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Introducing social media intelligence (SOCMINT)

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We introduce the latest member of the intelligence family. Joining IMINT, HUMINT, SIGINT and others is ‘SOCMINT’ – social media intelligence. In an age of ubiquitous social media it is the responsibility of the security community to admit SOCMINT into the national intelligence framework, but only when two important tests are passed. First, that it rests on solid methodological bedrock of collection, evidence, verification, understanding and application. Second, that the moral hazard it entails can be legitimately managed. This article offers a framework for how this can be done.
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Introducing Social Media Intelligence
(SOCMINT)
Sir David Omand , Jamie Bartlett & Carl Miller
Version of record first published: 28 Sep 2012.
To cite this article: Sir David Omand , Jamie Bartlett & Carl Miller (2012): Introducing Social Media
Intelligence (SOCMINT), Intelligence and National Security, 27:6, 801-823
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ARTICLE
Introducing Social Media Intelligence
(SOCMINT)
SIR DAVID OMAND,* JAMIE BARTLETT AND CARL MILLER
ABSTRACT We introduce the latest member of the intelligence family. Joining IMINT,
HUMINT, SIGINT and others is ‘SOCMINT’ – social media intelligence. In an age of
ubiquitous social media it is the responsibility of the security community to admit
SOCMINT into the national intelligence framework, but only when two important tests
are passed. First, that it rests on solid methodological bedrock of collection, evidence,
verification, understanding and application. Second, that the moral hazard it entails can
be legitimately managed. This article offers a framework for how this can be done.
Introduction
On Thursday 4 August 2011, Mark Duggan was shot and killed by a police
officer in Tottenham. By the morning of the 6th, social media channels
showed burgeoning hostility, peppered with explicit threats against the
police. From the 7th, social media information indicated the possible spread
of disorder to other parts of London, then England. Over the next few days,
content indicating criminal intent or action ratcheted in huge numbers
through both open-source social networking, such as Twitter, and closed
system networks, such as the BlackBerry Messaging Service and closed
groups such as chat forums. Similarly, huge numbers of messages appeared
trying to provide information to the police, either about an outbreak of
disorder or the identities of the people behind it.
1
In the aftermath, the police acknowledged that they had been
insufficiently equipped to deal with intelligence gathering via social media.
*Email: david.omand@kcl.ac.uk
1
Her Majesty’s Inspectorate of the Constabulary (HMIC), The Rules of Engagement: A
Review of the August 2011 Disorders (London: Crown Copyright 2011) especially pp.36–9.
Intelligence and National Security
Vol. 27, No. 6, 801–823, December 2012
ISSN 0268-4527 Print/ISSN 1743-9019 Online/12/060801-23 ª2012 Taylor & Francis
http://dx.doi.org/10.1080/02684527.2012.716965
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One intelligence professional said it was like ‘searching the British Library
for a page in a book without an index to refer to’.
2
Social media did not fit
into their systems of receiving, corroborating, prioritizing and disseminating
information, and therefore was not properly acted on. Her Majesty’s Chief
Inspector of Constabulary noted: ‘With some notable individual exceptions,
the power of this kind of media (both for sending out and receiving
information) is not well understood and less well managed’.
3
He concluded
that ‘the police have much to learn about social media, and the quickly
shifting modern communications of today’.
4
Since then, Government has reacted. The Metropolitan Police has
established a social media hub, in time for the London Olympics. A number
of police forces in the UK and elsewhere are believed to be trialling various
types of automated social media collection and analysis to collect
information to help criminal investigations and gauge the ‘temperature’ of
communities they are working with.
5
Police forces have used Flickr to crowd
source identifications of suspects from photographs. In the UK, the Ministry
of Defence’s Cyber and Influence Science and Technology Centre has
released a number of calls for research to develop capabilities including
‘cyber situational awareness’, ‘influence through cyber’ and ‘social media
monitoring and analysis: crowd sourcing’.
6
Underlying these developments
is significant planned public investment in the capabilities that will allow the
authorities to continue to access communications data and access under
warrant where necessary the content of internet communications including
social media. In the UK, new legislation has been proposed to ensure law
enforcement agencies maintain the ability to tackle crime and terrorism as
criminals use modern technology and new ways of communicating in the
digital space to plan and commit crime.
This rapid growth of interest by law enforcement in intelligence derived
from social media (which we term SOCMINT) prompts questions about the
methodological and ethical framework within which it will be used. Public
acceptability lies at the heart of any form of intelligence collection, and this
can only be secured if SOCMINT is properly used and properly authorized.
This article suggests a framework for how this can be achieved.
2
Ibid., p.31.
3
Ibid., p.30.
4
Ibid.
5
‘Facebook Crimes Probed by Humberside Police’, Hull Daily Mail, 24 August 2011,
5www.thisishullandeastriding.co.uk/Facebook-crimes-probed-Humberside-Police/story-131
91231-detail/story.html4(accessed 17 April 2012); see also Westminster City Council’s
‘Your Choice’ programme: Choose Life, Not Gangs: Problem Kids Told to Clean Up or Face
the Consequence (City of Westminster, 29 September 2011), 5www.westminster.gov.uk/press-
releases/2011-09/choose-life-not-gangs-problem-kids-told-to/4(accessed 17 April 2012).
6
Ministry of Defence and Centre for Defence Enterprise, Cyber and Influence Science and
Technology Centre, CDE Call for Research Proposals, 1 November 2011, 5www.science.
mod.uk/controls/getpdf.pdf?6034(accessed 17 April 2012).
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The Age of Social Media
We live in the age of social media. Facebook, Twitter, Googleþand LinkedIn
are all examples of the rapid transfer of people’s lives – interactions,
identities, arguments and views – onto a new kind of public and private
sphere; a vast digital social commons. This transfer is happening on an
unprecedented scale. On Facebook alone, 250 million photos are added per
day,
7
as are 200 million tweets on Twitter.
8
There are four billion video
views per day on YouTube.
9
As people transfer more of their lives onto social media platforms, they
become an increasingly significant public space, and therefore of interest to,
and used by, public bodies. Understanding the content of social media
presents an opportunity for public bodies better to understand, and respond
to, the public they serve. Public health experts are learning to scan tweets
and search requests to identify pandemics earlier than traditional methods.
10
US psychologists believe Facebook contains valuable indicators of mental
health, and indeed the social media profiles of a number of participants in
school shootings, such as the suspect in the Ohio School Shooting, TJ Lane,
seem to show some indicative content.
11
The United Nations believes that
tapping into social media can help tackle global unemployment and food
insecurity.
12
Social media spaces are also now significantly relevant to security and
public safety. Facebook has been used to try to hire hitmen, groom the
targets of paedophiles, violate restraining orders, steal identities and fatally
cyberbully victims.
13
Al-Qaeda’s Somali affiliate Al-Shabaab runs a twitter
account, whilst pirates operating in the Gulf of Aden use blogs, Twitter and
7
‘The Value of Friendship’, Economist, 4 February 2012, 5http://www.economist.com/node/
215460204(accessed 17 April 2011).
8
Twitterblog, 200 Million Tweets a Day, 30 June 2011, 5http://blog.twitter.com/2011/06/
200-million-tweets-per-day.html4(accessed 17 April 2012).
9
YouTube, Statistics,5www.youtube.com/t/press_statistics4(accessed 17 April 2012).
10
A. Signorini, A.M. Segre and P.M. Polgreen, ‘The Use of Twitter to Track Levels of Disease
Activity and Public Concern in the US During the Influenza A H1N1 Pandemic’, PLoS ONE
6/5 (2011) pp.1–10.
