Social Turing Tests: Crowdsourcing Sybil Detection
Gang Wang, Manish Mohanlal, Christo Wilson, Xiao Wang ‡ ,
Miriam Metzger † , Haitao Zheng and Ben Y. Zhao
Department of Computer Science, U. C. Santa Barbara, CA USA
†
Department of Communications, U. C. Santa Barbara, CA USA
‡
Renren Inc., Beijing, China
Abstract
As popular tools for spreading spam and malware, Sybils
(or fake accounts) pose a serious threat to online communities
such as Online Social Networks (OSNs). Today, sophisticated
attackers are creating realistic Sybils that effectively
befriend legitimate users, rendering most automated Sybil
detection techniques ineffective. In this paper, we explore
the feasibility of a crowdsourced Sybil detection system for
OSNs. We conduct a large user study on the ability of humans
to detect today’s Sybil accounts, using a large corpus
of ground-truth Sybil accounts from the Facebook and
Renren networks. We analyze detection accuracy by both
“experts” and “turkers” under a variety of conditions, and
find that while turkers vary significantly in their effectiveness,
experts consistently produce near-optimal results. We
use these results to drive the design of a multi-tier crowdsourcing
Sybil detection system. Using our user study data,
we show that this system is scalable, and can be highly effective
either as a standalone system or as a complementary
technique to current tools.
1 Introduction
The rapid growth of Sybil accounts is threatening the stability
and security of online communities, particularly online
social networks (OSNs). Sybil accounts represent fake
identities that are often controlled by a small number of real
users, and are increasingly used in coordinated campaigns
to spread spam and malware [6, 30]. In fact, measurement
studies have detected hundreds of thousands of Sybil accounts
in different OSNs around the world [3,31]. Recently,
Facebook revealed that up to 83 million of its users may be
fake 1 , up significantly from 54 million earlier 2 .
1 http://www.bbc.com/news/technology-19093078
2 http://www.bbc.com/news/technology-18813237
The research community has produced a substantial
number of techniques for automated detection of Sybils [4,
32, 33]. However, with the exception of SybilRank [3], few
have been successfully deployed. The majority of these
techniques rely on the assumption that Sybil accounts have
difficulty friending legitimate users, and thus tend to form
their own communities, making them visible to community
detection techniques applied to the social graph [29].
Unfortunately, the success of these detection schemes is
likely to decrease over time as Sybils adopt more sophisticated
strategies to ensnare legitimate users. First, early
user studies on OSNs such as Facebook show that users are
often careless about who they accept friendship requests
from [2]. Second, despite the discovery of Sybil communities
in Tuenti [3], not all Sybils band together to form
connected components. For example, a recent study of
half a million Sybils on the Renren network [14] showed
that Sybils rarely created links to other Sybils, and instead
intentionally try to infiltrate communities of legitimate
users [31]. Thus, these Sybils rarely connect to each other,
and do not form communities. Finally, there is evidence that
creators of Sybil accounts are using advanced techniques
to create more realistic profiles, either by copying profile
data from existing accounts, or by recruiting real users to
customize them [30]. Malicious parties are willing to pay
for these authentic-looking accounts to better befriend real
users.
These observations motivate us to search for a new approach
to detecting Sybil accounts. Our insight is that while
attackers are creating more “human” Sybil accounts, fooling
intelligent users, i.e. passing a “social Turing test,” is
still a very difficult task. Careful users can apply intuition
to detect even small inconsistencies or discrepancies in the
details of a user profile. Most online communities already
have mechanisms for users to “flag” questionable users or
content, and social networks often employ specialists dedicated
to identifying malicious content and users [3]. While
these mechanisms are ad hoc and costly, our goal is to ex-plore a scalable and systematic approach of applying human
effort, i.e. crowdsourcing, as a tool to detect Sybil accounts.
Designing a successful crowdsourced Sybil detection requires
that we first answer fundamental questions on issues
of accuracy, cost, and scale. One key question is, how accurate
are users at detecting fake accounts? More specifically,
how is accuracy impacted by factors such as the user’s experience
with social networks, user motivation, fatigue, and
language and cultural barriers? Second, how much would
it cost to crowdsource authenticity checks for all suspicious
profiles? Finally, how can we design a crowdsourced Sybil
detection system that scales to millions of profiles?
In this paper, we describe the results of a large user study
into the feasibility of crowdsourced Sybil detection. We
gather ground-truth data on Sybil accounts from three social
network populations: Renren [14], the largest social
network in China, Facebook-US, with profiles of English
speaking users, and Facebook-India, with profiles of users
who reside in India. The security team at Renren Inc. provided
us with Renren Sybil account data, and we obtained
Facebook (US and India) Sybil accounts by crawling highly
suspicious profiles weeks before they were banned by Facebook.
Using this data, we perform user studies analyzing
the effectiveness of Sybil detection by three user populations:
motivated and experienced “experts”; crowdsourced
workers from China, US, and India; and a group of UCSB
undergraduates from the Department of Communications.
Our study makes three key contributions. First, we analyze
detection accuracy across different datasets, as well
as the impact of different factors such as demographics,
survey fatigue, and OSN experience. We found that wellmotivated
experts and undergraduate students produced exceptionally
good detection rates with near-zero false positives.
Not surprisingly, crowdsourced workers missed more
Sybil accounts, but still produced near zero false positives.
We observe that as testers examine more and more suspicious
profiles, the time spent examining each profile decreases.
However, experts maintained their accuracy over
time while crowdworkers made more mistakes with additional
profiles. Second, we performed detailed analysis
on individual testers and account profiles. We found that
while it was easy to identify a subset of consistently accurate
testers, there were very few “chameleon profiles” that
were undetectable by all test groups. Finally, we propose a
scalable crowdsourced Sybil detection system based on our
results, and use trace-driven data to show that it achieves
both accuracy and scalability with reasonable costs.
By all measures, Sybil identities and fake accounts are
growing rapidly on today’s OSNs. Attackers continue to innovate
and find better ways of mass-producing fake profiles,
and detection systems must keep up both in terms of accuracy
and scale. This work is the first to propose crowdsourcing
Sybil detection, and our user study results are extremely
positive. We hope this will pave the way towards testing
and deployment of crowdsourced Sybil detection systems
by large social networks.
2 Background and Motivation
Our goal is to motivate and design a crowdsourced Sybil
detection system for OSNs. First, we briefly introduce the
concept of crowdsourcing and define key terms. Next, we
review the current state of social Sybil detection, and highlight
ongoing challenges in this area. Finally, we introduce
our proposal for crowdsourced Sybil detection, and enumerate
the key challenges to our approach.
