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Who Matters Online:Measuring infuence,evaluating content andcountering violentextremism in online
social networks
J.M. Berger & Bill Strathearn
March 2013
Developments in Radicalisationand Political Violence
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Developments in Radicalisationand Political Violence
Developments in Radicalisation and Political Violenceis a series o papers published by the International Centre
or the Study o Radicalisation and Political Violence (ICSR).
It eatures papers by leading experts, providing reviews o
existing knowledge and sources and/or novel arguments
and insights which are likely to advance our understanding
o radicalisation and political violence. The papers are written
in plain English. Authors are encouraged to spell out policy
implications where appropriate.
EditorsAlexander Meleagrou-Hitchens
Dr John Bew
ICSR, Kings College London
Editorial AssistantKatie Rothman
ICSR, Kings College London
Editorial BoardProf. Sir Lawrence Freedman
Kings College London
Dr. Boaz Ganor
Interdisciplinary Center Herzliya
Dr. Peter Neumann
Kings College London
Dr. Hasan Al Momani
Jordan Institute o Diplomacy
ContactAll papers can be downloaded ree o charge at www.icsr.
ino. To order hardcopies, please write to [email protected].
For all matters related to the paper series, please write to:
ICSR
Kings College London
138142 Strand
London WC2R 1HHUnited Kingdom
ICSR 2013
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Contents
Introduction and Key Findings 3
Overview 7
Methodology and terms 9
Collected Data 11
Analysis o Inuence 15
Identifcation o Engaged Extremists 17
Content o Tweets 21
Links 21
Top Sites 23
Hashtags 25
Political Context Considerations 27
Comparison Group 29
Identication Rate 30
Content Analysis 31
CVE Implications and Recommendations 35
Measurement 37
Targeting Infuence 38
Targeting Content 41Intervening with At-Risk Individuals 43
Countering Extremist Narratives 44
Reactive Opportunities to Counter Extremist Narratives 45
Data at a glance 47
Appendices 48
A: Seed accounts 48
B: Scoring ormula and glossary o metrics 48
C: Top 10 most infuential 50
D: Top 10 most exposed 51E: Top 20 most interactive 51
F: Glossary o Twitter Terms 52
G: Disclosures 53
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About the Authors
J.M. Berger is a researcher and consultant working on
problems related to extremism, with a particular ocus on theuse o the Internet by extremist movements. He has written
about extremism or Foreign Policy, The New York Times,
The Daily Beast, and the Sentinel, a peer-reviewed journal
published by the Combating Terrorism Center at West Point.
Berger is the author o Jihad Joe: Americans Who Go to War
in the Name o Islam (Potomac Books, 2011).
Bill Strathearn is a sotware engineer with Google and Google.
org with an interest in creating technology that improves theinterace between government and citizens. He has 13 years
o experience in a broad range o sotware systems including
social graph analysis pipelines used to improve search
results. Strathearn studied computer science at the University
o Caliornia at Santa Barbara where he graduated with a
Masters degree.
AcknowledgementThe authors would like to acknowledge and thank
Google Ideas or its support in commissioning this paper.
Views expressed in the paper are those o the authors.
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Introduction and
Key FindingsIt is relatively easy to identiy tens o thousands o social
media users who have an interest in violent ideologies, but
very dicult to gure out which users are worth watching.
For students o extremist movements and those working to
counter violent extremism online, deciphering the signal amid
the noise can prove incredibly daunting.
This paper sets out a rst step in solving that problem. We
have devised a scoring system to nd out which social
media accounts within a specic extremist circle were most
infuential and most prone to be infuenced (a tendency we
called exposure).
Our starting data centered on ollowers o 12 American white
nationalist/white supremacist seed accounts on Twitter. We
discovered that by limiting our analysis to interactions with
this set, the metrics also identied the users who were highly
engaged with extremist ideology.
Within our total dataset o 3,542 users, only 44 percent
overtly identied themselves as white nationalists online. By
measuring interactions alonewithout analyzing user content
related to the ideologywe narrowed the starting set down
to 100 top-scoring accounts, o which 95 percent overtly sel-
identied as white nationalist. Among the top 200, 83 percent
sel-identied, and or the top 400, the sel-identication rate
was 74 percent. A comparison analysis run on ollowers o
anarchist Twitter accounts suggests the methodology can be
used without modication on any number o ideologies.
Because this approach is entirely new (at least in the
public sphere), the paper spends some time discussing the
methodology and ndings in some detail, beore concluding
with a series o recommendations or countering violent
extremism (CVE) based on the ndings. The key terms or
understanding the recommendations are:
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Inuence: The tendency o a user to inspire a
measurable reaction rom other users (such as a replies
or retweets).
Exposure: The fip side o infuence, this is the
tendency o a user to respond to another user in a
measurable way.
Interactivity: The sum o infuence and exposure scores,
roughly representing how oten a user interacts with the
content o other users.
Our key ndings include:
Infuence is highly concentrated among the top 1 percent
o users in the set.
High scores in both infuence and exposure showed a
strong correlation to engagement with the seed ideology
(white nationalism in our primary analysis, and anarchism
in a secondary analysis).
Interactivity, the sum o infuence and exposure scores,
was even more accurate at identiying users highly
engaged with the seed ideology.
In the course o collecting the data needed to measure
infuence and exposure, we incidentally collected a large
amount o data on hashtags and links used by people who
ollow known white nationalists on Twitter. When we examined
this data, we discovered that members o the dataset were
highly engaged with partisan Republican and mainstream
conservative politics. The paper presents a signicant amount
o context needed to properly evaluate this nding.
Working rom these ndings, the paper makes several
recommendations or new CVE initiatives with a ocus
on NGO eorts, which was the purpose o this research,
although we recognize our ndings will likely have utility
or government eorts in this sphere as well.
Our recommendations include:
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We believe these metrics oer ways to concretely
measure which types o CVE approaches are eective
and which are not, bringing some clarity to a realm where
strategies are oten wishul and based on assumptions,while conclusions are oten anecdotal and inconclusive.
The concentration o infuence among a very ew users
suggests that disruptive approaches and counter-
messaging should be targeted to the top o the ood
chain, rather than working with the larger base o users.
Our analysis ound that the seed accountsall well-
known white nationalist ideologues and activistswerenot necessarily producing the most popular content and
links to external Web sites. The collected data can be
used to nd the most important external content sources,
and target them or disruption through terms-o-service
violation reporting, or through counter-messaging.
By tracking these metrics on an ongoing basis, NGO
eorts to counterprogram against extremist narratives
can be evaluated to measure how many users adopt
or respond to counter-messaging content, and how
much infuence accrues to dierent kinds o positive
messaging.
Since the data suggests white nationalists are actively
seeking dialogue with conservatives, CVE activists should
enlist the help o mainstream conservatives, who may be
considerably more successul than NGOs at engaging
extremists with positive messaging. Further research may
also suggest avenues or engagement between other
kinds o extremists and other mainstream political and
religious movements.
Finally, we believe that these metrics are only a starting point
or the study o extremist use o social media. We believe the
metrics and approaches here can be urther rened, and we
believe that additional research may yield substantial new
techniques or monitoring and countering the promotion o
violent ideologies online.
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Overview
As social media grows more important to organizational
activity o all types, its role in enabling radicalization and
extremism has come under intense scrutiny. This paper
examines one type o extremism, white nationalism, on one
social network, Twitter,1 in order to learn more about the
nature o social media interactions.