11
J. Hoffman, ‘Trying to Find a Cry of Desperation Amid the Facebook Drama’, New York
Times, 23 February 2012, 5www.nytimes.com/2012/02/24/us/facebook-posts-can-offer-
clues-of-depression.html?_r¼34(accessed 17 April 2012); ‘T.J. Lane Facebook Photos:
Suspect Faces Charges in Chardon High School Shooting (Slideshow)’, Huffington Post,28
February 2012, 5www.huffingtonpost.com/2012/02/28/tj-lane-facebook-photos_n_1307836.
html#s736080&title¼TJ_Lane_Facebook4(accessed 17 April 2012).
12
For instance the UN Global Pulse Programme, UN Unveils Initial Findings on Uses of
Real-time Data for Development Work (UN News Centre, 8 December 2011), 5www.un.
org/apps/news/story.asp?NewsID¼40667&Cr¼global&Cr1¼pulse4(accessed 17 April
2012).
13
Criminal Justice Degrees Guide, 20 Infamous Crimes Committed and Solved on Facebook,
5http://mashable.com/2012/03/01/facebook-crimes/4(accessed 1 July 2012).
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Facebook to plan and coordinate with each other.
14
The Daily Mail reported
that 12,300 alleged offences were linked to Facebook in 2011.
15
When society develops and adopts new methods of communication and
organization – such as social media – public institutions, including the police
and intelligence services, have a responsibility to react and adapt. The
explosion of social media is the latest in a long line of disruptive
technological innovations, and now requires a response from the authorities
in turn.
The Opportunity of SOCMINT
Measuring and understanding the visage of millions of people digitally
arguing, talking, joking, condemning and applauding is of wide and
tremendous value to many fields, interests and industries. A family of ‘big
data’ approaches already exists to make sense of social media. Known as
social media analytics, these tools constitute a broad church, ranging from
advertisers listening to social media ‘buzz’ to track attitudes surrounding
their brands and companies monitoring their social media reputation, to
mapping ‘social graphs’ of relationships between people, to drawing
‘wisdom of the crowd’ solutions to emergency situations, to conducting
linguistic analysis of forum posts and network analysis of Twitter users.
Fledgling academic efforts have used social media to inform investments into
hedge funds.
16
Looking at the current SOCMINT technologies now on the horizon – as
well as the threats we now face – the following capabilities could for
example contribute in the future to public security:
.Crowd-sourced information. This could help ensure a better flow of
information between citizens and the government, especially in times of
emergency.
17
With access to social media, passive bystanders can become
active citizen journalists, providing and relaying information from the
ground. The report by Her Majesty’s Inspectorate of Constabulary into
the riots notes, for example, a messaging service on West Midlands
14
Diego Laje, ‘#Pirate? Tracking Modern Buccaneers Through Twitter’, CNN, 15 March 2012,
5http://edition.cnn.com/2012/03/15/business/somalia-piracy-twitter/index.html4(accessed 1
June 2012).
15
Jack Doyle, ‘A Facebook Crime Every 40 Minutes’, Daily Mail, 4 June 2012, 5http://
www.dailymail.co.uk/news/article-2154624/A-Facebook-crime-40-minutes-12-300-cases-linked-
site.html4(accessed 1 June 2012).
16
T.O. Sprenger and I.M. Welpe, Tweets and Trades: The Information Content of Stock
Microblogs, 1 November 2010, 5http://papers.ssrn.com/sol3/papers.cfm?abstract_id¼
17028544(accessed 17 April 2012).
17
Twitter was used by pupils as an ad hoc emergency broadcasting system during the Ohio
school shooting. See L. Dugan, ‘Twitter Used as an Impromptu Broadcast System During Ohio
School Shooting’, Media Bistro,28February2012,5www.mediabistro.com/alltwitter/twitter-
used-as-impromptu-emergency-broadcast-system-during-ohio-school-shooting_b190304(ac-
cessed 1 June 2012).
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Police’s website, which allowed citizens to post messages and questions,
allowing the police to build up a picture of the situation on the ground in
real-time, as well as allowing people to identify pictures of suspects
uploaded to the site.
18
Tapping into the ‘wisdom of the crowds’ is already
of great, demonstrated value. For example, the open-source platform
Ushahidi has allowed large groups of people to provide collective
testimony on everything from the earthquake in Haiti to blocked roads in
Washington, DC.
19
These applications, impressive as they are, are only
the beginning, and the stronger the techniques to make sense of
information of this kind, scale and dynamism, the more effective the
responses, from providing snow ploughs to drinking water, that can
be made.
.Research and understanding. Research based on social media could
contribute to our understanding of a number of phenomena. This could
include the thresholds, indicators and permissive conditions of violence;
pathways into radicalization; an analysis of how ideas form and change;
and investigation of the socio-technical intersections between online and
offline personae. Beneath the tactical and operational level, a background
of more generic and distanced understanding is important for security
work. For instance, the British counter-terrorism strategy aims to reduce
the threat from terrorism so that people can go about their normal lives,
freely and with confidence, and it is understood that the long-term way to
do this is through tackling the underlying social, ideational and political
causes of terrorism.
In addition, the rise in use of social media, together with the rapid
development of analytics approaches, now provides a new opportunity for
law enforcement to generate operational intelligence that could help identify
criminal activity, indicate early warning of outbreaks of disorder, provide
information and intelligence about groups and individuals, and help
understand and respond to public concerns. Some of this access will come
from ‘open source’ information derived from Twitter and other social
media content authorized for public access. Some, however, will require
legal authorization to override privacy settings and encryption of commu-
nications. We can group the advantages of such operational exploitation in
terms of:
.Near real-time situational awareness. This is the ability to collect and
cluster social media and output in a way that indicates and describes
unfolding events. Analysis of Twitter has shown that, while the majority
of Twitter traffic only occurred after an event had been reported by a
18
HMIC, The Rules of Engagement, p. 31.
19
J. Howe, ‘The Rise of Crowdsourcing’, Wired, June 2006, 5www.wired.com/wired/
archive/14.06/crowds.html4(accessed 17 April 2012).
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mainstream news outlet, ‘bursts’ of tweets indicating a significant event
often pre-empt conventional reporting.
20
Social media traffic analysis
could allow for a more rapid identification of emerging events than
traditional reporting mechanisms. With the application of geo-location
techniques this could lead, for example, to a constantly evolving map
showing spikes in possible violence-related tweets, facilitating a faster,
more effective, and more agile emergency response.
.Insight into groups. This would include the ability to better understand
activities and behaviour of certain groups already of interest to police or
intelligence agencies. Given the appropriate legal authorization, the
police could use SOCMINT to spot new, rapidly emerging ‘hot topics’
that spring up within group-specific conversations and how the group
reacts to a specific, perhaps volatile, event. Through these and other
techniques, SOCMINT might indicate the overall levels of anger within a
group, and their key concerns and themes that animate intra-group
discussions. At a higher level of specificity, information can also be
identified and extracted regarding when a group is planning demonstra-
tions or flashmobs, which could lead to violence or increasing community
tensions; football fans planning ‘meets’, which could cause major
economic disruption; groups planning counter-demonstrations, which
could change the kind of policing required to maintain public order.
.Identification of criminal intent or criminal elements in the course of an
enquiry both for the prevention and prosecution of crime. Similarly, law
enforcement could use the warranted surveillance of social media use by
individuals suspected of involvement in a crime or criminal conspiracy,
the cross referencing of such individuals’ accounts, the identification of
accomplices, the uncovering of assumed identities, the identification of
criminal networks that operate through social media sites, and the
provision of social media content suspected of being evidence of a crime
to the Crown Prosecution Service.