2.1 Crowdsourcing
Crowdsourcing is a process where work is outsourced to
an undefined group of people. The web greatly simplifies
the task of gathering virtual groups of workers, as demonstrated
by successful projects such as Wikipedia. Crowdsourcing
works for any job that can be decomposed into
short, simple tasks, and brings significant benefits to tasks
not easily performed by automated algorithms or systems.
First, by harnessing small amounts of work from many people,
no individual is overburdened. Second, the group of
workers can change dynamically, which alleviates the need
for a dedicated workforce. Third, workers can be recruited
quickly and on-demand, enabling elasticity. Finally and
most importantly, by leveraging human intelligence, crowdsourcing
can solve problems that automated techniques cannot.
In recent years, crowdsourcing websites have emerged
that allow anyone to post small jobs to the web and have
them be solved by crowdworkers for a small fee. The pioneer
in the area is Amazon’s Mechanical Turk, or MTurk
for short. On MTurk, anyone can post jobs called Human
Intelligence tasks, or HITs. Crowdworkers on MTurk, or
turkers, complete HITs and collect the associated fees. Today,
there are around 100,000 HITs available on MTurk at
any time, with 90% paying ≤$0.10 each [11,24]. There are
over 400,000 registered turkers on MTurk, with 56% from
the US, and 36% from India [24].
Social networks have started to leverage crowdsourcing
to augment their workforce. For example, Facebook crowdsources
content moderation tasks, including filtering pornographic
and violent pictures and videos [10]. However,
to date we know of no OSN that crowdsources the identification
of fake accounts. Instead, OSNs like Facebook
and Tuenti maintain dedicated, in-house staff for this purpose
[3, 10].
Unfortunately, attackers have also begun to leverage
crowdsourcing. Two recent studies have uncovered crowdsourcing
websites where malicious users pay crowdworkersto create Sybil accounts on OSNs and generate spam [21,
30]. These Sybils are particularly dangerous because they
are created and managed by real human beings, and thus appear
more authentic than those created by automated scripts.
Crowdsourced Sybils can also bypass traditional security
mechanisms, such as CAPTCHAs, that are designed to defend
against automated attacks.
2.2 Sybil Detection
The research community has produced many systems designed
to detect Sybils on OSNs. However, each one relies
on specific assumptions about Sybil behavior and graph
structure in order to function. Thus, none of these systems
is general enough to perform well on all OSNs, or against
Sybils using different attack strategies.
The majority of social Sybil detectors from the literature
rely on two key assumptions. First, they assume that Sybils
have trouble friending legitimate users. Second, they assume
that Sybil accounts create many edges amongst themselves.
This leads to the formation of well-defined Sybil
communities that have a small quotient-cut from the honest
region of the graph [4, 28, 29, 32, 33]. Although similar
Sybil community detectors have been shown to work well
on the Tuenti OSN [3], other studies have demonstrated limitations
of this approach. For example, a study by Yang
et al. showed that Sybils on the Renren OSN do not form
connected components at all [31]. Similarly, a meta-study
of multiple OSN graphs revealed that many are not fastmixing,
which is a necessary precondition for Sybil community
detectors to perform well [20].
Other researchers have focused on feature-based Sybil
detectors. Yang et al. detect Sybils by looking for accounts
that send many friend requests that are rejected by
the recipient. This detection technique works well on Renren
because Sybils must first attempt to friend many users
before they can begin effectively spamming [31]. However,
this technique does not generalize. For example, Sybils on
Twitter do not need to create social connections, and instead
send spam directly to any user using “@” messages.
Current Sybil detectors rely on Sybil behavior assumptions
that make them vulnerable to sophisticated attack
strategies. For example, Irani et al. demonstrate that “honeypot”
Sybils are capable of passively gathering legitimate
friends and penetrating the social graph [13]. Similarly,
some attackers pay users to create fake profiles that bypass
current detection methods [30]. As Sybil creators adopt
more sophisticated strategies, current techniques are likely
to become less effective.
2.3 Crowdsourcing Sybil Detection
In this study, we propose a crowdsourced Sybil detection
system. We believe this approach is promising for
three reasons: first, humans can make overall judgments
about OSN profiles that are too complex for automated algorithms.
For example, humans can evaluate the sincerity
of photographs and understand subtle conversational nuances.
Second, social-Turing tests are resilient to changing
attacker strategies, because they are not reliant on specific
features. Third, crowdsourcing is much cheaper than hiring
full-time content moderators [9, 25]. However, there
are several questions that we must answer to verify that this
system will work in practice:
• How accurate are users at distinguishing between real
and fake profiles? Trained content moderators can perform
this task, but can crowdworkers achieve comparable
results?
• Are there demographic factors that affect detection accuracy?
Factors like age, education level, and OSN experience
may impact the accuracy of crowdworkers.
• Does survey fatigue impact detection accuracy? In
many instances, people’s accuracy at a task decline over
time as they become tired and bored.
• Is crowdsourced Sybil detection cost effective? Can the
system be scaled to handle OSNs with hundreds of millions
of users?
We answer these questions in the following sections.
Then, in Section 6, we describe the design of our crowdsourced
Sybil detection system, and use our user data to
validate its effectiveness.
3 Experimental Methodology
In this section, we present the design of our user studies
to validate the feasibility of crowdsourced Sybil detection.
First, we introduce the three datasets used in our
experiments: Renren, Facebook US, and Facebook India.
We describe how each dataset was gathered, and how the
ground-truth classification of Sybil and legitimate profiles
was achieved. Next, we describe the high-level design of
our user study and its website implementation. Finally, we
introduce the seven groups of test subjects. Test subjects
are grouped into experts, turkers from crowdsourcing websites,
and university undergraduates. We use different test
groups from China, the US, and India that correspond to our
three datasets. All of our data collection and experimental
methodology was evaluated and received IRB approval before
we commenced our study.
3.1 Ground-truth Data Collection
Our experimental datasets are collected from two large
OSNs: Facebook and Renren. Facebook is the most popular
OSN in the world and has more than 1 billion users [8].
Renren is the largest OSN in China, with more than 220Facebook
Unknown
Profiles
Google Image
Search
Suspicious
Profiles
Friends of Suspicious Profiles
Dataset
Figure 1. Facebook crawling methodology.
million users [14]. Both sites use similar visual layouts and
offer user profiles with similar features, including space for
basic information, message “walls,” and photo albums. Basic
information in a profile includes items like name, gender,
a profile image, total number of friends, interests, etc.