We used a numerical scoring system to analyze the number
and type o interactions among 3,542 Twitter users whoollowed the accounts o well-known white nationalists.
Not all members o the overall dataset were committed white
nationalists, but we ound that 95 o the 100 users with the
highest scores or interactivity were also observed to be highly
engaged with white nationalist (WN) ideology in their tweets
and how they described themselves in their Twitter account
proles. For the top 200, the metrics accuracy in identiying
users overtly engaged with WN ideology was 83 percent, and
or the top 400 the accuracy rate was 74 percent.
Importantly, the scoring system did not include analysis o
any terms or conditions specic to white nationalism, dened
here as any ideology that prioritizes white racial identity as a
undamental component o how society should be organized.
Instead, results were derived solely by measuring interactions
within the dataset, regardless o content, which suggests
the approach can be applied without modication to other
extremist ideologies. To test that theory, we replicated the
analysis o the initial dataset on a group o Twitter users who
ollowed anarchist accounts, with encouraging but more
ambiguous results.
As one component o the scoring system, we analyzed the
dataset to identiy the most infuential users. We ound that
nearly 50 percent o all infuence in the system (as dened by
1 A glossary o Twitter terms is included as Appendix F. For more explanation o
how Twitter works see https://support.twitter.com/articles/215585-twitter-101-
how-should-i-get-started-using-twitter
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a weighted measurement o interaction described below)
was concentrated among the top 1.5 percent o users.
We believe these results oer signicant opportunities toimprove initiatives aimed at countering violent extremism
(CVE) in the online arena, including improved selection
o targets or counter-messaging and disruptive tactics
and the ability to quantitatively measure the success o
dierent approaches.
In a secondary analysis, we also examined links and
hashtags tweeted by users in the dataset and ound a
signicant portion o the content related to mainstreamconservative and partisan Republican politics. This nding
requires a signicant amount o context to be interpreted
airly, and it is important to reiterate that not all users in
the dataset were committed to white nationalism. Most
importantly, the timing o the data collection, during the
political convention season with a major election
approaching, should also be taken into consideration as
it likely infuenced the choice o both links and hashtags,
as discussed in greater detail below.
With a ull consideration o the context, we believe this
nding suggests that social conservatives are in a very
strong position to conduct eective CVE programs aimed
at neutralizing the messages and narratives promulgated
by infuential white nationalists.
Our comparison analysis o ollowers o anarchist
Twitter accounts ound that while those users posted a
large amount o material pertaining to liberal views, they
posted much less content related to mainstream and
partisan politics.
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Methodology and terms
Methodology
A short glossary o Twitter-specic terms used herein is
attached as Appendix F.
Sampled Twitter accounts: 12 seed Twitter
accounts or users identied as prominent individuals or
organizations within the white nationalist movement
Followers dataset: 3,542 Twitter accounts that ollow
one or more seed accounts
Total dataset: The 200 most recent tweets or each seed
and ollower, or all available tweets i the account had less
than 200. Total tweets analyzed: 342,807.
Sample period: Automated collection was completed
25 August 2012. Manual inspection o accounts began
25 August 2012 and continued through 31 October 2012.
Metrics: A complete explanation o all metrics may be
ound in Appendix B.
Key Terms
White nationalist: Any ideology that prioritizes white
racial identity as a undamental component o how a
given society or nation should be organized.
Inuence: A metric measuring a Twitter users ability to
publicly create and distribute content that is consumed,
armed and redistributed by other users.
Exposure: A metric measuring a Twitter users tendency
to publicly consume, arm and redistribute content
created by other users.
Interactivity: A metric measuring a Twitter users
combined infuence and exposure based on their
public activity.
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Collected Data
Collection began with 12 Twitter accounts belonging to
prominent individuals and organizations that openly and
unambiguously promote organized, ideological white
nationalism, including the ocial accounts o David Duke, the
Aryan Nations and the White Aryan Resistance. A ull list is
ound in Appendix A.
These seed accounts were chosen based on the current
and historical signicance o the people and organizations towhite nationalist ideology, as well as or their unambiguous
status as white nationalists. We collected inormation that
the ollowers o the seed accounts made publicly available
between August and October 2012, using a Web application
designed by co-author Bill Strathearn.
No one o the 12 seed accounts attracted a notably large
number o ollowers. All 12 accounts together had 3,542
ollowers with publicly viewable proles, many o whom
ollowed more than one seed. For each o the 12 seeds
and their 3,542 ollowers, we collected the 200 most recent
tweets. In some cases, accounts had not yet posted 200
tweets, in which case all available tweets were collected. A
total o 342,807 tweets were analyzed statistically. Tens o
thousands o tweets were also manually reviewed.
Not all the ollower accounts collected belonged to users
who openly embraced or supported white nationalist views.
A manual inspection o 350 randomly selected accounts
rom the ollower dataset showed 44 percent were overtly
engaged with white nationalism in observed tweets or by
sel-identication in their user prole (margin o error +/- 5.4
percent). The sample was selected using a random number
unction to assign a random number value to each user.
The dataset was then sorted according to the random
value, and the rst 350 accounts were analyzed.
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Sixteen percent o the dataset unambiguously sel-identied
with white nationalism in their proles or usernames by using
clear terms and phrases to describe themselves, such as
Aryan, racist, racialist or white nationalist. We took such
primary sel-identication to indicate a very high level o
engagement with WN ideology.
Close to 50 percent o the random sample o ollowers
o seed accounts displayed no overt sign o positive
engagement with white nationalism in their tweets or proles
other than the act o ollowing a seed account. The vast
majority o these simply provided no clear evidence o their
attitudes toward white nationalism, either or or against.
A tiny handul overtly expressed views incompatible with
white nationalism, such as belie in racial harmony or non-
discrimination. Only one account was observed to be an
activist opposed to white nationalism.
44%36%
6%
64%50%
Overtly engagedwith white
nationalism
No overt white
nationalism
Random Sample
N = 350
Overtly Engaged
N = 154
Tweets Only
Self-description
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Twelve percent o the random sample ollowed more than
500 other accounts. We judged these individuals to be
indiscriminately ollowing others, oten in hopes o gaining
more ollowers themselves (a common Twitter strategy).Twenty-two percent o the indiscriminate ollowers were
overtly engaged with WN ideology on Twitter, about hal the
rate o the overall set, while 70 percent showed no overt
engagement.
All o the seed accounts were based in the United States.
Followers came rom a number o locations. Because Twitters
prole parameters are unstructured, we could not statistically
analyze the locations, but users were anecdotally observedto live primarily in the United States, the United Kingdom,
Sweden and Canada. Users were observed rom elsewhere in
Europe, including France and Germany, Latin America, Asia
and the Middle East.
Only public inormation was actored into this analysis. No
data was collected on accounts marked as private at the time
o collection. The status o some accounts changed between
the start o collection and the end o analysis, including a
handul o voluntary and involuntary suspensions, and some
accounts which switched rom public to private.
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Analysis o Infuence
We designed a scoring system to measure the infuence
and exposure o the seed accounts and their ollowers
using their patterns o interaction with other users in the
ollowers dataset. Interactions with the seeds and users
outside the dataset o ollowers are not scored in the
metrics presented here.