Such potential suggests that SOCMINT will merit a significant place in the
national intelligence framework. However, whenever a new form of
technology emerges, it takes some time before legitimate and rigorous
systems of capture, analysis and interpretation are developed. There are a
number of key challenges that need to be addressed before SOCMINT can
be fully exploited in the interest of national and public security. It is to these
challenges, and their suggested solutions, that we turn next.
The Challenges of SOCMINT: Necessity and Legitimacy
The full promise of SOCMINT as a law enforcement tool in addition to its
use as an open source of information must be tempered against the reality
20
‘Reading the Riots: Investigating England’s Summer of Disorder’ [interactive], Guardian,
5www.guardian.co.uk/uk/interactive/2011/aug/24/riots-twitter-traffic-interactive4(accessed
17 April 2012).
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that the methods employed to protect society rest ultimately on some form
of public acceptability and involvement. Britain’s National Security Strategy
recognizes that security and intelligence work in general is predicated not
only on the public’s consent and understanding, but also on the active
partnership and participation of people and communities. Serious and
recognized damage to security occurs when the state’s efforts are not
accepted or trusted.
21
Public acceptability can be secured and maintained through two
important public demonstrations. First, that the collection of intelligence
is able to make an effective and necessary contribution toward safety and
security; second, that this contribution is being proportionately and
appropriately balanced against other desirable public goods – such as the
right to private life. In sum, intelligence activity must effectively contribute
to a public good but not detract from or threaten any others in ways that are
not recognized and appropriately managed. These are the challenges of
necessity and legitimacy.
Necessity
The first demonstration that the use of SOCMINT must make is that it
works. If it did not have a reasonable prospect of contributing towards
public safety there would be no moral, or indeed financial, argument for it to
be collected or used. If SOCMINT is not efficacious, it risks harm to suspects
who turn out to be innocent, risks of collateral damage to others to whom a
duty of care is owed, and the risk of confounding otherwise sound
intelligence efforts.
The ‘success’ of intelligence is not the information or even secrets that it
collects, but the value it adds to decision-making. Indeed, the justification for
creating SOCMINT (or any intelligence) capabilities and applying them,
with all the recognized hazards this entails, is that it contributes to the public
good of safety and security. It is therefore morally imperative that
SOCMINT operations present a reasonable chance that they will yield
actionable, useable intelligence that contribute to consequential decisions,
such as deploying emergency services to the right place at the right time.
For information to be considered successful ‘intelligence’ it needs to meet
certain thresholds of how it is gathered, evidenced, corroborated, verified,
understood and applied. Different sources and kinds of information have
developed signature ways of meeting this challenge. For example, open-
source intelligence (OSINT) triangulates reliable sources; human intelligence
(HUMINT) might consider the track record of the agent; imagery
intelligence (IMINT) needs to pay attention to the technical characteristics
of the collection platform; and signals intelligence (SIGINT) would need to
understand the context of the language used. All source intelligence
21
Cabinet Office, A Strong Britain in an Age of Uncertainty: The National Security Strategy
London: HMSO 2010) p.5.
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assessments try to get an overall picture on the basis of these different types
and reliability of contribution.
SOCMINT has its own characteristics that can be seen by examining how
SOCMINT fits into the traditional intelligence cycle, in particular the
functional steps of collection, processing and analysis, and dissemination.
Data access. One of the difficulties of SOCMINT which it shares with much
of modern SIGINT is not a paucity of data, often the key problem in the
collection of Cold War secret intelligence, but a deluge. The term ‘access’ is
preferred over ‘collection’ to indicate that in the internet we are dealing with
a very different process from that of traditional intelligence gathering.
During the week of the August 2011 riots, for example, millions of riot-
related tweets were sent, consisting of news stories, rumours, reactions and
indications of criminal intent.
22
Knowing what data ought to have been
accessed and analyzed – sorting the wheat from the chaff – is the critical
consideration. Selection and filtering tools (including such techniques as
semantic search) are available and have, for example, been extensively used
in signals intelligence and in email searches and legal discovery and will be
needed for social media analysis by law enforcement of their targets.
Rather different problems arise for the analyst when trying to distil
general meaning from large open data sets. One useful way to approach this
challenge is to draw on how traditional quantitative disciplines deal with
overwhelmingly large data sets. Most turn to the statistical technique of
sampling, wherein a manageable amount of data is collected that represent
the unmanageably large ‘population’ being researched. The reliability and
validity of inferences or extrapolations made on this basis depend on the
quality, especially representativeness, of the sample collected. Over the past
century, statisticians have developed techniques that allow small data sets to
be representative, and therefore permit more general conclusions to be
drawn – particularly through the use of randomized sampling. Simply put,
however, inferences and conclusions can only be reasonably drawn if one
knows how the sample was constructed and the data collected, and what this
means about the inferences that are then made.
The broad problem is that social sciences have not developed an approach
to robustly sample social media data sets. Only a very limited amount of
work has been done to develop different types of sampling for automated
systems of data collection.
23
More attention has been paid to methodologies
that produce a large sample (something that computational approaches are
22
R. Proctor, F. Vis and A. Voss, ‘Riot Rumours: How Misinformation Spread on Twitter
During a Time of Crisis’, Guardian, 7 December 2011, 5www.guardian.co.uk/uk/
interactive/2011/dec/07/london-riots-twitter4(accessed 17 April 2012).
23
See for instance J. Leskovec, J. Kleinberg and C. Faloutsos, ‘Graph Evolution: Densification
and Shrinking Diameters’, ACM Transactions on Knowledge Discovery from Data 1/1
(2007) 5www.cs.cmu.edu/*jure/pubs/powergrowth-tkdd.pdf4(accessed 16 April 2012); J.
Leskovec and C. Faloutsos, ‘Sampling from Large Graphs’ in T. Ellassi-Rad (chair),
Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery
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good at delivering), rather than methodologies that produce a representative
one (something social sciences are good at delivering). Moreover, the
emerging, highly technical and computer science-driven developments in
social media sampling practices, including ‘forest-fire’ (wherein links and
nodes ‘burn’ outward from a random seed to create the sample), user-
activity and location-attribute-based techniques, have had very little uptake
within the social science community.
Very little research based on social media data sets acknowledges the
sampling frame applied, and how this might limit or bias the results that are
drawn. Typical data acquisition strategies within the small but growing field
of academic work of remain ‘samples of convenience’ or ‘incidental
sampling’, which means the most readily available or easily accessible –
rather than the most representative – are collected.
24
For obvious reasons
this type of sampling limits the strength of conclusions drawn. One
prominent example is the recent Reading the Riots collaboration involving
the Guardian, a number of British universities, freelance journalists and
community researchers. The project gathered 2.6 million tweets about the
August 2011 riots and drew a number of conclusions, including that there
were very few examples of Twitter being used to express or encourage
criminal activity. However, the data set of tweets was collected using around
150 ‘hashtag clouds’, which means only tweets that included an identifying
hashtag, such as #londonriots, were collected and analyzed. It is possible,
however, that people who use a hashtag when tweeting are not
representative of all tweets about the riots; for example, they might be less
likely to talk about criminal activity because hashtags are usually employed
by users to disseminate tweets to a wider audience. In statistics, this is
known as ‘missing at non-random data’, which means certain data might be
systemically absent as a result of the sampling method. This is considered a
serious problem when drawing conclusions, because when people are absent
from a data set for a reason other than chance, they share a related and likely
important trait (or traits) that could have a substantial impact on the
research findings.