Each dataset is composed of three types of user profiles:
confirmed Sybils, confirmed legitimate users, and suspicious
profiles that are likely to be Sybils. Confirmed
Sybil profiles are known to be fake because they have been
banned by the OSN in question, and manually verified by
us. Suspicious profiles exhibit characteristics that are highly
indicative of a Sybil, but have not been banned by the OSN.
Legitimate profiles have been hand selected and verified by
us to ensure their integrity. We now describe the details of
our data collection process on Facebook and Renren.
Facebook. We collect data from Facebook using a custom
web crawler. Because Facebook caters to an international
audience, we specifically targeted two regional areas
for study: the US and India. We chose these two regions
because they have large, Internet enabled populations,
and both countries have active marketplaces for crowdworkers
[24]. Our Facebook crawls were conducted between December
2011 and January 2012.
The legitimate profiles for our study were randomly selected
from a pool of 86K profiles. To gather this pool of
profiles, we seeded our crawler with 8 Facebook profiles belonging
to members of our lab (4 in the US, and 4 in India).
The crawler then visited each seed’s friends-of-friends, i.e.
the users two-hops away on the social graph. Studies have
shown that trust on social networks is often transitive [18],
and thus the friends-of-friends of our trusted seeds are likely
to be trustworthy as well. From the 86K total friends-offriends
in this set, the crawler sampled 100 profiles (50 from
the US, 50 from India) that had Facebook’s default, permissive
privacy settings. We manually examined all 100 profiles
to make sure they were 1) actually legitimate users,
and 2) we did not know any of them personally (to prevent
bias in our study).
To facilitate collection of Sybils on Facebook, we make
one assumptions about Sybil behavior: we assume that
Sybils use widely available photographs from the web as
profile images. Intuitively, Sybils need realistic profile images
in order to appear legitimate. Hence, Sybils must resort
to using publicly available images from around the web. Although
all Sybils on Facebook may not obey this assumption,
we will show that enough do to form a sufficiently
large sample for our user study.
To gather suspicious profiles, we seeded our crawler with
the profiles of known Sybils on Facebook [1]. The crawler
then snowball crawled outward from the initial seeds. We
leveraged Google Search by Image to locate profiles using
widely available photographs as profile images. Figure
1 illustrates this process. For each profile visited by the
crawler, all of its profile images were sent to Google Search
by Image (Facebook maintains a photo album for each user
that includes their current profile image, as well as all prior
images). If Google Search by Image indexed ≥90% of the
profile images on sites other than Facebook, then we consider
the account to be suspicious. The crawler recorded the
basic information, wall, and photo albums from each suspicious
profile. We terminated the crawl after a sufficient
number of suspicious profiles had been located.
We search for all of a user’s profile images rather than
just the current image because legitimate users sometimes
use stock photographs on their profile (e.g. a picture of their
favorite movie star). We eliminate these false positives by
setting minimum thresholds for suspicion: we only consider
profiles with ≥2 profile images, and if ≥90% are available
on the web, then the profile is considered suspicious.
In total, our crawler was able to locate 8779 suspicious
Facebook profiles. Informal, manual inspection of the profile
images used by these accounts reveals that most use pictures
of ordinary (usually attractive) people. Only a small
number of accounts use images of recognizable celebrities
or non-people (e.g. sports cars). Thus, the majority of profile
images in our dataset are not suspicious at first-glance.
Only by using external information from Google does it become
apparent that these photographs have been misappropriated
from around the web.
At this point, we don’t have ground-truth about these
profiles, i.e. are they really Sybils? To determine groundtruth,
we use the methodology pioneered by Thomas et al.
to locate fake Twitter accounts [27]. We monitored the suspicious
Facebook profiles for 6 weeks, and observed 573
became inaccessible. Attempting to browse these profiles
results in the message “The page you requested was not
found,” indicating that the profile was either removed by
Facebook or by the owner. Although we cannot ascertain
the specific reason that these accounts were removed,
the use of widely available photographs as profile images
makes it highly likely that these 573 profiles are fakes.
The sole limitation of our Facebook data is that it only
includes data from public profiles. It is unknown if the characteristics
of private accounts (legitimate and Sybil) differ
from public ones. This limitation is shared by all studies
that rely on crawled OSN data.
Renren.
We obtained ground-truth data on Sybil and# of Profiles
# of Profiles
Dataset
Test Group
Sybil Legit. Testers per Tester
CN Expert 24 100
Renren 100 100
CN Turker 418 10
US Expert 40 50
Facebook
32 50 US Turker 299 12
US
US Social 198 25
Facebook
IN
50 50
IN Expert 20 100
IN Turker 342 12
Table 1. Datasets, test groups, and profiles
per tester.
Dataset
Renren
Facebook
US
Facebook
IN
Category
News-
Profile Censored
Photos
Feed
Images Images
Legit. 165 302 10 0
Sybil 30 22 1.5 0.06
Legit. 55.62 184.78 32.86 0
Sybil 60.15 10.22 4.03 1.81
Legit. 55 53.37 7.27 0
Sybil 31.6 10.28 4.44 0.08
Table 2. Ground-truth data statistics (average
number per profile).
legitimate profiles on Renren directly from Renren Inc. The
security team at Renren gave us complete information on
1082 banned Sybil profiles, from which we randomly selected
100 for our user study. Details on how Renren bans
Sybil accounts can be found in [31]. We collected legitimate
Renren profiles using the same methodology as for Facebook.
We seeded a crawler with 4 trustworthy profiles from
people in the lab, crawled 100K friends-of-friends, and then
sampled 100 public profiles. We forwarded these profiles to
Renren’s security team and they verified that the profiles
belonged to real users.
Summary and Data Sanitization. Table 1 lists the final
statistics for our three datasets. Since the Renren data was
provided directly by Renren Inc., all profiles are confirmed
as either Sybils or legitimate users. For Facebook US and
India, profiles that were banned by Facebook are confirmed
Sybils, and the remaining unconfirmed suspicious profiles
are not listed.
During our manual inspection of profiles, we noticed that
some include images of pornography or graphic violence.
We determined that it was not appropriate for us to use
these images as part of our user study. Thus, we manually
replaced objectionable images with a grey image containing
the words “Pornographic or violent image removed.”
This change protects our test subjects from viewing objectionable
images, while still allowing them to get a sense of
the original content that was included in the profile. Out
of 45,096 total images in our dataset, 58 are filtered from
Facebook US, 4 from Facebook India, and 6 from Renren.
All objectionable images are found on Sybil profiles; none
are found on legitimate profiles.
Finally, we show the basic statistics of ground-truth profiles
in Table 2. Legitimate users have more photo albums
and profile photos, while Sybils have more censored photos.
The “News-Feed” column shows the average number of
items in the first 5 chronological pages of each user’s newsfeed.