Infuence and exposure are dened as Twitter interactions
that mirror the real lie denitions o the terms infuencedened as the ability to spread and promote content or ideas,
and exposure as a tendency to consume content or ideas
generated by others.
In terms o Twitter activity specically, infuence was dened
as when a user did something that inspired a reaction rom a
second user (such as prompting the second user to reply to a
tweet, or to retweet content initially tweeted by the rst user).
In the context o this paper, a users infuence score may be
said to determine how infuential that user is.
Exposure was simply the other side o the coin, dened
as when a user perormed such an action in response to
another user. A user who retweeted a second user, or replied
to something the second user tweeted, received points or
exposure. Throughout the paper, a users exposure score may
be said to determine how exposed that user is to content
generated by other users within the dataset. For instance,
that a user with a high exposure score may be designated as
more exposed than one with a low score.
Although infuence and exposure represent opposite types o
interactions, an account could have high or low scores in one
or both categories, depending on the nature and quantity o
its interactions. The complete scoring ormula, with examples,
is attached as Appendix B.
Interactions were weighted to emphasize keywords in tweets
indicating they might be linked to more signicant types o
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communication such as call, email or DM (reerring to
Twitters direct messaging system or private communications
between users).
Ater analysis o these initial results, we created a third score,
which was simply the sum o the infuence and exposure
scores, a measurement we reer to as total interactivity,
representing the overall quantity o meaningul interactions
conducted by the account holder.
Importantly, the scoring system did not consider or score
terms relevant to white nationalism. Our initial goal was
simply to analyze the social network structure to evaluatewhich ollowers o seed accounts were extremely infuential
or extremely vulnerable to infuence. The scoring systems
eectiveness at identiying people overtly engaged with
the ideology that united the seed accounts was an
unexpected nding.
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Identication o Engaged
Extremists
The rankings discussed throughout this paper apply to
the ollowers o the seed accounts and exclude the seeds
themselves, as our goal was to evaluate the less obvious
participants in the network. Within the ollower dataset, the
number one most-infuential account, by a wide margin, had
been identied as a potentially important user in the dataset
through a manual examination o the seeds and their ollowers
prior to data collection, suggesting that the scoring systems
results were not out o line with manual analysis. Manual
inspection o the most infuential and exposed accounts ound
that the ranked accounts generally conormed to the qualities
we sought to identiy (i.e., infuence and exposure).
0
20
40
60
80
100
PERCENT
Random
sample
Top 400 Top 200 Top 100 Top 50
RANK ACCORDING TO INTERACTIVITY SCORE
USERS OVERTLY ENGAGING WITH WHITE NATIONALISM
Not overtly engaged
Overtly engaged
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Among top 100 most infuential, 86 percent were observed to
be overtly engaged with white nationalism beyond the simple
act o ollowing a seed account (as previously dened in the
dataset section, overtly engaged means users sel-identiedas white nationalists or tweeted consistently about white
nationalism in a non-adversarial light). For the top 200, the
percentages dropped to 73.5 percent. Ninety-three o the
100 most exposed accounts were overtly engaged with white
nationalism. O the top 200, 83 percent were overtly engaged.
Ater examining these results in detail and observing their
unanticipated utility at identiying engaged extremists, we
posed the question o whether it was possible to improve theidentication process. We experimented with variations on the
original scores and ound that adding infuence and exposure,
creating a measurement o overall interactivity, produced
improved identications.
Manual examination showed 95 o the 100 accounts with
the highest interactivity scores were overtly engaged with
TOP 100
MOST INTERACTIVE
USERS
5%
24%
71%
Overtly engaged in tweets only
Sel-identied as white nationalist
Not overtly engaged
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white nationalism, and 71 sel-identied as white nationalist
in their usernames or proles, showing extremely high levels
o engagement. This compared very avorably to the random
sample, where 44 percent were overtly engaged and 16percent sel-identied.
For users with the top 200 interactivity scores, 83 percent
were overtly engaged and 57.5 percent sel-identied. O the
top 400, 74 percent were overtly engaged.
There was substantial overlap among the highest-scoring
accounts in each category, with a number o accounts
scoring high in all three measurements.
Within the top 50 o all three categories, only one account
appeared totally irrelevant, a person who generically
interacted with accounts that began ollowing him. Another
user sel-identied using neo-Nazi terminology and interacted
with engaged white nationalists, but did not hersel
obsessively tweet on the subject.
The remaining top-50 users were observed to be highly
engaged with white nationalism and spent the vast majority
o their time on Twitter discussing WN ideology.
Based on these ndings, we believe the scoring ormula may
have a number o useul applications in CVE eorts, which
will be discussed below.
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Content o Tweets
Although we set out with the primary goal o analyzing the
network interactions o the ollowers o white nationalist
seed accounts, the nature o our process also resulted in the
collection o other data, including links to external content and
hashtags (sel-conscious content identiers) used in tweets
by members o the dataset. A casual examination o this
collected data produced unexpected insights into the content
being promoted and consumed by the ollowers dataset, and
so we carried out a more detailed analysis.
Links
We detected 676 distinct domains in a total o 84,825 tweets
that included links rom the seed accounts and their ollowers.
2%
SOURCES BY
NUMBER OF LINKS
Identifiable content
domain names
4%
5%
16%
7%
12%
46%
8%
Alternative
Mainstream news
Extremist
Youtube
Facebook
Blogspot
Wordpress
All other content-neutral, includinglink shorteners
CONTENT-NEUTRAL
DOMAIN NAMES
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We classied the links into our major categories: mainstream
news, alternative sources, extremist sources, and content-
neutral. Sites that generally adhered to journalistic standards
were classied as mainstream news, even i they had apolitical slant, such as Fox News.
The alternative category included sites that explicitly
articulated a political slant and sites that did not consistently
conorm to mainstream journalistic standards.
Extremist sources consisted o sites that openly advocated
white nationalism or very closely related themes.
The content-neutral category included links where the
specic content could not be resolved, as well as sports,
entertainment, health and science news sources deemed
o minimal interest to the study.
Mainstream news accounted or 12 percent o all links;
alternative and extremist sources combined represented
another 15 percent.
The categorization process necessarily involved a number
o subjective judgments. For instance, a handul o sites
included in the extremist category did not explicitly endorse
white nationalism, but provided narrative support to themes
white nationalists consider to be crucial. The most important
o these, in terms o the number o links tweeted by users in
the dataset, was Inowars.com, a conspiracy-oriented Web
site. Other subjective calls included the classication o state-
sponsored media and oreign nationalist sites. Overall, we
estimate around 6 to 8 percent o these subjective judgments
could be considered open or debate.
The single most-linked domain by an overwhelming margin
was YouTube, which accounted or about 21 percent o
all links detected. The content-neutral category included
a number o other service providers, such as Blogger and
Wordpress, where the nature o the content could not be
determined. Finally, generic URLs generated by various
popular Twitter clients represented 15.9 percent o all links.
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We suspect many o these links would be classied as
alternative or extremist content, to an extent that could
substantially shit the percentages given above.