Unlike in traditional social science research, technical considerations
might also affect the quality of sample. For example, in the case of Twitter,
the publicly available application programme interface is limited to 150
requests per hour, going up to 20,000 when it is ‘whitelisted’. While this can
capture an enormous data set by traditional social science standards, it can
only capture a small amount, and not an automatically representative
sample, of the total number of tweets. Researchers can gain access to larger
proportions of the tweet-feed. The ‘firehose’ gives access to all tweets, the
and Data Mining,2006 (Philadelphia: KDD 2006), 5www.stat.cmu.edu/*fienberg/Stat36-
835/Leskovec-sampling-kdd06.pdf4(accessed 16 April 2012).
24
See, for instance, B. O’Connor, R. Balasubramanyan, B.R. Routledge and N.A. Smith,
‘From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series’, Proceedings of
the AAAI Conference on Weblogs and Social Media (Washington, DC: AIII Press 2010). The
authors collected their sample using just a few keyword searches.
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‘gardenhose’ gives 10 per cent, and the ‘spritzer’ gives 1 per cent.
Unfortunately, precisely how these different access levels affect the quality
of the data, and what sorts of systemic bias they might hide, are not fully
known – and very rarely stated.
For the interests of public policy, including security strategies, what are
especially required are data acquisition plans that allow online phenomena
to be related to offline behaviour. Demographic representativeness is key for
this. The people who use social media such as Twitter and Facebook tend to
be younger, richer, more educated and more urban than the population in
general.
25
Additionally, when looking at demography, it is not only the
general population that is important, but also the community that accounts
for the information that is gathered. Online social content is subject to the
enduring influence of the Pareto principle of the ‘vital few’ that 80 per cent
of the user-generated content for any given site will tend to come from a
highly productive 20 per cent of users.
26
A 2010 study of Twitter found this
to be broadly true: 22.5 per cent of users accounted for around 90 per cent of
all activity.
27
Processing and analysis. As described in the previous section, drawing
meaning from open social media data presents great challenges for the
intelligence analyst. Many of the technologies that have been developed in
the private sector by the advertising and public relations sectors have been
moulded by the metrics traditions and to suit the needs of these industries.
They aim to gain a general understanding of attitudes toward a product or
whether a new advertising campaign is creating a ‘buzz’. Security and
intelligence efforts, however, demand modes of analysis that can deliver
levels of confidence that these technologies cannot, yet, deliver.
Because social media data sets are so large, a number of broadly
computational approaches have been developed to infer and extract
‘meaning’ automatically, without the routine presence of a human analyst.
The most important approach is a variant of artificial intelligence – ‘machine
learning’ – where algorithms are taught to recognize patterns and therefore
meaning within pieces of information that human beings need therefore
never see. Machine learning has a number of important applications, from
identifying clusters and anomalies in large data sets to the extraction of
semantic information from text. A particularly important application is
‘sentiment analysis’, where an algorithm looks for certain qualities and
properties in a piece of text that it considers to correlate statistically with an
emotion or ‘sentiment’. Once human input has defined what sentiments are
25
For information Twitter and Facebook demographics see, ‘Infographic: Facebook vs.
Twitter Demographics’, Digital Buzz Blog, 21 December 2010, 5www.digitalbuzzblog.com/
infographic-facebook-vs-twitter-demographics-2010-2011/4(accessed 16 April 2012).
26
See C. Shirky, Here Comes Everybody: The Power of Organizing Without Organizations
(New York: Penguin 2008).
27
‘Twitter Statistics for 2010’, Sysomos, December 2010, 5www.sysomos.com/insidetwitter/
twitter-stats-2010/4(accessed 16 April 2012).
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being searched for, and what textual examples of these sentiments are, the
algorithm is able, with varying degrees of specificity and accuracy, to classify
enormous volumes of data automatically on the same basis. Sentiment
analysis has been applied for a number of aims, from measuring Twitter
users’ feelings towards political parties to predicting the future of box office
revenues.
28
In the future we may see sentiment analysis used by the police to
gauge the mood of demonstrators and the possibility of criminal violence
erupting.
The ability to extract automatic meaning from unstructured data such as
tweets opens many research opportunities, and social researchers can now
contemplate handling bodies of information, and compiling sample sets, on
a previously unmanageable scale. However, a crucial consequence of the rise
of machine learning approaches within social media analytics is that we are
currently much better at counting examples of online human behaviour than
critically explaining why they are and what it might mean.
To make this sort of sense of any form of communication, context is
critical. A central tenet of all semiotics and linguistics is that language is
textured: the intent, motivation, social signification, denotation and
connotation of any utterance is mutable and dependent on the context of
situation and culture. The accuracy of any interpretation depends on a very
detailed understanding of the group or context that is being studied. For
example, most groups of people use vernacular and group-specific language
that a generic or standardized sentiment lexicon or thesaurus would often
misinterpret.
However, because automatic data collection is required to process the
sheer volume of data now available, many of the contextual cues – the
thread of a conversation, information about the speaker, the tone of the
utterance and the information about the speaker – are often lacking in
analysis of social media data. Therefore utterances have to be abstracted out
of the wider situational, contextual and cultural picture – what we would
call their ‘naturalistic setting’. The act of ‘scraping’ a social media platform –
such as collecting tweets or Facebook posts – like filtered selection in signals
intelligence usually does not by itself provide the utterance’s position in a
social network (such as whether they were talking to their friend) or a
conversational network (such as whether the utterance was a heated rebuttal
in an argument). Again, these are issues that have been faced by signals
intelligence agencies in the internet age and where analytic experience and
judgment have been shown to be key.
Context is also shaped by the norms and mores of the medium we are
using. A number of studies are beginning to identify norms, rules and
behaviours that dictate communication via social media that differ in
28
J. Weng, Y. Yao, E. Leonardi and F. Lee, ‘Event Detection in Twitter’, HP Laboratories,6
July 2011, 5www.hpl.hp.com/techreports/2011/HPL-2011-98.html4(accessed 17 April
2012); S. Asur and B.A. Huberman, ‘Predicting the Future With Social Media’, HP
Laboratories,29March2010,5www.hpl.hp.com/research/scl/papers/socialmedia/socialme-
dia.pdf4(accessed 17 April 2012).
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significant ways to how people might communicate offline. Some studies for
example argue for an ‘online disinhibition effect’ – that the invisible and
anonymous qualities of online interaction lead to disinhibited, more intensive,
self-disclosing and aggressive uses of language.
29
Identification with groups or
movements has also changed. Traditional forms of membership to a group or
a movement are relatively intense, often involving subscription fees and
membership lists. For many online groups, however, a single click of a mouse
is sufficient to express some form of affiliation. This is a more ephemeral,
looser and possibly less involved form of affiliation. Indeed, a recent study of
1300 Facebook fans of the English Defence League found that only three-
quarters considered themselves ‘members’ of the group, and only one-quarter
of those had ever actually been on a march.
30
Taken together, these phenomena constitute the rapid emergence of
distinct social media sub-cultures, which are developing new understand-
ings, social mores and uses of language in clearly distinct ways.
31
Indeed, a
new branch of sociology – digital sociology – is devoted to understanding
these socio-cultural consequences of the internet and the new ways it is
being used.
When context is not considered, there can be profound consequences and
potential for misinterpretation. In 2010, Paul Chambers declared to his 650
Twitter followers his intention of ‘blowing [Robin Hood] airport sky
high!!’
32
Undoubtedly in jest, his initial conviction for the ‘menacing use of a
public communication system’ under the Communications Act 2003 has
attracted wide public criticism and was overturned on appeal. Jonathan
Bennett, the district judge, noted the ‘huge security concerns’ within the
context of the times in which we live, but perhaps not the Twitter-specific
situational and cultural context of the utterance.
33
In a similar case, Leigh
Van Bryan and Emily Bunting were denied entry to America after tweeting
‘free this week for a quick gossip/prep before I go and destroy America? x’.