On Facebook, the news-feed includes many types of
items, including wall posts, status updates, photo tags, etc.
On Renren, the feed only includes wall posts from friends.
3.2 Experiment Design
Using the datasets in Table 1, our goal is to assess the
ability of humans to discriminate between Sybil and legitimate
user profiles. To test this, we perform a simple, controlled
study: we show a human test subject (or simply a
tester) a profile from our dataset, and ask them to classify
it as real or fake. The tester is allowed to view the profile’s
basic information, wall, photo albums, and individual photos
before making their judgment. If the tester classifies the
profile as fake, they are asked what profile elements (basic
information, wall, or photos) led them to this determination.
Each tester in our study is asked to evaluate several profiles
from our dataset, one at a time. Each tester is given
roughly equal number of Sybil profiles and legitimate profiles.
The profiles from each group are randomized for each
tester, and the order the profiles are shown in is also randomized.
Implementation. We implement our study as a website.
When a tester begins the study, they are presented with a
webpage that includes a consent form and details about our
study. After the tester agrees, they are directed to the first
profile for them to evaluate. Figure 2 shows a screenshot of
our evaluation page. At the top are links to the all of the
profiles the tester will evaluate. Testers may use these links
to go back and change their earlier answers if they wish.
Below the numbered links is a box where testers can
record their evaluation for the given profile: real or fake,
and if fake, what profile elements are suspicious (profile,
wall, and/or photos)? When the tester is done evaluating the
given profile, they click the “Save Changes” button, which
automatically directs their browser to the next profile, or the
end of the survey if all profiles have been evaluated.
Below the evaluation box are three buttons that allow the
tester to view the given profile’s basic information (shown
by default), wall, and photo albums. The basic information
and wall are presented as JPEG images, in order to preserve
the exact look of Facebook/Renren, while also preventing
the tester from clicking any (potentially malicious) embedded
links. Testers may click on each photo album to view
the individual photos contained within.Figure 2. Screenshot of the English version
of our user study website.
At the end of the survey, the tester is asked to answer a
short questionnaire of demographic information. Questions
include age, gender, country of residence, education level,
and years of OSN experience. There is also a free-form
comment box where tester can leave feedback.
On the server-side, we record all of the classifications
and questionnaire answers made by each tester. We also
collect additional information such as the time spent by the
tester on each page, and total session time per tester.
Because our datasets are in two different languages, we
construct two versions of our study website. Figure 2 shows
the English version of our site, which is used to evaluate
Facebook profiles. We also constructed a Chinese version
of our site to evaluate Renren profiles.
Limitations. The methodology of our user study has
two minor limitations. First, we give testers full profiles to
evaluate, including basic info, wall, and photos. It is not
clear how accurate testers would be if given different information,
or a restricted subset of this information. Second,
we assume that there are no malicious testers participating
in our user study. Although attackers might want to infiltrate
and disrupt a real crowdsourced Sybil detector, there
is little for them to gain by disrupting our study. Related
work on detecting crowdsourcing abuse may be helpful in
mitigating this problem in the future [7].
3.3 Test Subjects
In order to thoroughly investigate how accurate different
types of users are at detecting Sybils, we ran user studies
on three different groups of test subjects. Each individual
tester was asked to evaluate ≥10 profiles from our dataset,
and each profile was evaluated by multiple testers from each
group. This allows us to examine the overall detection accuracy
of the group (e.g. the crowd), versus the accuracy
of each individual tester. We now introduce the three test
groups, and explain how we administered our study to them.
Experts. The first group of test subjects are experts.
This group contains Computer Science professors and graduate
students that were carefully selected by us. The expert
group represents the practical upper-bound on achievable
Sybil detection accuracy.
The expert group is subdivided into three regional
groups: US, Indian, and Chinese experts. Each expert group
was evaluated on the corresponding regional dataset. We
approached experts in person, via email, or via social media
and directed them to our study website to take the test.
Table 1 lists the number of expert testers in each regional
group. Expert tests were conducted in February, 2012.
As shown in Table 1, each Chinese and Indian expert
evaluated 100 profiles from our dataset, while US experts
evaluated 50 profiles. This is significantly more profiles per
tester than we gave to any other test group. However, since
experts are dedicated professionals, we assume that their accuracy
will not be impacted by survey fatigue. We evaluate
this assumption in Section 5.
Turkers. The second group of test subjects are turkers
recruited from crowdsourcing websites. Unlike the expert
group, the background and education level of turkers cannot
be experimentally controlled. Thus, the detection accuracy
of the turker group provides a lower-bound on the efficacy
of a crowdsourced Sybil detection system.
Like the expert group, the turker group is subdivided into
three regional groups. US and Indian turkers were recruited
from MTurk. HITs on MTurk may have qualifications associated
with them. We used this feature to ensure that only
US based turkers took the Facebook US test, and Indian
turkers took the Facebook India test. We also required that
turkers have ≥90% approval rate for their HITs, to filter
out unreliable workers. We recruited Chinese turkers from
Zhubajie, the largest crowdsourcing site in China. Table 1
lists the number of turkers who completed our study in each
region. Turker tests were conducted in February, 2012.
Unlike the expert groups, turkers have an incentive to
sacrifice accuracy in favor of finishing tasks quickly. Because
turkers work for pay, the faster they complete HITs,
the more HITs they can do. Thus, of all our test groups, we
gave turkers the fewest number of profiles to evaluate, since
turkers are most likely to be effected by survey fatigue. As
shown in Table 1, Chinese turkers each evaluated 10 profiles,
while US and Indian turkers evaluated 12.
We priced each Zhubajie HIT at $0.15 ($0.015 per profile),
and each MTurk HIT at $0.10 ($0.0083 per profile).
These prices are in line with the prevailing rates on crowd-% of Testers
100
80
60
40
20
0
CN
Social
Turker
Expert
Turker
Expert
Turker
Expert
IN
US
Graduate
Bachelors
High
Primary
% of Testers
100
80
60
40
20
0
CN
(a) Education Level
Social
Turker
Expert
Turker
Expert
Turker
Expert
IN
US
5-10 Years
2-5 Years
0-2 Years
Never
% of Testers
100
80
60
40
20
0
CN
(b) OSN Usage Experience
Turker
Expert
Turker
Expert
IN
US
Social
Turker
Expert
Female
Male
Figure 3. Demographics of participants in our user study.
(c) Gender
sourcing websites [11]. Although we could have paid more,
prior work has shown that paying more money does not
yield higher quality results on crowdsourcing sites [19].