Top Sites
The top 10 sites linked in each category were:
Extremist Alternative Mainstream Content-neutral
whiteresister.com rt.com dailymail.co.uk youtube.com
cocc.org examiner.com oxnews.com bit.ly
realisten.se theblaze.com bbc.co.uk acebook.com
inowars.com wnd.com nytimes.com youtu.be
vnnorum.com breitbart.com guardian.co.uk blogspot.com
amren.com presstv.ir hungtonpost.com twitter.com
northwestront.org dailycaller.com telegraph.co.uk tumblr.com
malevolentreedom.org hotair.com thehill.com wordpress.com
thenewamerican.com globalresearch.ca cnn.com b.me
vdare.com thegatewaypundit.com reuters.com instagram.com
Top 10 as a percentage o all links
46.3 46.8 36.1 58.5
Mainstream conservative inormation sources were ound
throughout the links, mostly in the alternative category, which
by denition included political content.
The top 10 alternative URLs accounted or nearly hal o
all links in the alternative category. Among the top 10, 54
percent o links went to sources espousing clear partisan
Republican viewpoints (as opposed to simply expressing
conservative views).
The number o l inks per site dropped sharply in the lower
rankings. Many sites in the lower 112 URLs were oriented
so ambiguously among mainstream conservative, ringe
conservative and libertarian views as to deeat easy
classication.
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Among the top 10 mainstream news sites, 31 percent o
links went to the top two sources, the Daily Mail (UK) and
Fox News (US), both o which are strongly oriented toward
mainstream political conservatism. The remainder went to
a mix o top mainstream news sources, including sites that
were reasonably considered apolitical, such as the New
York Times, or had a liberal orientation, such as the
Hungton Post.
The most-linked extremist site was WhiteResister.com,
but more than hal the links to that site originated with two
Twitter accounts, both aliated with the site. Discounting
links rom those two accounts, WhiteResister.com would
have dropped to sixth.
Signicantly, the accounts promoting WhiteResister.com
were the two most ollowed by other users in the dataset,
pointing to infuence above and beyond the bias introduced
by their sel-promotional tweets (or at least highlighting the
eectiveness o sel-promotion at garnering links).
ALL OTHER
ALTERNATIVE
LINKS
53%
PartisanConservative(54%oftop 10)
OtherAlternative
DISTRIBUTION
OF ALTERNATIVE
LINKS
TOP 10
47%
TOP 10
0%
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The top 10 list also il lustrates an important point. While
the ollowers o white nationalist Twitter accounts are clearly
engaged with the ideology, that doesnt mean they are
involved in promoting the seed accounts o leading WNgures and organizations.
O the top 10 most-linked extremist sites, only two amren.
com and northwestront.org were associated with seed
accounts. Our infuence rankings excluded the seed
accounts. I they had been included in the dataset, three o
the seed accounts would have ranked among the top 10
most infuential. But the Web sites associated with those three
accounts did not appear among the 100 sites most-linked bythe ollowers dataset.
This demonstrates that ollowers were ar more likely to
promote their own related agendas than to promote well-
known leaders and groups and suggests that these well-
known leaders o white nationalism in the United States may
be losing touch with their constituents. While they still carry
signicant weight in the movement, they are not generating
daily buzz, on Twitter at least.
Connecting the most infuential Twitter users with online
content hosted elsewhere also expands the universe o
options or CVE practitioners, which will be discussed in
more detail in the CVE Implications section o this paper.
Hashtags
Hashtags included in tweets by the seed accounts and
their ollowers pointed more clearly to engagement with
mainstream politics. The top 10 hashtags used by the seeds
and ollowers combined were:
1. tcot (top conservatives on Twitter)
2. svpol (Swedish police)
3. teaparty
4. p2 (used to address comments to progressives)
5. gop
6. tlot (top libertarians on Twitter)
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7. (used to recommend Twitter accounts
to
other ollowers)
8. Obama9. news
10. ows (Occupy Wall Street)
Given that not all accounts overtly engaged with white
nationalism, we analyzed the top 10 hashtags tweeted by the
200 and 50 most infuential ollowers o seed accounts, who
were more highly engaged with white nationalism.
For hashtags, narrowing the dataset down to the mostinfuential and extreme users suraced specic tags relating
to extremism, including #wpww (White Power World Wide),
#nwo (New World Order) and #edl (English Deense League).
However, the mainstream political tags remained signicant
and in many cases represented ahigherpercentage as the
user base became more extreme. The tags #tcot, #gop and
#teaparty remained in the top 10, in the same order as they
appeared or the whole dataset. #tcot was the most-used
hashtag in all subsets examined.
All ollowers 200 mostinuential
50 mostinuential
TCOT 10% TCOT 18% TCOT 20%
SVPOL 3% SVPOL 11% SVPOL 18%
TEAPARTY 3% TEAPARTY 6% WPWW 14%
P2 3% GOP 5% EDL 11%
GOP 2% P2 5% TEAPARTY 8%
TLOT 2% WPWW 4% FF 6%
FF 2% TLOT 4% GOP 5%
OBAMA 1% OCRA 3% OBAMA 5%
NEWS 1% NATPOL 3% NWO 4%
OWS 1% NOW 3% P2 3%
TOP 10 HASHTAGS, PERCENTAGE OF ALL TAGS
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Political Context Considerations
In our random sample, 16 percent o users sel-identied
as white nationalists, but only 4 percent sel-identied as
mainstream conservatives. That suggests the link and
hashtag results are driven more by white nationalists eeling
an anity or conservatism than by conservatives eeling an
anity or white nationalism.
The timing o the data collection, during the polit ical
convention season with a major election approaching, likely
infuenced the choice o both links and hashtags. President
Obamas race almost certainly motivated white nationalists to
take an enhanced interest in mainstream Republican politics.
A number o the 10 hashtags most oten tweeted by theollower dataset (including those most oriented toward
Republicans) were recommended or use in an organized
0
5
10
15
20
PERCENTAGEOFALLHASHTAGS
All followers Top 200 Top 50
LESS EXTREME USERS MORE EXTREME USERS
#TCOT
#TEAPARTY
#GOP
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program o tweets by a social media group outside the
dataset with a stated intention to discredit the Obama
campaign. This eort began no later than August and may
have tilted the results.2
Many o the same tags were recommended on a non-racial
militia movement Web site in September 2012 as a way to
broadcast that movements message to those in the political
mainstream.3
This tactic could also have been adopted by white
nationalists, although explicit evidence o such an eort was
not ound. These online campaigns were ound by searchingor several o the hashtags together in a database o extremist
Web sites routinely maintained by co-author J.M. Berger.
Finally, it is important to note that engagement does not
necessarily mean positive or two-way engagement. For
instance, the ranking o #p2 and #ows among the top
10 suggests the mere presence o a hashtag does not
necessarily represent an endorsement.
In light o these actors, we recommend revisiting this
research ater the election and caution against drawing
overbroad conclusions about the relationship between
mainstream conservative politics and white nationalism.
Despite all this, we believe these results are neither invalid
nor insignicant. The context and timing may have infated
numbers regarding mainstream political engagement, but
the social networks involvement in electoral politics is
itsel interesting and signicant.
Additional research over time may help illuminate the
strength and persistence o these patterns, but we suspect
it would not erase them. We discuss the implications o
these ndings urther in the CVE recommendations below.
2 https://www.acebook.com/groups/TwitterAttack/, retrieved October 20123 HASHTAGS # or Waging Political Warare: Use these tags i youre using
twitter posted on a militia Web site monitored by Intelwire.com on September
15, 2012
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Comparison Group
Ater reviewing the rst set o results, we perormed
supplemental data collection in October using a seed group
o eight well-known and overtly anarchist Twitter accounts in
order to urther test the scoring system and try to capture a
better picture o how mainstream political discourse relates
to extremist movements aside rom white nationalism.