34
Although there are no simple solutions to these difficulties, some steps
forward are possible. First, big data computational tools must become more
‘human-sized’ – sympathetic to the human subject they wish to measure.
29
J. Suler, ‘The Online Disinhibition Effect’, Journal of Cyberpsychology and Behaviour 7/3
(2004) pp.321–6. See also J. Suler, The Psychology of Cyberspace: The Online Disinhibition
Effect,5http://users.rider.edu/*suler/psycyber/disinhibit.html4(accessed 17 April 2012).
30
J. Bartlett and M. Littler, Inside the EDL (London: Demos 2011).
31
‘Twitterology High and Low’, The Economist, 31 October 2011, 5www.economist.com/
blogs/johnson/2011/10/technology-and-language?fsrc¼scn%2Ftw%2Fte%2Fbl%2Ftwitterolo
gyhighandlow4(accessed 16 April 2012).
32
‘Robin Hood Airport Tweet Bomb Joke Man Wins Case’, BBC News, 27 July 2012,
5www.bbc.co.uk/news/uk-england-190093444(accessed 16 April 2012).
33
‘Jack of Kent’ (David Allen Green), Paul Chambers: A Disgraceful and Illiberal Judgment,
11 May 2010, 5http://jackofkent.blogspot.com/2010/05/paul-chambers-disgraceful-and-
illiberal.html4(accessed 16 April 2012).
34
A. Parker, ‘US Bars Friends over Twitter Joke’, Sun, 30 January 2012, 5www.thesun.co.
uk/sol/homepage/news/4095372/Twitter-news-US-bars-friends-over-Twitter-joke.html4(ac-
cessed 16 April 2012).
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Statistical analytical methods, such as sentiment analysis, must involve
analysts and experts who understand the norms and behaviours of the
groups involved. Second, any analysis of social media data sets should
always be based on an understanding of the medium itself: the specific online
culture, language and behaviour. Project Raynard is a good and relatively
recent of an intelligence programme that stresses the importance of first
establishing norms in online environments before looking for deviations
from the norm.
35
Most important is that any organization using SOCMINT
must recognize the analytical and interpretative limitations of the field, how
they reflect on the kind of insight that can be drawn, and the kind of
decisions that can be made on the basis of those limitations.
Dissemination. The effective use of intelligence from internet sources
including social media data sets also depends on it getting to the right people
quickly, securely and presented in a format that makes sense to strategic and
operational decision-makers. Depending on the purpose of the SOCMINT,
this may range from a footnoted, caveated and in-depth strategic analysis
paper, to the operational use of single-screen, real-time visualizations of data
available on portable devices.
36
Several challenges will need to be addressed. First, SOCMINT dissemina-
tion must reflect the general difficulties in using SOCMINT: its complexity,
scale, dynamism and – given the problems outlined above relating to both
access and interpretation – any presentation of data needs to be presented
with new procedures and caveats.
Second, SOCMINT dissemination must slot into existing intelligence
channels – police, emergency service response, the Security Service, the Joint
Terrorism Analysis Centre, Cabinet Office Assessments Staff and so on.
However, this requires specific training for gold, silver and bronze
commanders and additional training for frontline officers who could benefit
from the daily use of such intelligence but who are not today regarded as
direct intelligence customers.
Third, SOCMINT dissemination and retention must comply with the
highest standards of information assurance. Existing controls must be
applied to ensure the safekeeping of SOCMINT data accessed under warrant
35
Intelligence Advanced Research Projects Agency, Broad Agency Announcement: Reynard
Program, 16 June 2009 5http://www.iarpa.gov/solicitations_reynard.html4(accessed 12
June 2012).
36
For instance, in deprived areas of Berlin, civil servants have increasingly used portable devices
connected to database records when visiting care homes for the elderly and hospitals. These
devices give constant, mobile access to databases, enabling public servants to understand the
needs of individuals and families, track their previous contact and check for problems and
underlying issues that may have been recorded by other agencies. See J. Millard, ‘eGovernance
and eParticipation: Lessons from Europe in Promoting Inclusion and Empowerment’, paper
presented to UN Division for Public Administration and Development Management (DPADM)
workshop ‘e-Participation and e-Government’ (Budapest, Hungary, 27–28 July 2006),
5unpan1.un.org/intradoc/groups/public/documents/UN/UNPAN023685.pdf4(accessed 23
January 2012).
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and regulating their dissemination, including overseas. The unregulated
dissemination of SOCMINT data would risk jeopardizing public confidence
in this form of intelligence. More generally, whether lost, insecurely held, or
subject to hostile access, as government increases the amount of personal
information it holds, the potential for various data compromises, and the
harm it might cause to public confidence, will inevitably grow.
Fourth, the effective application of data visualization techniques will be
required to render complex and often interlinked intelligence in a way that is
intuitively comprehensible, but conserves the networked nature of the
information. More experience of using such SOCMINT data analysis
techniques by law enforcement in particular is needed in order to draw up
detailed rules and regulations for its safe management.
Validation and use. The way SOCMINT can add value relates to how the
operators – such as frontline police officers – will actually use the
information, and how they ought to interpret and act on it (such as
deploying reserve forces in the build up to a march). In addition to the many
methodological hurdles that stand in the way of the responsible interpreta-
tion of data, the social media being monitored is itself prone to contain
misleading information of a polemical nature, which may involve the
recirculation of selective half-truths, mistakes and outright distortions.
Validation of SOCMINT intelligence is therefore an important function for
the social media analyst.
One risk that must be accounted for when considering validating
SOCMINT data is the risk of engineering the ‘observation effect’: the
tendency of individuals to change their behaviour if they believe they are
being observed. In 2009, the LSE’s Policy Engagement Network warned of
this ‘effect’ in a briefing paper responding to the then-government’s
Interception Modernisation Programme. The report feared that when the
public became aware that communications data were being collected and
collated, there would be a risk that ‘it will generate a chilling effect on the
individual’s right to free expression, association and might dissuade people
from participating in communications transactions’.
37
On the other hand,
previous predictions such as the decline of communications intelligence due to
the ready availability of hard encryption have not proved correct, and
similarly it is unlikely that changes in public behaviour as a result of
knowledge of social media monitoring will significantly limit the effectiveness
of social media and online communications data as sources of intelligence.
Related to this issue is the problem of ‘gaming’ – the deliberate use of this
media as a means of misleading or confusing an observer, in this case the law
enforcement agencies. In the context of intelligence work, this problem is not
new, although experience such as the Allied deception operations in the
Second World War illustrates the care with which deception operations have
to be planned if they are not to backfire on the originator. The nature of
37
Briefing on the Interception Modernisation Programme, Policy Engagement Network Paper
5(2009), p.56.
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SOCMINT may make attempts at deception more likely given the ubiquity
of social media, its widespread use and the democratization of computer
coding and technical know-how. In a recent example, a leaked cache of
emails allegedly belonging to Bashar al-Assad indicated that an aide, Hadeel
al-Assad, posted pro-regime commentary under an assumed Facebook
identity that was mistaken as genuine and given international coverage by
CNN.
38
For these reasons, there must be a thorough (yet sufficiently rapid) process
to ensure that an item of SOCMINT can, as far as possible, be validated
before it reaches the final user. The validation of SOCMINT is ideally done
further up the ‘food chain’ from the functions of access and processing of
data when all sources of intelligence, including open source material, can be
brought to bear.
As with other intelligence sources, this validation process must take the
form of a reporting framework that rates the ‘confidence’ in any piece of
freestanding piece of SOCMINT. By pointing out potential vulnerabilities
and biases in the acquisition and analysis of the information, we may gauge
the importance of the information collected and caveat the conclusions that
may be drawn.