Sociology Undergraduates. The final group of test
subjects are undergraduate students from the Department of
Communications at UCSB (Social Science major). These
students were asked to take our study in exchange for course
credit. This group adds additional perspective to our study,
apart from Computer Science oriented experts and the uncontrolled
turker population.
The social science students are listed in Table 1 as “US
social.” We only asked the students to evaluate our Facebook
US dataset, since cultural and language barriers prevent
them from effectively evaluating Chinese and Indian
profiles. 198 total students completed our study in March,
2012. Each student was asked to evaluate 25 profiles, midway
between what we asked of experts and turkers.
Summary. We conduct experiments with 7 groups of
testers: experts from US, India, and China; turkers from
US, India, and China, and social science students from the
US. Table 1 lists the number of testers in each group and the
number of profiles evaluated by each tester.
4 User Study Results
In this section, we present the high level results of our
user study. We start by introducing the demographics of the
test subjects. Next, we address one of our core questions:
how accurate are people at identifying Sybils? We compare
the accuracy of individual testers to the accuracy of
the group to assess whether the “wisdom of the crowd” can
overcome individual classification errors. Finally, we examine
the reasons testers cited in classified profiles as Sybils.
4.1 Demographics
At the end of each survey, testers were asked to answer
demographic questions about themselves. Figure 3 shows
the results that were self-reported by testers.
Education. As shown in Figure 3(a), most of our experts
are enrolled in or have received graduate level degrees. This
is by design, since we only asked Computer Science graduate
students, undergrads enrolled in graduate courses, and
professors to take part in our expert experiments. Similarly,
the social science testers are drawn from the undergraduate
population at UCSB, which is reflected in the results.
The education levels reported by turkers are surprisingly
high. The majority of turkers in the US and China report
enrollment or receipt of bachelors-level degrees [24]. Surprisingly,
over 50% of Indian turkers report graduate level
educations. This result for Indian turkers stems from cultural
differences in how education levels are denoted. Unlike
in the US and China, in India “graduate school” refers
to “graduated from college,” not receipt of a post-graduate
degree (e.g. Masters or Doctorate). Thus, most “graduate”
level turkers in India are actually bachelors level.
OSN Usage Experience. As shown in Figure 3(b),
the vast majority of testers report extensive experience with
OSNs. US experts, Chinese experts, and social science undergrads
almost uniformly report ≥2 years of OSN experience.
Indian experts, Indian turkers, and Chinese turkers
have the greatest fractions of users with <2 years of OSN
experience. US turkers report levels of OSN experience
very similar to our most experienced expert groups.
Gender. As shown in Figure 3(c), the vast majority
of our testers are male. The only group which exhibits a
female majority is the social science undergrads, a demographic
bias of the communications major. Turker groups
show varying levels of gender bias: Chinese and Indian
turkers are predominantly male [24], while the US group
is evenly divided.
4.2 Individual Accuracy
We now address one of the core questions of the paper:
how accurate are people at identifying Sybils? To achieve
100% accuracy, a tester needs to correctly classify all Sybil
and legitimate profiles they were shown during the test. Figure
4 shows the accuracy of testers in 5 of our test groups.
Chinese experts are the most accurate, with half achieving
≥90% accuracy. The US and Indian (not shown) experts
also achieved high accuracy. However, the turker groups doCDF (%)
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CN Turker
US Turker
US Social
US Expert
CN Expert
0
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Accuracy per Tester (%)
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0 0.2 0.4 0.6 0.8 1
Average Jaccard Coefficient
CDF (%)
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Turkers
Social
0 0.2 0.4 0.6 0.8 1
Average Jaccard Coefficient
Figure 4. Tester accuracy.
(a) Renren
(b) Facebook US
Figure 5. Jaccard similarity coefficient of reasons.
CDF (%)
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Turker FP
20
Expert FN
0
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(a) Renren
CDF (%)
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Turker FP
20
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Error Rate per Tester (%)
(b) Facebook US
Figure 6. False positive (FP) and false negative (FN) rates for testers.
CDF (%)
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Expert FP
Turker FP
20
Expert FN
0
Turker FN
0 20 40 60 80 100
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(c) Facebook India
not perform as well as the experts. The Chinese and Indian
(not shown) turkers perform the worst, with half achieving
≤65% accuracy. The accuracy of US turkers and social science
students falls in-between the other groups.
To better understand tester accuracy, Figure 6 separates
the results into false positives and false negatives. A false
positive corresponds to misclassifying a legitimate profile
as a Sybil, while a false negative means failing to identify a
Sybil. Figure 6 focuses on our expert and turker test groups;
social science students perform similarly to US turkers, and
the results are omitted for brevity.
Figure 6 reveals similar trends across all test groups.
First, false positives are uniformly lower than false negatives,
i.e. testers are more likely to misclassify Sybils as legitimate,
than vice versa. Second, in absolute terms, the
false positive rates are quite low: <20% for 90% of testers.
Finally, as in Figure 4, error rates for turkers tend to be significantly
higher than those of experts.
In summary, our results reveal that people can identify
differences between Sybil and legitimate profiles, but most
individual testers are not accurate enough to be reliable.
4.3 Accuracy of the Crowd
We can leverage “the wisdom of the crowd” to amortize
away errors made by individuals. Many studies on
crowdsourcing have demonstrated that experimental error
can be controlled by having multiple turkers vote on the
answer, and then using the majority opinion as the final answer
[17,25]. As long as errors by turkers are uncorrelated,
this approach generates very accurate results.
We now examine whether this methodology can be used
to improve the classification accuracy of our results. This
question is of vital importance, since a voting scheme would
be an essential component of a crowdsourced Sybil detector.
To compute the “final” classification for each profile in our
dataset, we aggregate all the votes for that profile by testers
in each group. If ≥50% of the testers vote for fake, then we
classify that profile as a Sybil.
Table 3 shows the percentage of false positive and negative
classifications for each test group after we aggregate
votes. The results are mixed: on one hand, false positive
rates are uniformly low across all test groups. In the worst
case, US turkers and social science students only misclassify
1 out of 50 legitimate profiles. Practically, this means
that crowds can successfully identify real OSN profiles.
On the other hand, false negative rates vary widely across
test groups. Experts in China, in the US, and the social
science students all perform well, with false negative rates
<10%. Indian experts also outperform the turker groups,
but only by a 2.7% margin. The Chinese and Indian turker
groups perform worst, with ≥50% false negatives.