Anarchism is by denition less leadership-oriented, which may
skew comparisons in various ways. It is also, by its nature,less clearly dened, making the categorization o accounts
more dicult. For instance, the Occupy movement has
strong anarchistic components, but Occupy is not generally
considered anarchist by denition.
We collected rom ewer accounts than we did WNs, but the
eight anarchist seeds had more ollowers than the 12 white
national accounts (5,409 versus 3,542). In part, this likely
refects the lesser social stigma associated with anarchism
compared to white nationalism, as well as an anecdotally
observed tendency o anarchists to be younger and more
technologically procient than white nationalists.
Based on anecdotal observation, we would also argue it is
easier to be casually involved with anarchism than it is to be
casually involved with ideological white nationalism. Users
who sel-identied with a variation o the word anarchist oten
included several other interests in their sel-identication.
Users who sel-identied as white nationalist were much more
prone to identiy solely with that ideology.
For all o these reasons, we believe that anarchism likely
represents the most dicult ideological extremism challenge
or metrics-based identication.
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Identifcation Rate
O the 100 ollowers o anarchist seeds with the highest
interactivity scores, 70 percent overtly engaged on
Twitter with anarchism; engaged with very closely related
movements, including the Black Bloc segment o the Occupy
movement and Anonymous; or sel-described as radical or
revolutionary.
Another 20 percent overtly engaged in tweets with Occupy
but not Black Bloc, or overtly engaged with strongly anti-
government letist movements, making their presence clearly
relevant, while not precisely matching the denition o the
seeds. Depending on how you classiy that 20 percent, the
identication rate alls between 70 and 90 percent or the top
100, compared to 95 percent or white nationalists.
Fity-eight o the top 100 accounts sel-identied as anarchist,
Black Bloc, Anonymous or something clearly within the same
ideological space. Another 18 sel-identied with Occupy,
communism or socialism, but did not sel-identiy
as anarchist.
100
80
60
40
20
PERCENT
Anarchists AnarchistsWhite Nationalists White Nationalists
SELF-IDENTIFICATION TWEETS
TOP 100 MOST INTERACTIVE, COMPARISON
Not overtly engaged
Engaged with relatedideology
Overtly engaged
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This sel-identication rate o 58 percent to 76 percent
(depending how tightly one denes the target category)
compared to a sel-identication rate o 71 percent or the
top 100 most interactive members o the white nationalistollower dataset.
Given that anarchism is extremely diverse and resistant to
consistent labeling and movement cohesion, and given the
lower threshold or casual involvement, we believe these
results are extremely encouraging, especially given that only
about 10 percent o the entire dataset sel-identied with a
variation o the word anarchist.
In light o these ndings, we believe the scoring system can
be eective at identiying ollowers who are highly engaged
in ideological extremism in social networks revolving around
seed accounts with a clear ideological orientation, regardless
o what specic ideology is being examined.
Content o Tweets
3%
SOURCES BY
NUMBER OF LINKS
Identifiable content
links
4%
8%
13%
9%
15%
45%
3%
Alternative
Mainstream news
Extremist
Youtube
Facebook
Wordpress
Blogspot
All other content-neutral, includinglink shorteners
CONTENT-NEUTRAL
LINKS
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Links and hashtags used by the anarchist seed accounts
and their ollowers somewhat predictably skewed to the
let side o the political spectrum. However, they tweeted
considerably less content related to partisan politics thanWN seeds and ollowers.
In addition to being a larger dataset, the anarchist ollowers
were also prolic tweeters. We analyzed 188,573 links
(the top 85 percent o the 220,214 collected) and ound
that ollowers o anarchist seeds relied somewhat more
on mainstream news than white nationalists (15 percent
compared to 13 percent), while relying much less on clearly
extremist sites (3 percent versus 8 percent). The numbero content neutral links was the same, 73 percent, with the
remaining links going to alternative sources.
Categorizations were even more subjective or this dataset
than or the original. For instance, it was dicult to determine
whether Occupy, Wikileaks, Anonymous and hacker sites
should be considered as extremist sites relevant to the seed
ideology.
For purposes o the analysis above, all o these were counted
as extremist. I they were discounted, extremist sites would
have represented only 1.4 percent o the total mix, ar less
than the 7 percent tweeted by the white nationalist dataset.
Alternative sites were overwhelmingly liberal in their
orientation, although debatably less partisan. For instance,
Democracy Now, Mother Jones and Salon were among the
top 10 alternative sites, but the latter two especially have
requently and sharply criticized the Obama administration
on topics such as law enorcement, surveillance, oreign
policy and the use o drones.
The hashtags oer a better window or comparing the
political proclivities o ollowers o white nationalist seeds
versus ollowers o anarchist seeds. The top 10 hashtags
used in the anarchist dataset were:
1. ows (Occupy Wall Street)
2. occupy
3. oo (Occupy Oakland)
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4. occupydenver
5. anonymous
6. p2 (used to engaged with progressives)
7. (Follow Friday)8. s17 (September 17, the anniversary o the ounding
o Occupy Wall Street)
9. occupywallstreet
10. occupyoakland
Compared to the top 10 hashtags used by white nationalists,
these tags suggest a much higher disenranchisement rom
mainstream politics. Again, this may relate to the basic nature
o anarchism, which is undamentally opposed to politicalinstitutions, compared to white nationalism, which is not
opposed to institutions per se.
Overall, we interpreted these results to indicate anarchists,
while clearly embracing many liberal values, were ar less
engaged with partisan liberal politics than white nationalists
with partisan conservative politics.
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CVE Implications and
Recommendations
The rst challenge in conducting any type o Countering
Violent Extremism (CVE) program is nding the target
audience. Given the ease o access to extremism online and
the allure o large numbers, many CVE eorts have ocused
on social media as a hunting ground or extremists.But while
it is relatively simple to collect large datasets o potential
extremists online, it is much more challenging to identiy
people who are suciently engaged to be suitable targets
or CVE. Online activity oten involves a lot o bad behavior
and indiscriminate sampling o ideas. Shielded by physical
0
20
40
60
80
100
PERCENT
Random
sample
Top 400 Top 200 Top 100 Top 50
RANK ACCORDING TO INTERACTIVITY SCORE
USERS OVERTLY ENGAGING WITH WHITE NATIONALISM
Not overtly engaged
Overtly engaged
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distance and anonymity, people oten express views online
that they would rarely, i ever, express in their ofine lives.
In short, the vast majority o people taking part in extremisttalk online are unimportant. They are casually involved,
dabbling in extremism, and their rhetoric has a relatively
minimal relationship to the spread o pernicious ideologies
and their eventual metastasization into real-world violence.
Any CVE program must begin by siting the wheat rom
this cha.
While this study is not an end-state solution to the sorting
problem, we believe it represents a signicant step orward.As shown in the graph above, a higher interactivity score
clearly corresponded to a higher probability that a user was
overtly engaged with the ideology espoused by the seed
accounts. Ranked by the interactivity metric, we ound that
95 o the 100 highest-scoring members o the ollowers
dataset were highly engaged with white nationalist ideology.