We must also be able to relate SOCMINT to other kinds of evidence to
produce an overall picture – the equivalent of an ‘all-source assessment’. The
value of SOCMINT relative to other forms of intelligence must be evaluated
and the ways in which various types of intelligence can be used in tandem
needs to be investigated. The crucial points here are the exact application of
SOCMINT in a given circumstance and its ‘strength’ in relation to other
forms of intelligence. To complicate the issue, both will of course vary
according to the situation ranging from identifying broad societal-level
trends on the one hand and the context of a riot or crowd control on the
other.
A number of strategies will be useful to create processes to validate
SOCMINT. More methodologically mature forms of offline research can be
conducted in parallel to SOCMINT projects to allow the results to be
compared. For example, it would be especially useful to establish rules about
how online phenomena maps onto offline behaviour. Retrospective analysis
can also be used to quantify SOCMINT accuracies and diagnose instances
where accuracy was confounded. In addition to the specific validation
responsibilities placed on the agency that collected the intelligence, there
needs to be a very general up-skilling of all the branches of government that
might be involved in this work. It will be impossible to use this medium
without analysts, police officers or judges who understand its norms and
mores. Ultimately, the value of SOCMINT can only really be understood
through using it. Understanding will slowly emerge as SOCMINT is trialled –
we must expect varying degrees of success in different contexts.
38
‘Shopping Amid a Massacre: Leaked E-mails from Syria’s Regime’, CNN International,16
March 2012, 5http://edition.cnn.com/2012/03/15/world/meast/syria-al-assad-e-mails/index.
html?iphoneemail4(accessed 16 April 2012).
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In general, the origin of the main risks in using information from social
media arises from a lack of interaction between the humanities and the
statistical and computational disciplines. Those disciplines best equipped to
understand and explain human behaviour – the social and behavioural
sciences, political science, psephology, anthropology and social psychology –
have not kept pace in relating this insight to the big-data approaches
necessary to understand social media. Conversely, these very same big-data
approaches that form the backbone of current SOCMINT capabilities have
not used sociology to employ the measurements and statistics they use to the
task of meaningfully interpreting human behaviour.
Taken together, the methodological steps forward point towards a
fundamental evolution in the capabilities available to exploit social media.
If SOCMINT is to be methodologically robust enough to base decisions on
and change policy, it must rest on a new, applied, academic inter-discipline:
social media science. This would embody a more meaningful and intensive
fusion of the computational, technological and humanities approaches. Only
through this fusion can data-led explanations of human behaviour also be
humanistic explanations of human behaviour. This will require new
relationships with industry and academia, and concerted, long-term invest-
ment to build technological, methodological and presentational capabilities.
Legitimacy
The second important condition of SOCMINT use is that it be legitimate.
Broadly speaking, all security and intelligence work rests on a delicate
balance between three classes of public goods: the maintenance of national
security including public order and public safety; citizens’ right to the rule of
law, liberty and privacy; and the overall economic and social wellbeing of
the nation and its citizens. To be legitimate, any use of SOCMINT similarly
has to recognize where it risks harm to a public good, and balance this
against any contribution it makes to another.
In most circumstances these three classes of public goods should be
mutually reinforcing: security promotes inward investment and market
confidence promoting economic wellbeing and social harmony that in turn
supports the maintenance of security. There are times however when choices
have to be made. Within a rights-based approach, the only justification for
one public good to be hazarded is the provision of another. Yet social media
is a potentially disruptive phenomenon that is already affecting and in some
cases redefining how these three classes of public goods can be attained, for
the following reasons:
.Fungibility and heterogeneity. SOCMINT cuts across several categories
and can be in more than one category at a time. The broad scanning of
tweets has similarities to mass surveillance such as the deployment of
CCTV in crowded places. The close following of an individual’s
Facebook page during the course of an investigation has similarities to
de visu surveillance as ‘authorizable’ by a senior police officer. Accessing
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encrypted BlackBerry messaging by cracking the security PIN is an
interception of communications under RIPA 2000 for which a warrant
would be required.
.Generality. When used for intrusive investigation there may be no named
suspect or telephone number to target and the output may be general
rather than specific to an individual (such as noting an increase in social
media communications in a specific area where demonstrations are
taking place).
.Scalability. Many of the automated techniques can be scaled up from the
collection of hundreds to millions of pieces of social media data easily
and cheaply. The scale may be difficult to pin down in advance.
.Flexibility. The scope of many ‘scraping’ technologies (for instance, the
keywords they scan for) can be changed easily. This means they can easily
be redirected away from their original mission and function, which may
be justified operationally by tactical changes on the ground.
.Invisibility. Like other forms of covert surveillance, the operation of SMA
techniques will normally not be visible to the social media users themselves
and will override what they may assume are their privacy settings.
.Broader public concerns with digital surveillance. SOCMINT must be
understood within the context of public concerns about digital surveillance
driven by the broad rise in information systems that have vast capacities to
capture, stockpile, retrieve, analyze, distribute, visualize and disseminate
information. Concerns arise in the proliferation of surveillance opportu-
nities in this data-rich environment, and the consequences of collateral
intrusion, the possibility of data compromise, and a general implication of
suspicion of wrongdoing entailed by the widespread collection of
information.
39
As with other forms of intelligence, public concerns over privacy must be
managed. Privacy itself is an elusive concept. Article 8 of the European
Convention of Human Rights enshrines the right to respect for ‘a person’s
private and family life, his home and correspondence’. Respecting privacy
can mean that data are kept confidentially, gathered anonymously, used in a
self-determined way (the principle of ‘informed consent’), and that people
are able to see them and correct errors, or, of course, that no data are
gathered at all.
Many broad and fundamental changes in society are nonetheless
transforming what privacy means to people and social media use in
particular challenges clear-cut distinctions of what is private and what is not.
McKinsey Global Institute has calculated that 30 billion pieces of content
are shared on Facebook each month, many of them personal.
40
This sharing
39
For a description of many of these trends, see Information Commissioner’s Office, Information
Commissioner’s Report to Parliament on the State of Surveillance (November 2010).
40
S. Sengupta, ‘Zuckerberg’s Unspoken Law: Sharing and More Sharing’, New York Times,
23 September 2011, 5http://bits.blogs.nytimes.com/2011/09/23/zuckerbergs-unspoken-law-
sharing-and-more-sharing/4(accessed 17 April 2012).
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of such a large amount of voluntarily uploaded personal data, and the
number of people and institutions to whom these data are accessible, is
unprecedented; depending on the user-selected privacy settings employed,
personal information added to Facebook can be viewed by all of Facebook’s
845 million other users. Far from being incidental, this move towards the
widespread dissemination of personal information is fundamental to the
ethos of social networking sites. Facebook’s privacy settings inform users
that the ability to share information ‘allows us to provide Facebook as it
exists today’, while Twitter states more explicitly that ‘most of the
information you provide to us is information you are asking us to make
public’.
41
Indeed as a result of these changing behaviours, Mark Zuckerberg,
Facebook’s CEO, declared that privacy is ‘no longer a social norm’.
42
Most
of us accept that both private and public bodies – from Tesco through its
Clubcards to Amazon, Oyster and Google – learn and record a vast amount
about us daily. In a Eurobarometer poll, a bare majority of UK respondents
considered photos of themselves to be personal data, less than half
considered ‘who your friends are’ to be personal data, 41 per cent thought
that details of the websites they visit were personal data, and only 32 per
cent thought their tastes and opinions were personal data, yet in contrast,
large majorities regard financial data as personal.
43
However, although
research suggests that users recognize disclosing personal information is an
increasingly important part of modern life, the majority have concerns about
what this means.
44
In a 2008 European Commission Poll, around 80 per
cent of people agreed that ‘people’s awareness about personal data
protection in the UK is low’.