From these results, we can conclude three things. First,
using aggregate votes to classify Sybils does improve overall
accuracy significantly. Compared to the results for individual
testers in Figure 6, both false positive and negative
rates are much lower after aggregation. Second, the uniformly
low false positive rates are a very good result. This
means that running a crowdsourced Sybil detection system
will not harm legitimate social network users. Finally, even
with aggregate voting, turkers are still not as accurate as ex-Dataset Tester FP Rate FN Rate
Renren
CN Expert IN Expert 0% 0% 3%
16%
CN Turker IN Turker 0% 0% 63%
50%
US Expert 0% 9.4%
Facebook US US Turker 2% 18.7%
US Social 2% 6.25%
Facebook IN
IN Expert 0% 16%
IN Turker 0% 50%
Table 3. Error rates after aggregating votes.
Dataset Tester Info Wall Photos
Renren
CN Expert IN Expert 18% 39% 57% 28% 25%
33%
CN Turker IN Turker 31% 39% 31% 27% 38%
34%
US Expert 37% 30% 33%
Facebook US US Turker 35% 32% 33%
US Social 30% 31% 39%
Facebook IN
IN Expert 39% 28% 33%
IN Turker 39% 27% 34%
Table 4. Reasons why profiles are suspicious.
perts. In the next section, we look more deeply into factors
that may negatively influence turkers accuracy, and techniques
that can mitigate these issues.
4.4 Reasons for Suspicion
During our user study, testers were asked to give reasons
for why they classified profiles as Sybils. Testers were given
the option of reporting the profile’s basic information, wall,
and/or photos as suspicious. Testers could select as many
options as they liked.
In this section, we compare and contrast the reasons reported
by different test groups. Table 4 shows percentage
of votes for each reasons across our seven test groups. The
US and Indian expert and turker groups are very consistent:
they all slightly favor basic information. The bias may be
due to the way our study presented information, since each
profile’s basic information was shown first, by default. The
social science students are the only group that slightly favors
photos.
In contrast to the US and Indian groups, Chinese experts
and turkers often disagree on their reasons for suspicion.
The majority of experts rely on wall messages, while turkers
slightly favor photos. As shown in Figure 4, Chinese
turkers have lower accuracy than Chinese experts. One possible
reason for this result is that turkers did not pay enough
attention to the wall. As previously mentioned, there is
a comment box at the end of our survey for testers to offer
feedback and suggestions. Several Chinese experts left
comments saying they observed wall messages asking questions
like “do I know you?,” and “why did you send me a
friend request?,” which they relied on to identify Sybil profiles.
Consistency of Reasons. There is no way to objectively
evaluate the correctness of tester’s reasons for classification,
since there is no algorithm that can pick out suspicious
pieces of information from an OSN profile. Instead,
what we can do is examine how consistent the reasons are
for each profile across our test groups. If all the testers agree
on the reasons why a given profile is suspicious, then that is
a strong indication that those reasons are correct.
To calculate consistency, we use the following procedure.
In each test group, each Sybil is classified by N
testers. For all pairs of users in each group that classified
a particular Sybil profile, we calculate the Jaccard similarity
coefficient to look at overlap in their reasons, giving us
N ∗ (N − 1)/2 unique coefficients. We then compute the
average of these coefficients for each profile. By computing
the average Jaccard coefficient for each Sybil, we arrive at
a distribution of consistency scores for all Sybils for a given
test group.
Figure 5 shows the consistency distributions of the China
and US test groups. The results for the Indian test groups
are similar to US testers, and are omitted for brevity. The
Chinese turkers show the most disagreement: for 50% of
Sybils the average Jaccard coefficient is ≤0.4. Chinese experts
and all three US groups exhibit similar levels of agreement:
50% of Sybils have coefficients ≤0.5. The fraction of
Sybils receiving near complete disagreement (0.0) or agreement
(1.0) is negligibly small across all test groups.
Based on these results, we conclude that testers identify
Sybils for inconsistent reasons. Even though Table 4 shows
that each of the three available reasons receives a roughly
equal portion of votes overall, the reasons are assigned randomly
across Sybils in our dataset. This indicates that no
single profile feature is a consistent indicator of Sybil activity,
and that testers benefit from having a large, diverse set
of information when making classifications. Note that this
provides further support that automated mechanisms based
on individual features are less likely to succeed, and also
explains the success of human subjects in detecting Sybils.
Answer Revisions. While taking our survey, testers had
the option of going back and changing classifications that
they have previously made. However, few took advantage
of this feature. This is not unexpected, especially for turkers.
Since turkers earn more if they work faster, there is
a negative incentive to go back and revise work. In total,
there were only 28 revisions by testers: 16 from incorrect to
correct, and 12 from correct to incorrect.False Negative Rate (%)
100
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60
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20
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OSN Experience Education Gender
(a) US Turker
False Negative Rate (%)
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0
OSN Experience Education Gender
(b) India Turker
False Negative Rate (%)
100
80
60
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OSN Experience Education Gender
(c) Chinese Turker
Figure 7. False positive rates for turkers, broken down by demographic.
Never
0-2 Years
2-5 Years
5-10 Years
Primary School
High School
Bachelors
Graduate
Female
Male
Time Per Profile (S)
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Time
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(a) Renren Expert
(b) Renren Turker
(c) Facebook US Expert
Figure 8. Accuracy and time on the nth profile.
(d) Facebook US Turker
5 Turker Accuracy Analysis
The end goal of our work is to create a crowdsourced
Sybil detection system. However, in Section 4 we observed
that turkers are not as accurate as experts. In this section,
we examine factors that may impact the accuracy of turkers,
and investigate ways to improve our Sybil detection
system. We start by looking at demographic factors. Next,
we examine profile evaluation time to understand if turkers
are adversely affected by survey fatigue. Next, we examine
issues of turker selection. Will adding more turkers to the
crowd improve accuracy? What if we set a threshold and filter
out turkers that consistently perform poorly? Finally, we
calculate the per profile accuracy of testers to detect “stealth
Sybils” that are undetectable by both experts and turkers.
5.1 Demographic Factors
First, we explore the impact of demographic factors on
the turker’s accuracy. We focus on false negative rates of
turkers, since their false positive rates are close to zero. Figure
7 shows the average false negative rate and standard deviation
of turkers from China, US and India, broken down
by different demographics. Education has a clear impact on
false negatives: higher education level correlates with increased
ability to identify Sybils. The impact of OSN usage
experience is less clear. Chinese and US turker’s false negative
rates decline as OSN experience increases, which is
expected. However, for Indian turkers there is no correlation.
Gender does not appear to impact false negatives in a
meaningful way. The results in Figure 7 indicate that turker
accuracy could be improved by filtering out workers with
few years of OSN experience and low education level.