These results are not a substitute or manual examination o
user activity, but they provide a way to take a snapshot o a
large user base and almost instantly prioritize the dataset,
isolating ruitul avenues o investigation. For instance, a
manual examination o the ollowers o seed white nationalist
accounts took several days and identied only two or three
ollower accounts that subjectively appeared to be very
infuential. The metrics identied the same accounts and
many more while requiring much less direct investment o
time and eort by a human analyst.
CVE initiatives are not limited to one type o extremism.
They can cover diverse ideologies such as jihadism, white
nationalism, black nationalism, anti-Muslim, sovereign citizen,
anarchism, eco-terrorism and more.
Based on the comparison results or anarchists, we believe
this is an o-the-shel approach that can be applied to any
ideology without customization. However we do believe the
metrics and analysis can be improved, perhaps substantially,
with urther research and testing.
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We urther believe that the paradigm o counting and
weighting interactions can be adapted to any social network
where an adequate amount o data can be scraped using
automated tools.
In addition to identication, we believe the scoring
system applied in this study has several other quantitative
applications to CVE programs.
Measurement
CVE takes various orms. The most-discussed avenuesinvolve persuasion (convincing potential extremists not to
pursue violent ideologies) and disruption (preventing networks
that promote radicalization rom unctioning eectively).
These eorts can be conducted by government or by non-
governmental organizations.
Critics o current CVE initiatives, including co-author J.M.
Berger, have questioned the eectiveness o many CVE
programs, pointing to the act that there are no meaningul
methods to measure success, especially as it pertains to
persuasion programs. CVE strategies to date have been
guided largely by intuition and anecdotal observation rather
than by clearly relevant metrics.4
The infuence, exposure and interactivity scores showed
remarkable success at identiying highly engaged white
nationalists within the starting set. Manual inspection o
the top-ranked accounts also suggests that the scores
themselves can be interpreted as they were intended as
quantitative measures o engagement-related activity.
Furthermore, the scores are not proportions or percentages;
they measure the temperature o a given community, which
means that a drop in score represents an absolute drop in
the property (such as infuence) being measured. So i the
sum o all infuence scores in the dataset drops, it suggests
4 J.M. Berger, Intelwire.com, Finding a Way Forward or CVE, 19 August 2011,http://www.oreignpolicy.com/articles/2011/12/09/monsters_and_children; Will
McCants and Clint Watts, Foreign Policy Research Institute, U.S. Strategy or
Countering Violent Extremism: An Assessment, December 2012
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that the leaders o the dataset have become less infuential.
I interactivity scores drop, it suggests that engagement
with the target ideology by members o the dataset has
been reduced.
Put simply, we believe the scoring system can be used to
measure the absolute amount o engagement within a targeted
online community in real time and over extended periods.
This opens the door to quantitative testing o dierent CVE
approaches. For instance, the scoring system can measure
eorts to diminish the infuence o key leaders, to inoculate
a target audience against exposure, or simply to depress all
interaction in the dataset.
Several CVE approaches are suggested by these conclusions.
Targeting Inuence
INFLUENCESCORE
USERS RANKED BY INFLUENCE
12000
10000
8000
6000
4000
2000
140
79
118
157
196
235
274
313
352
391
430
469
508
547
586
625
664
703
742
781
820
859
898
937
976
1015
1054
1093
1132
1171
1210
1249
1288
1327
1366
Infuence was disproportionately distributed throughout users with a nonzero score, asshown above. More than 50 percent o all the infuence in the system was attributed to just
54 accounts, or 1.5 percent o the total number o accounts. The chart above shows the
curve or all users with nonzero infuence scores rom highest to lowest.
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Our understanding o infuence in the real world is airly
simple. A relative ew individuals wield a tremendous amount
o infuence over much larger numbers o people. Our ndings
put some specicity on that intuitive concept.
The vast majority o infuence in the WN dataset was
disproportionately concentrated at the top, as seen in the
chart above. The top 50 most-infuential users, looking this
time at both ollowers and seeds just 1.4 percent o the
total accounted or nearly 46 percent o all the infuence in
the system. (Because o their prominence, the seeds become
more relevant when targeting accounts or CVE eorts.)
These ndings generally conorm to other studies o online
activity, including the well-known 90-9-1 rule, which states 90
percent o users in most online social networks are passive, 9
percent are somewhat engaged and 1 percent drive most o
the engagement and discussion.5
We attempted to enhance measures o simple engagement
by weighting interactions to emphasize users with greater
reach and who provide content that is widely redistributed
and deemphasize users whose engagement carried less
weight in group dynamics. We expect the scoring system
can be rened to improve these measurements urther, but it
perormed very well out o the gate.
The top 10 most infuential accounts (less than 0.3 percent
o all sampled users) accounted or 24.8 percent o the total
infuence in an ecosystem o 3,554 Twitter accounts (ollowers
plus seeds) a massively disproportionate distribution. This
suggests the system is extremely vulnerable to disruption by
ocusing CVE eorts at the top o the ood chain.
Possible methods or disruption include submitting complaints
over violations o terms o service, where applicable;
challenging and discrediting infuencers through investigative
5 Jakob Nielsen, Nielsen Norman Group, Participation Inequality: Encouraging
More Users to Contribute, 9 October 2006, http://www.useit.com/alertbox/participation_inequality.html; Ben McConnell, The Church o the Customer
Blog, The 1% Rule: Charting citizen participation, 3 May 2006, http://www.
churchothecustomer.com/blog/2006/05/charting_wiki_p.html
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reporting and research; or challenging and discrediting
infuencers through open debate in the online sphere.
Infuence was disproportionately distributed even aterremoving the top 50 users. The chart above shows the curve
or all remaining users with nonzero infuence scores.
Ater the top 50, a sharp curve remained even among the
users who contributed the lower 50 percent o infuence to
the system. While this suggests there would be a diminishing
benet to removing infuencers over time, the distribution
remains top-heavy, with the second 50 (1.4 percent o the
remaining sampled users) comprising slightly more than 27percent o the remaining infuence. This suggests targeting
the top o the curve can be eective even ater the ecosystem
has suered signicant attrition.6
While it is impossible to know or certain without direct
experimentation, we believe these curves suggest disruptive
CVE aimed at the top can be very eective over the course o
a long engagement.
Another CVE option is to aim disruptive techniques at those
with the highest exposure or interactivity scores. We originally
suspected that high exposure scores might correlate to
people at risk o radicalization who could be targeted or
personalized interventions by nonprot organizations. What
we discovered instead was that users with high exposure
scores were extremely committed to white nationalism and
very active participants in the network.
While interventions seem unlikely to be eective with such
users, disruption might be eective. The distribution o both
exposure and interactivity was less concentrated at the
top than infuence (with interactivity slightly outperorming
exposure), but the distribution remained disproportionately
top-heavy or both metrics.
6 These calculations do not account or the redistribution o infuence whenusers are removed rom the system, with some o the remaining accounts
becoming more prominent and thus more infuential. Nor do they account or
the reemergence o removed users under new Twitter handles.
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Our evaluation o users with high exposure but medium to
low infuence is that they were mainly reactive, unctioning
as a supportive cheering section but not necessarily directly
driving the content o discussions (which is not the same assaying their role in the system is insignicant). However, the
interactivity metric was signicantly more eective than any
other method at identiying highly engaged users. Since users
cannot be infuential without other users who are exposed
to them, we suspect that targeting the highest interactivity
scores might have benets that are not obvious but could
become visible when scores are tracked over time.
