminals on the Dark Web:-Undercover Operations: Law enforcement agencies can use undercover agents to infiltrate criminal networks on the Dark Web. ...
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The Dark Web is the hidden group of Internet sites that can only be accessed through specific software. The Dark Web enables private computer networks to communicate anonymously without revealing identifying information. Keeping Internet activity anonymous and private can be beneficial for both legal and illegal applications. Although it is used to evade government censorship, it is also deployed for highly illegal activity. The aim of this paper is to provide a critical analysis of the technical, legal, and ethical challenges to policing the Dark Web. The most significant recommendation identified in this paper is the need for stronger national cyber security strategies, increased awareness and use of the UN Cybercrime Repository, and greater support from intergovernmental organisations. This would help to contribute towards addressing many of the technical, legal and ethical challenges concerning the multi-jurisdictional nature of Dark Web investigations and lack of reliable data and resources while ensuring transparency and accountability. The recommendations proposed in this paper are restricted by certain limitations, therefore, further research is recommended into the field of digital policing and the Dark Web.KeywordsThe Dark WebDigital policingDigital forensicsThe internetCyberspaceCybercrime
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A firsthand account and incisive analysis of modern protest, revealing internet-fueled social movements' greatest strengths and frequent challenges. To understand a thwarted Turkish coup, an anti-Wall Street encampment, and a packed Tahrir Square, we must first comprehend the power and the weaknesses of using new technologies to mobilize large numbers of people. An incisive observer, writer, and participant in today's social movements, Zeynep Tufekci explains in this accessible and compelling book the nuanced trajectories of modern protests-how they form, how they operate differently from past protests, and why they have difficulty persisting in their long-term quests for change. Tufekci speaks from direct experience, combining on-the-ground interviews with insightful analysis. She describes how the internet helped the Zapatista uprisings in Mexico, the necessity of remote Twitter users to organize medical supplies during Arab Spring, the refusal to use bullhorns in the Occupy Movement that started in New York, and the empowering effect of tear gas in Istanbul's Gezi Park. These details from life inside social movements complete a moving investigation of authority, technology, and culture-and offer essential insights into the future of governance.
Article
The so-called “gig-economy” has been growing exponentially in numbers and importance in recent years but its impact on labour rights has been largely overlooked. Forms of work in the “gig-economy” include “crowd work”, and “work-on-demand via apps”, under which the demand and supply of working activities is matched online or via mobile apps. These forms of work can provide a good match of job opportunities and allow flexible working schedules. However, they can also pave the way to a severe commodification of work. This paper discusses the implications of this commodification and advocates the full recognition of activities in the gig-economy as “work”. It shows how the gig-economy is not a separate silo of the economy and that is part of broader phenomena such as casualization and informalisation of work and the spread of non-standard forms of employment. It then addresses the issue of misclassification of the employment status of workers in the gig-economy. Current relevant trends are thus examined, such as the emergence of forms of self-organisation of workers. Finally, some policy proposals are critically analysed, such as the possibility of creating an intermediate category of worker between “employee” and “independent contractor” to classify work in the gig-economy, and other tentative proposals are put forward such extension of fundamental labour rights to all workers irrespective of employment status, and recognition of the role of social partners in this respect, whilst avoiding temptations of hastened deregulation.
Book
The Oxford Handbook of National Security Intelligence is about intelligence and national security. The text examines the topic in full, beginning with an examination of the major theories of intelligence. It then shifts its focus to how intelligence agencies operate, how they collect information from around the world, the problems that come with transforming "raw" information into credible analysis, and the difficulties in disseminating intelligence to policymakers. It also considers the balance between secrecy and public accountability, and the ethical dilemmas that covert and counterintelligence operations routinely present to intelligence agencies. Throughout, contributors factor in broader historical and political contexts that are integral to understanding how intelligence agencies function in our information-dominated age. The book is organized into the following sections: theories and methods of intelligence studies; historical background; the collection and processing of intelligence; the analysis and production of intelligence; the challenges of intelligence dissemination; counterintelligence and counterterrorism; covert action; intelligence and accountability; strategic intelligence in other nations.