5.2 Temporal Factors and Survey Fatigue
It is known that turkers try to finish tasks as quickly as
possible in order to earn more money in a limited amount
of time [16]. This leads to our next question: do turkers
spend less time evaluating profiles than experts, and does
this lead to lower accuracy? The issue of time is also related
to survey fatigue: does the accuracy of each tester decrease
over time due to fatigue and boredom?
To understand these temporal factors, we plot Figure 8,
which shows the average evaluation time and accuracy per
profile “slot” for Chinese and US experts and turkers. The
x-axis of each subfigure denotes the logical order in which
testers evaluated profiles, e.g. “Profile Order” n is the n th
profile evaluated by each tester. Note that profiles are presented
to each tester in random order, so each tester evaluated
a different profile within each slot. Within each slot, we
calculate the average profile evaluation time and accuracy
across all testers. 100% accuracy corresponds to all testers
correctly classifying the nth profile they were shown. Although
experts evaluated >10 profiles each, we only show
the first 10 to present a fair comparison versus the turkers.
The results for the Indian test groups are similar to the US
groups, and are omitted for brevity.
The first important result from Figure 8 is that absolute
profile evaluation time is not a good indicator of accuracy.
The Chinese experts exhibit the fastest evaluation times, averaging
one profile every 23 seconds. However, they are
more accurate than Chinese turkers who spend more time
on each profile. This pattern is reversed on Facebook: experts
spend more time and are more accurate than turkers.
Next, we look for indications of survey fatigue. In all 4
subfigures of Figure 8, the evaluation time per profile decreases
over time. This shows that testers speed up as theyprogress through the survey. As shown in the expert Figures
8(a) and 8(c), this speedup does not affect accuracy.
These trends continue through the evaluation of additional
profiles (10-50 for Chinese experts, 10-100 for US experts)
that are not shown here. However, for turkers, accuracy
does tend to decrease over time, as shown in Figures 8(b)
and 8(d). This demonstrates that turkers are subject to survey
fatigue. The up-tick in Chinese turker accuracy around
profile 10 is a statistical anomaly, and is not significant.
5.3 Turker Selection
As demonstrated in Section 4.3, we can mitigate the classification
errors of individuals by aggregating their votes together.
This raises our next question: can we continue to
improve the overall accuracy of turkers by simply adding
more of them?
To evaluate this, we conducted simulations using the data
from our user study. Let C be the list of classifications received
by a given profile in our dataset (either a Sybil or
legitimate profile) by a given group of turkers (China, US,
or India). To conduct our simulation, we randomize the order
of C, then calculate what the overall false positive and
negative rates would be as we include progressively more
votes from the list. For each profile, we randomize the list
and conduct the simulation 100 times, then average the rates
for each number of votes. Intuitively, what this process reveals
is how the accuracy of the turker group changes as
we increase the number of votes, irrespective of the specific
order that the votes arrive in.
The results of our simulations demonstrate that there are
limits to how much accuracy can be improved by adding
more turkers to the group, as shown in Figure 9. Each line
plots the average accuracy over all Sybil and legitimate profiles
for a given group of turkers. For false positives, the
trend is very clear: after 4 votes, there are diminishing returns
on additional votes. For false negatives, the trend is
either flat (US turkers), or it grows slightly worse with more
votes (China and India).
Filtering Inaccurate Turkers. Since adding more turkers
does not significantly increase accuracy, we now investigate
the opposite approach: eliminating turkers that are
consistently inaccurate. Many deployed crowdsourcing systems
already use this approach [22]. Turkers are first asked
to complete a pre-screening test, and only those who perform
sufficiently well are allowed to work on the actual job.
In our scenario, turkers could be pre-screened by asking
them to classify accounts from our ground-truth datasets.
Only those that correctly classify x accounts, where x is
some configurable threshold, would be permitted to work
on actual jobs classifying suspicious accounts.
To gauge whether this approach improves Sybil detection
accuracy, we conduct another simulation. We vary the
accuracy threshold x, and at each level we select all turkers
that have overall accuracy ≥ x. We then plot the false negative
rate of the selected turkers in Figure 10. Intuitively,
this simulates turkers taking two surveys: one to pre-screen
them for high accuracy, and a second where they classify
unknown, suspicious accounts.
Figure 10 demonstrates that the false negative rate of the
turker group can be reduced to the same level as experts by
eliminating inaccurate turkers. The false negative rates are
stable until the threshold grows >40% because, as shown
in Figure 4, almost all the turkers have accuracy >40%. By
70% threshold, all three test groups have false negative rates
≤10%, which is on par with experts. We do not increase
the threshold beyond 70% because it leaves too few turkers
to cover all the Sybil profiles in our dataset. At the 70%
threshold, there are 156 Chinese, 137 Indian, and 223 US
turkers available for work.
5.4 Profile Difficulty
The last question we examine in this section is the following:
are there extremely difficult “stealth” Sybils that resist
classification by both turkers and experts? As we show
in Table 3, neither experts nor turkers have 0% false negatives
when classifying Sybils. What is unknown is if there
is correlation between the false negatives of the two groups.
To answer this question, we plot Figure 11. Each scatter
plot shows the average classification accuracy of the Sybils
from a particular region. The x-axes are presented in ascending
order by turker accuracy. This is why the points for
the turkers in each subfigure appear to form a line.
Figure 11 reveals that, in general, experts can correctly
classify the vast majority of Sybils that turkers cannot (e.g.
turker accuracy <50%). There are a select few, extremely
difficult Sybils that evade both the turkers and experts.
These “stealth” Sybils represent the pinnacle of difficulty,
and blur the line between real and fake user profiles. There
is only one case, shown in Figure 11(a), where turkers correctly
identify a Sybil that the experts missed.
One important takeaway from Figure 11 is that “stealth”
Sybils are a very rare phenomenon. Even if a crowdsourced
Sybil detector was unable to identify the them, the overall
detection accuracy is so high that most Sybils will be caught
and banned. This attrition will drive up costs for attackers,
deterring future Sybil attacks.
Turker Accuracy and Luck. Another takeaway from
Figure 11 is that some profiles are difficult for turkers to
classify. This leads to a new question: are the most accurate
turkers actually better workers, or were they just lucky
during the survey? Hypothetically, if a turker was randomly
shown all “easy” Sybils, then they would appear to be accurate,
when in fact they were just lucky.
Close examination of our survey results reveals that accurate
turkers were not lucky. The 75 Chinese turkers whoError Rate (%)
70
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0
FN (CN)
FN (IN)
FN (US)
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Number of Votes per Profile
Fale Negative Rate (%)
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0
CN
IN
US
0 10 20 30 40 50 60 70
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Figure 9. Votes per profile versus the FP and
FN rate.
Figure 10. Accuracy threshold versus the
false negatives.