Finally, we should note that the infuence distribution wasdierent or anarchists than or white nationalists. The
distribution o infuence or the anarchists was much fatter,
but still quite top heavy, with hal the infuence in the system
residing among the top 106 accounts. This suggests the
approach may need to be ne-tuned or dierent ideologies,
but that the basic principles will remain eective.
Targeting ContentDisruptive CVE approaches on Twitter are limited by broadly
permissive Terms o Service. Social media platorms in
general are biased towards reedom o speech, although it
should be noted that reedom o speech is not synonymous
with the reedom to use a specic companys services.
Providers o Internet publishing platorms are not obligated
to host content in violation o their published rules, and they
have wide latitude in deciding what those rules should be.
Twitters TOS as o October 2012 banned direct, specic
threats o violence against others and the use o Twitter to
conduct or promote illegal activities and loosely dened
abusive behavior.
These terms are suciently ambiguous to allow designated
global terrorist groups like Al Shabab to maintain active
accounts on Twitter (although Shababs account was recently
suspended or a direct threat o violence, it has since opened
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a new account).7 The Taliban and many other extremists also
use Twitter extensively. However several prominent extremist
accounts have been suspended or unknown reasons as o
December 2012. This may refect a shit in policies, or it may
be due to other unknown reasons.
Regardless, disruption need not be limited to Twitter, thanks
in part to the content analysis, which shows the most
signicant external sources o content. A large part o Twitters
value as a social media tool involves the ability to provide links
and steer users to more detailed material on other Web sites.
For instance, YouTube emerged as the most-linked source o
content in the dataset, representing more than 21 percent o
all links and 49 percent o the top 10 links. Facebook, Blogger
and Wordpress also ranked among the top 10 sources, even
7 BBC News, Somalias al-Shabab opens new Twitter account, 4 Feburary
2013, http://www.bbc.co.uk/news/world-arica-21321687
2%
TOP 10 USER-
GENERATED
CONTENT SOURCES
BY NUMBER
OF LINKS
3%
3%
5%
5%
9%
15%
49%
6%
3%
acebook.com
youtube.com
blogspot.com
twitter.com
tumblr.com
wordpress.com
instagram.com
oursquare.com
twitpic.com
yahoo.com
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without accounting or sites that use those services under a
custom domain. As noted previously, we could not determine
the nature o the content being linked to under these
domains, but its likely a signicant percentage o these linkspoint to user-generated content related to white nationalism.
Terms o service vary or each o these online publishing
platorms. YouTube has relatively robust tools or reporting
and removing hate speech, including the ability to report
users or violent or repulsive, and hateul or abusive
content, with specic subcategories or promoting terrorism
and violence and hatred.
CVE practitioners can also make note o which subsets o a
sampled dataset are linking to content, or instance targeting
links provided by the most infuential, exposed or interactive
users, and report TOS violations, i any exist, with the
content publisher.
Intervening with At-Risk Individuals
Manual inspection o the highest infuence, exposure and
interactivity scores ound that almost all o the top 50 in each
category were highly committed white nationalists unlikely to
be swayed by intervention.
The percentages dropped steadily and consistently as the
view expanded to the top 100 and 200 accounts. We believe
manually examining users whose interactivity rank alls
between 200 and 500 might be ruitul in identiying people
vulnerable to radicalization or in its early stages, whose
interactions indicate an interest in an extremist ideology but
not a single-minded obsession with it.
Additional research is required to evaluate these ndings
in order to capture the characteristics o at-risk users
and evaluate possible approaches to online intervention.
Approaches can be eld-tested and the scoring system
can be used to help evaluate the results, although manual
inspection and contextualization will likely be required. For
instance, i an at-risk user is questioning the wisdom o
pursuing white nationalism as a personal ideology, he or she
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might temporarily become more interactive while seeking to
test assumptions and challenge infuencers.
Since radicalization is a process, the rankings are onlyrelevant to this dataset during the time period o collection,
so scores should be recalculated prior to experimentation.
Additional research might eventually point to ranges o
infuence, exposure and interactivity where the highest
percentage o at-risk individuals can be ound. Some
adjustments to the scoring system would be necessary
in order to support a universal range-based approach.
CVE partners who undertake this research should keep
detailed notes on the scores, ranks and proles o individuals
they approach and the results o their intervention in order to
urther rene the most eective target ranges.
Countering Extremist Narratives
A popular approach to countering violent extremism involves
creating and promoting narratives that oppose extremist
ideologies, in the hopes o undermining existing extremist
messages and competing or the hearts and minds o
potential recruits.
Although such eorts can be evaluated by how many
participants they attract, their utility in actually countering
extremism remains unclear. For instance, accumulating
1,000 social media users who promote messages opposed to
racism is an achievable goal, but its not clear how that eort
lands in at-risk communities and whether people already
attracted to white nationalism are positively infuenced by
such eorts or i they may even be urther alienated by them.
The metrics developed in this paper oer the possibil ity o
perorming quantitative evaluations or online messaging
eorts. For instance, i the eort is centered around a Web
site, CVE practitioners can track whether the links to the site
are being tweeted by members o a targeted dataset, how the
site is being characterized and whether infuential users are
discussing it.
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I the eort is being promoted by specic Twitter accounts,
those accounts can be introduced into the dataset and their
infuence can be tracked. Similarly, eorts to sway sentiment
among specic subsets o users (or even individuals) canbe tracked and scored to measure their success or ailure
in the online context. For instance, i the goal is to diminish
the reach o the seed accounts, it is possible to track the
infuence o those accounts, whether users have exited the
dataset by unollowing them, and so orth.
It should be noted that it is possible to game the system, so
CVE practitioners must be coached on how to achieve honest
results, although it is also possible that gaming the systemcould in some cases actually achieve the desired result.
Further study and rigorous analysis o uture outcomes will
be required to evaluate how the scoring system can best be
applied to such initiatives.
Reactive Opportunities to Counter
Extremist Narratives
The political content observed during data collection strongly
suggests that white nationalist seed accounts, their Twitter
ollowers and the most engaged subsets o their ollowers
believe that mainstream political conservatives represent
potential recruits, potential allies, or at minimum, persons
with whom they share some interests.
Regardless o exactly why, the data indisputably shows
that during the collection period, people engaged with
WN ideology consumed and distributed content related to
mainstream political conservatism and tried to engage with
mainstream conservatives.
Many mainstream conservatives likely nd these approaches
oensive and choose to ignore them, but this outreach
represents opportunities to intervene with people at various
stages in the radicalization process. I someone reaches
out to infuence you, you gain an opportunity to infuence
them back.
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Conservatives who are politically active online may
thereore be better positioned than anyone to attempt CVE
interventions with white nationalists. We strongly recommend
that politically active conservatives consider ways they canspearhead or aggressively contribute to such CVE eorts by
directly challenging white nationalists who attempt to get their
attention through dierent styles o interaction online.