45
Attitudes towards privacy – especially broad, generic and in-principle
attitudes – are notoriously hard to measure objectively. Broad behavioural
norms, such as the amount of information we now share, suggest the concept
is in a state of flux, where its boundaries of definition are being
fundamentally redrawn. The debate will continue to rage about where
these redrawn boundaries on the possession, sharing and use of personal
information, now lie – indeed what privacy is.
46
41
Twitter, Privacy Policy,5http://twitter.com/privacy/previous/version_24(accessed 17
April 2012); Facebook, Data Use Policy,5www.facebook.com/about/privacy/your-info4
(accessed 17 April 2012).
42
B. Johnson, ‘Privacy No Longer a Social Norm, Says Facebook Founder’, Guardian,11
January 2011, 5www.guardian.co.uk/technology/2010/jan/11/facebook-privacy4(accessed
17 April 2012).
43
Attitudes on Data Protection and Electronic Identity in the European Union:Special
Eurobarometer 359 (Brussels: European Commission 2010).
44
See D. Boyd and E. Hargittai, ‘Facebook Privacy Settings: Who Cares?’, First Monday 15/8
(2010).
45
European Commission, Data Protection in the European Union: Citizens’ Perceptions
(2008), 5http://ec.europa.eu/public_opinion/flash/fl_225_en.pdf4(accessed 17 April 2012).
46
For recent deliberative research into people’s conceptions of privacy, see P. Bradwell,
Private Lives: A People’s Inquiry into Personal Information (London: Demos 2010),
5www.demos.co.uk/files/Private_Lives_-_web.pdf4(accessed 17 April 2012).
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The crucial implication for the use by government of different types of
SOCMINT is that the framework for recognizing and managing incursions
into privacy is struggling to keep pace with changing social attitudes and
habit. There are many ways the state can collect and use information about
people, while different systems exist for carrying this out. Each system
identifies and limits potential harm from accessing private information.
When the state conducts clinical research, for example, consent is requested
and anonymity often guaranteed; when the state draws on the research of
others we apply fair usage and ascribe credit to the individuals concerned.
When the state obtains biometric information such as a DNA sample from a
suspect, consent is not required but restrictions are applied to the retention
of material. When the state carries out surveillance, the activity is usually
covert and individual consent is irrelevant. Instead, to deal with concerns of
privacy and intrusiveness, society’s consent is needed, expressed through
enabling legislation. Whilst enabling legislation differs between states, they
are predicated on an association between the level of harm on the one hand,
and a series of steps to mitigate harm (including ways of establishing
authorization, accountability, and necessity) on the other. With the concept
of privacy now so mutable, the calculation of this particular kind of moral
hazard is difficult.
Economic and social wellbeing. The internet as a free and open space – of
course within reasonable limits – provides an immense economic and social
benefit. Government activity is intended to protect prosperity, not under-
mine it. As the British Foreign Secretary William Hague commented in 2011
‘nothing would be more fatal or self-defeating than the heavy hand of state
control on the internet, which only thrives because of the talent of
individuals and of industry within an open market for ideas and
innovation’.
47
On the other hand, the risk must be recognized that the
unregulated large-scale collection and analysis of social media data by
business and government alike (even if open source) risks undermining
confidence in, and therefore the value of, this space. The idea that the
economic and social benefit of the internet is premised on its openness and
freedom of government control is not new. From the early 1990s, a powerful
argument and vision has existed about what the internet is for and what it
should be like: an opportunity to evolve past the nation-state system into
post-territorial, self-governing communities who operate under their own
floating social contracts of consent-through-use. John Perry Barlow’s famous
Declaration of Cyberspace Independence declared to the ‘weary giants of
flesh and steel’ that cyberspace was developing its own sovereignty and ‘you
are not welcome among us’.
48
47
M. Hick, ‘Hague: Governments Must Not Censor Internet’, Huffington Post, 1 November
2011, 5www.huffingtonpost.co.uk/2011/11/01/william-hague-government-internet-censor
ship_n_1069298.html4(accessed 17 April 2012).
48
J.P. Barlow, A Declaration of the Independence of Cyberspace, 8 February 1996, 5https://
projects.eff.org/*barlow/Declaration-Final.html4(accessed 17 April 2012).
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We believe that it is important to distinguish, as we have tried to do in this
article, between open-source, non-intrusive SOCMINT and closed-source,
intrusive SOCMINT. Any legitimizing and enabling framework for the
collection and use of SOCMINT must therefore begin by distinguishing
between what is a form of intrusive surveillance and what is not. This
recognizes that there are times when we can legitimately seek control of
what information we give away and how it is used, but there are also times
when individual control must be over-ridden. The circumstances where this
can happen are based on collective decisions and assent about the state’s
authority.
The key concept relevant to making this distinction is whether the user
controls the use of their data through consent. For SOCMINT to be non-
intrusive and open-source, it should not be able to identify individuals, be
used as a means of criminal investigation, or puncture the privacy wishes of
the user.
Any Government use of open SOCMINT should be put on the same
footing as private companies and academia, with conditions relating to
anonymity and data protection. For open SOCMINT, then, harm is
conceived not as intrusion into someone’s private space, nor the wider
issues of trust and implied suspicion (since neither of these would happen
within open SOCMINT), but by the loss of control over the information
through use beyond what can be reasonably expected. Reasonable
expectation can be protected through a characteristic openness of how or
when this kind of SOCMINT is conducted. All such collection, retention,
and sharing policies are publicized and justified and the reason why the
information is collected is publicized.
Intrusive SOCMINT is more straightforward since most states already
have a legislative framework for regulating intrusive intelligence gathering,
for example for the purposes of national security and the prevention and
detection of crime (in the case of the UK such authority is provided in the
Regulation of Investigative Powers Act 2000). The application of such
legislation can be codified for intrusive SOCMINT to cover the powers of
access required and the procedures to be followed at a legal and operational
level. What is important is that there is public acceptance of the
arrangements based on recognition that at the heart of an enduring, effective
settlement for state surveillance rest sound ethical principles. We suggest an
adaptation of the set of principles earlier suggested for the intelligence
community by Sir David Omand in his book, Securing the State.
49
Principle 1: There must be sufficient, sustainable cause. This first and
overarching principle forces the big picture to be taken into account: the
overall purposes that could justify the acquisition by a public body of
capabilities to gather, understand and use social media data. There is a
danger that a series of SOCMINT measures – each in themselves justifiable –
together creep towards an undesirable end point: a publicly unacceptable
49
D. Omand, Securing the State (London: Hurst & Co 2010).
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level of overall surveillance; the possession of a dangerous capability; and
the overall damage to a medium that is of obvious intrinsic value beyond
security. Just because it can be done does not mean that it should be done.
Application of the principle of requiring sufficient, sustainable cause is
therefore necessary to ensure that SOCMINT remains within the boundaries
required to deliver social, economic, security and law enforcement benefits
to the public, resisting bureaucratic empire building, finding ways to employ
spare capacity or simply the banalization of the technology available from
commercial suppliers.
Principle 2: There must be integrity of motive. This principle refers to the
need for integrity throughout the whole ‘intelligence’ system, from the
statement of justification for access, accessing the information itself, through
to the objective analysis, assessment and honest presentation of the results.
The justification for seeking SOCMINT in individual cases must in
particular be clear and not mask other motives on the part of the
investigating officers. Intelligence is by its nature usually incomplete and
fragmentary, and can be wrong or subject to deception. In presenting
intelligence to end-users the limitations and caveats must be made clear. Nor
must the decision to use (or not to use) SOCMINT, or the conclusions drawn
from it, be influenced by local or national political considerations or media
pressure.
Principle 3: The methods used must be proportionate and necessary. There is
a well-established principle in law enforcement that the extent of harm likely
to arise from any specific action being taken should be proportionate to the
harm that it is being sought to prevent. In assessing proportionality, the
extent of intrusion has to be assessed. That would mean a lower threshold
for covertly monitoring material that the user has not restricted than in cases
where they had only a limited list of friends who had access, or where they
used a system such as BlackBerry that required a PIN.
Principle 4: There must be right authority, validated by external oversight.
There is a general principle that there must be an audit trail for the
authorization of actions that may carry moral hazard with an unambigu-
ously accountable authority within a clear chain of command. Having an
adequate paper trail (or its electronic equivalent) for key decisions is
essential for confidence of staff and politicians, and for the operation of
investigations and redress in any cases of suspected abuse of powers. We
believe this principle should apply to SOCMINT as well as to any other
intelligence operation. This is an important way in which proportionality
and accountability is realized in practice.
Principle 5: Recourse to secret intelligence must be a last resort if more open
sources can be used. Because of the moral hazards of all intrusive secret
intelligence gathering methods, those authorizing such operations should ask
whether the information could reasonably be expected to be obtained
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through other means, ranging from fully open sources to information freely
volunteered from within the community. Applying this general principle to
SOCMINT, less intrusive forms should be preferred to more intrusive covert
forms. SOCMINT should be based wherever possible on the clearest
possible mandate of informed consent. The most preferred route is to access
explicitly ‘consenting’ information from the online community, for example
crowd-sourced information that has been explicitly volunteered on a
particular Facebook wall or hashtag. Recourse to covert intelligence
gathering, including via exploitation of social media, should be confined
to cases where the information is necessary for the lawful purpose of the
operation and cannot reasonably be expected to be gained by other means.
Overall, these principles maintain a series of crucial associations: as the
type of surveillance becomes increasingly intrusive so three vital and
increasingly narrow conditions are imposed onto it: the agencies that can
conduct it, who must authorize it, and the reasons why the surveillance can
be legitimately conducted. This is vital to balance the possible good of the
collection and use of SOCMINT with the possible harm. Measuring
intrusion, however, is far from straightforward. People often share what
would usually be considered private things about their lives in ways that are
unprecedentedly public, sometimes with unexpected or unrealized con-
sequences. The scale of intrusion entailed by SOCMINT access varies
greatly. To gather and analyze a suspect’s open tweets looks similar to the
surveillance of someone in public, whilst to gather and analyze someone’s
Facebook messages seems closer to reading their private correspondence. We
do not yet have a conceptual framework of what should in future constitute
privacy in social media use and the consequent sorts of harms associated
with breaching that privacy.
Concluding Remarks
The opportunities that the explosion of social media use offers are
remarkable. SOCMINT must become a full member of the intelligence
and law enforcement family. At the heart of this process are the twin
demonstrations of necessity and legitimacy.
To meet the challenge of necessity, a new, applied academic discipline –
social media science – must be developed. This requires new relationships
with industry and academia, and concerted, long-term investment to build
technological, methodological and presentational capabilities. Those dis-
ciplines best equipped to understand and explain human behaviour – the
social and behavioural sciences, political science, psephology, anthropology
and social psychology – must be made to interweave with the big data
approaches necessary to understand social media. Only through this fusion
can data-led explanations of human behaviour also be humanistic
explanations of human behaviour.
But technology and capability is only half the picture. To meet the
challenge of legitimacy, the public must broadly understand and accept
why, when and with what restrictions SOCMINT is undertaken. Any
822 Intelligence and National Security
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Government that wishes to conduct SOCMINT must adopt an explicitly
articulated approach grounded in respect for human rights and the
associated principles of accountability, proportionality and necessity.
Notes on Contributors
Sir David Omand GCB is a visiting professor in the War Studies Department
at King’s College London. He was appointed in 2002 the first UK Security
and Intelligence Coordinator, responsible to the Prime Minister for the
professional health of the intelligence community, national counter-
terrorism strategy and ‘homeland security’. He served for seven years on
the Joint Intelligence Committee. He was Permanent Secretary of the Home
Office from 1997 to 2000, and before that Director of GCHQ. Previously, in
the Ministry of Defence he served as Deputy Under Secretary of State for
Policy, Principal Private Secretary to the Defence Secretary, and served for
three years in NATO Brussels as the UK Defence Counsellor. He was
educated at the Glasgow Academy and Corpus Christi College, Cambridge
where he is an honorary fellow. His book, Securing the State, was published
by C. Hurst in hardback (2010) and paperback (2012).
Carl Miller is an associate at Demos and a researcher at the International
Centre for Security Analysis, King’s College London.
Jamie Bartlett is head of the Violence and Extremism Programme and
Director of the Centre for Social Media Analysis at the think-tank Demos.
Introducing Social Media Intelligence 823
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... With the proliferation of broadband mobile network and the corresponding upsurge in the social media usage population; the volume of this data keeps increasing. The role of social media platforms as centralized databases for intelligence gathering among security agencies has been acknowledged (Omand, Bartlett & Miller, 2012). A 2016 survey on law enforcement use of social media by the International Association of Chiefs of Police and the Urban Institute which covered 539 law enforcement agencies in 48 states of the United States and the District of Columbia found that 91 percent of law enforcement agencies use the social media to inform the public of safety concerns. ...
... In the next couple of days, social media intelligence revealed the likelihood of the disorder spreading to other parts of London and also was awash with contents that suggest criminal intents. These provided the police with information about an outbreak or disorder as well as the identities of the perpetrators (Omand, Bartlett & Miller, 2012). Furthermore, a research with the British police interrogated how social media and big data uses form part of a broader shift from reactive to proactive forms of governance that enables state agencies to predict, pre-empt and respond in real time to various social problems (Dencik, Hintz & Carey, 2018). ...
... This emerging tool of intelligence gathering needs to be guided by global best practices for it to yield the desired results. Hence, Omand, Bartlett and Miller (2012) argue that law enforcement increased interest in social media intelligence has raised concerns about the methodological and ethical framework for using it. ...
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... With the proliferation of broadband mobile network and the corresponding upsurge in the social media usage population; the volume of this data keeps increasing. The role of social media platforms as centralized databases for intelligence gathering among security agencies has been acknowledged (Omand, Bartlett & Miller, 2012). A 2016 survey on law enforcement use of social media by the International Association of Chiefs of Police and the Urban Institute which covered 539 law enforcement agencies in 48 states of the United States and the District of Columbia found that 91 percent of law enforcement agencies use the social media to inform the public of safety concerns. ...
... In the next couple of days, social media intelligence revealed the likelihood of the disorder spreading to other parts of London and also was awash with contents that suggest criminal intents. These provided the police with information about an outbreak or disorder as well as the identities of the perpetrators (Omand, Bartlett & Miller, 2012). Furthermore, a research with the British police interrogated how social media and big data uses form part of a broader shift from reactive to proactive forms of governance that enables state agencies to predict, pre-empt and respond in real time to various social problems (Dencik, Hintz & Carey, 2018). ...
... This emerging tool of intelligence gathering needs to be guided by global best practices for it to yield the desired results. Hence, Omand, Bartlett and Miller (2012) argue that law enforcement increased interest in social media intelligence has raised concerns about the methodological and ethical framework for using it. ...
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... They include issues commonly found in most Big Data Analytics (BDA) applications, as summarized by the renowned 5V: Volume, Velocity, Variety, Variability, and Veracity [157]. However, the specific context of OSM introduces further difficulties [218]. According to the Digital in 2020 report 1 , there are 3.8 billion active OSM users, equal to the 49% of the total world population. ...
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