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Data at a glance
Data collected
Seed accounts 12
Follower dataset 3,542
Tweets collected 342,807
Total number o links 84,825
Distinct domains linked to 676
Total number o hashtags 69,476
Unique hashtags 1,240
Identifcation rates byscore based on randomsample o 350 accounts(margin o error+/- 5.4 percent)
% overtly engagedwith WN as observedin manual inspectiono accounts
All ollowers 44
Infuence (Top 200) 73.5
Infuence (Top 100) 86
Infuence (Top 50) 98
Exposure (Top 200) 83
Exposure (Top 100) 93
Exposure (Top 50) 100
Interactivity (Top 200) 83
Interactivity (Top 100) 95
Interactivity (Top 50) 98
Link content by percentage
Mainstream news 12
Alternative 7
Extremist 8
Content neutral 73 15.9 (could not bedetermined)
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Appendices
A: Seed accounts
The ollowing seed accounts were manually identied or
collection based on co-author J.M. Bergers previous research
on white nationalist movements. Each account was associated
with a well-known ideological white nationalist organization or
individual. The organizational aliation or each account is
given in parentheses.
1. @HeimdallsGhost (White Aryan Resistance)
2. @American3rdP (American 3rd Position)
3. @ANP14 (American Nazi Party)
4. @AryanNations (Aryan Nations, Morris Gullett branch)
5. @david_duke (David Duke)
6. @jartaylor (Jared Taylor, American Renaissance)
7. @TheNewNaziParty (American Nazi Party)
8. @nwront (Northwest Front)
9. @UKANW (Northwest chapter o The United Klans o
America)
10. @UKASOUTHFLA (Southern chapter o The United Klans
o America)
11. @TheHoodedone88 (Aliated with the United Klans)
12. @NSFM_Commander (Edward McBride, National Socialist
Freedom Movement)
The number o seed accounts was designed to be large
enough to produce substantial amounts o data, but small
enough to keep collection rom becoming prohibitively time-
and resource-consuming.
B: Scoring ormula and glossaryo metrics
For each interaction, points were awarded or infuence and
exposure. For instance, i Account A retweeted a post by
Account B, Account B received points or infuencing Account
A, while Account A received points or exposure to Account B.
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I account C tweeted many links that were requently
retweeted, but never directly replied to or retweeted other
users, Account C would accrue a high infuence score but a
low exposure score. I Account D sent replies to many users,but only a ew responded, Account D would accrue a high
exposure score, but a low infuence score.
I Account E oten engaged in two-way conversations with
other users, Account E would register high scores in both
infuence and exposure.
Interactions were scored as ollows:
at-replies: 10
retweets (RT): 5
hat-tip (HT): 20
modifed tweet (MT): 15
non-reply mention: 2
any o the above actions in a tweet with a high-
value keyword: 25. The list o high-value keywords
included reerences to private communications, and
ofine and real-lie interactions such as call, email,
or DM (direct message). These terms were chosen
on the theory that the most infuential members o the
network would be those who extend their infuence
beyond Twitter.
ollowing: For Account A ollowing Account B, scores
were awarded between one and 10 based on the
average number o tweets per day. For instance, i
Account B tweeted an average o one or ewer times
per day, Account B received one point or infuence and
Account A received one point or exposure. I Account
B tweeted an average o 10 times per day or more,
the score was capped at 10 in order to avoid unduly
weighting scores toward users who were simply prolic.
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We experimented with several dierent parameters or the
social network we would measure infuence and exposure
to the seeds, infuence and exposure among ollowers o
the seeds, and infuence and exposure to ollowers o theollowers. Based on manual examination, the second category
was easily the most interesting and eective. Except where
explicitly noted, all reerences to infuence and exposure are
limited by the parameter, interactions only among ollowers
o the seeds.
A quick overview o the measurements produced:
Infuence: A score that measures the ability o one twitteruser to prompt other Twitter users to redistribute or
engage with content.
Infuential: A Twitter user who has a high infuence score
and prompts many other users to engage with content.
Exposure: A score that measures the tendency o one
Twitter user to engage with or redistribute content
created by other users.
Exposed: The measured tendency o a given Twitter user
to engage with or redistribute content.
Interactivity: The sum o a users infuence and exposure
score, refecting quantity and quality o the users overall
interactions with other users.
Interactive: The tendency o a user to interact with
other users.
C: Top 10 most inuential
1. Eaglesnest1488
2. MAreedom
3. GILLY1488
4. MarmiteMan4
5. WARRIOR33_6
6. Aryanliving
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7. Par_Sjogren
8. emilyscreams18
9. johnnywhiter
10. whiteunity14
D: Top 10 most exposed
1. WARRIOR33_6
2. danielb1714
3. Eaglesnest1488
4. karlhanke32
5. whiteunity146. HellWar88
7. DogoWarN
8. xdtac
9. craig25081989
10. viking14ranger
E: Top 20 most interactive
1. Eaglesnest1488
2. MAreedom
3. WARRIOR33_6
4. GILLY1488
5. whiteunity14
6. Aryanliving
7. MarmiteMan4
8. johnnywhiter
9. danielb1714
10. WhiteResister
11. KevinGoudreau
12. Par_Sjogren
13. emilyscreams18
14. IvanaWAU
15. HVRabbit
16. DogoWarN
17. B14USA
18. xdtac
19. PaulinaForslund
20. craig25081989
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F: Glossary o Twitter Terms
Account: An account registered with Twitter. These are
usually, but not always, managed by a single user. Accountsmay also be congured automatically tweet content rom a
Web site.
Handle: The username chosen by a Twitter account holder.
Tweet: A short message posted on Twitter, limited to 140
characters in length. Tweets may be simple comments or
replies to other users, and may include links to content rom
other Web sites.
Follow: When a Twitter user subscribes to another
users tweets.
Followers: A list o all users that ollower a specic
Twitter account.
Followed: A list o all users ollowed by a specic Twitter
account, sometimes reerred to as riends.
Timeline: A users view o all tweets posted by the people
they ollow.
Reply or @reply: When a user directs a comment publicly to
another user. This is done by placing an @ sign in ront o
the target users handle.
Direct Message: When two users ollow each other, Twitter
allows them to communicate privately through this service.
Retweet or RT: When a user republishes another users
tweet, with attribution.
Modifed Tweet or MT: A retweet in which the user modies
the text o the original tweet.
Hat-Tip or HT: A tweet that acknowledges another user
as the source o a link.
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Non-Reply Mention: Including another users handle in a
tweet that is not a response to a previous tweet by that user.
This oten unctions as the equivalent o CCing someone on
an e-mail message.
Hashtag: When a Twitter users highlights a term in a tweet
by placing the # sign in ront o a word or phrase. Hashtags
can be used or emphasis but are more oten intended to
make the tweet show up in thematic searches by other users.
G: Disclosures
This paper was commissioned by Google Ideas, but the
opinions expressed are those o the authors.
A minimum o 11 accounts in the set were noted in the
data to be ollowing co-author J.M. Bergers Twitter account
(@intelwire) at the time o collection. Bergers Twitter eed
ocuses on violent extremism and national security.
The most-linked URL in the white nationalist dataset was
YouTube.com, which is owned by Google, o which Google
Ideas is a part. The sixth most-linked URL was Blogspot.com,
another Google property. We considered a number o
online platorms or this study, including YouTube, but
Twitter was ultimately chosen due to its more accessible
API or developers, and the relative transparency o its
social transactions.
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About ICSR
ICSR is a unique partnership o
Kings College London, the University
o Pennsylvania, the InterdisciplinaryCenter Herzliya (Israel) and the
Regional Center or Confict Prevention
at the Jordan Institute o Diplomacy.
Its aim and mission is to bring together
knowledge and leadership to counter
the growth o radicalisation and
political violence. For more inormation,
see www.icsr.ino