Accuracy Per Sybil (%)
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Experts
10 20 30 40 50 60 70 80 90 100
Sybils Ordered by Turker Accuracy
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Accuracy Per Sybil (%)
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5 10 15 20 25 30
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(b) Facebook US
Figure 11. Scatter plots of average accuracy per Sybil profile.
Accuracy Per Sybil (%)
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Experts
5 10 15 20 25 30 35 40 45 50
Sybils Ordered by Turker Accuracy
(c) Facebook India
achieved ≥90% accuracy were collectively shown 97% of
Renren Sybils during the survey. Similarly, the 124 US
turkers with ≥90% accuracy were also shown 97% of the
Facebook US Sybils. Thus, the high accuracy turkers exhibit
almost complete coverage of the Sybils in our dataset,
not just the “easy” ones.
6 A Practical System
In this section, we design a crowdsourced Sybil detection
system based on the lessons learned from our experiments.
We focus on practical issues such as scalability, accuracy,
and privacy. We first describe our system architecture that
enables crowdsourced Sybil detection at large scale. Second,
we use trace-driven simulations to examine the tradeoff
between accuracy and cost in such a system. Finally, we
discuss how to preserve user privacy when distributing user
profile data to turkers.
6.1 System Design and Scalability
The first challenge we address is scalability. Today’s social
networks include hundreds of millions of users, most
of whom are legitimate. How do we build a system that
can focus the efforts of turkers on the subset of accounts
that are suspicious? To address this challenge, we propose
a hierarchical Sybil detection system that leverages both automated
techniques and crowdsourced human intelligence.
As shown in Figure 12, the system contains two main layers:
the filtering layer and the crowdsourcing layer.
Filtering Layer. In the first layer, we use an ensemble
of filters to locate suspicious profiles in the social network.
These filters can be automated using techniques from prior
work, such as Sybil community detection [3] and feature
based selection [31]. Filters can also be based on existing
“user report” systems that allow OSN users to “report” or
“flag” suspicious profiles. These tools are already implemented
in social networks such as Facebook and Renren,
and help to dramatically reduce the number of target profiles
studied by our crowdsourcing layer.
Crowdsourcing Layer. The output of the filtering layer
is a set of suspicious profiles that require further validation
(Figure 12). These profiles are taken as input by the crowdsourcing
layer, where a group of turkers classify them as
legitimate or fake. OSNs can take further action by either
banning fake accounts, or using additional CAPTCHAs to
limit potentially damaging behavior.
We begin with two techniques to increase the accuracy of
the turker group. First, we use majority voting by a group
of workers to classify each suspicious profile. Our earlier
results in Table 3 show that the accuracy of the crowd is
significantly better than the accuracy of individual turkers.
The second mechanism is a “turker selection” module
that filters out inaccurate turkers. Figure 10 shows that
eliminating inaccurate turkers drastically reduces the false
negative rate. As shown in Figure 12, we can implement
turker selection by randomly mixing in “ground-truth profiles”
that have been verified by social network exployees
with the larger set of suspicious profiles. By examining aAll Turkers
Crowdsourcing
Employees
Very Accurate
Turkers
Threshold 70% 80% 90%
L (Lower Layer, Accurate Turkers) 5 5 5
U (Upper Layer, Very Accurate Turkers) 3 3 2
Turker
Selection
Accurate
Turkers
Sybils
Table 5. Optimal # of votes per profile in each
layer in order to keep the false positives <1%.
Social
Networks
User Reports
Auto Filters
Filtering
?
Suspicious
Profiles
Figure 12. Crowdsourced Sybil detector.
tester’s answers on the ground-truth profiles, we can gauge
the evaluation accuracy of each worker. This accuracy test
is performed continuously over time, so that any significant
deviation in the quality of turker’s work will be detected.
This protects against malicious attackers who go “undercover”
as one of our testers, only to turn malicious and generate
bad results when presented with real test profiles.
6.2 System Simulations and Accuracy
In this section, we examine the tradeoff between accuracy
and cost in our system. The overall goal of the system
is to minimize false positives and negatives, while also minimizing
the number of votes needed per profile (since each
vote from a turker costs money). There is a clear tradeoff
between these two goals: as shown in Figure 9, more votes
reduces false positives.
Simulation Methodology. We use trace-driven simulations
to examine these tradeoffs. We simulate 2000 suspicious
profiles (1000 Sybil, 1000 legitimate) that need to be
evaluated. We vary the number of votes per profile, V , and
calculate the false positive and false negative rates of the
system. Thus, each profile is evaluated by V random turkers,
and each turker’s probability of being correct is based
on their results from our user study. In keeping with our system
design, all turkers with <60% accuracy are eliminated
before the simulation by our “turker selection” module.
We consider two different ways to organize turkers in
our simulations. The first is simple: all turkers are grouped
together. We refer to this as one-layer organization. We
also consider two-layer organization: turkers are divided
into two groups, based on an accuracy threshold T . Turkers
with accuracy >Tare placed in the upper layer, otherwise
they go into the lower layer.
In the two-layer scheme, profiles are first evaluated by
turkers in the lower layer. If there is strong consensus
among the lower layer that the profile is Sybil or legitimate,
then the classification stands. However, if the profile is controversial,
then it is sent to the more accurate, upper layer
turkers for reevaluation. Each profile receives B votes in
the lower layer and U votes in the upper layer. Intuitively,
the two-layer system tries to maximize the utility of the very
accurate turkers by only having them evaluate difficult profiles.
Figure 12 depicts the two-layer version of our system.
In our design, we cap the maximum acceptable false positive
rate at 1%. Our motivation is obvious: social network
providers will not deploy a security system that has a nonnegligible
negative impact on legitimate users. We conducted
simulations on all our turker groups, with consistent
results across groups. For brevity, we limit our discussion
here to results for the Chinese turkers. As shown in Figure
6, the Chinese turkers have the worst overall accuracy
of our turker groups. Thus, they represent the worst-case
scenario for our system. The US and Indian groups both
exhibit better performance in terms of cost and accuracy
during simulations of our system.
Votes per Profile. In the one-layer simulations, the only
variable is votes per profile V . Given our constraint on false
positives <1%, we use multiple simulations to compute the
minimum value of V . The simulations reveal that the minimum
number of votes for the Chinese profiles is 3; we use
this value in the remainder of our analysis.
Calculating the votes per profile in the two-layer case is
more complicated, but still tractable. The two-layer scenario
includes four variables: votes per profile (U upper
and L lower), the accuracy threshold between the layers
T , and the controversial range R in which profiles are forwarded
from the lower to upper layer. To calculate L and U
we use the same methodology as in Figure 9. Turkers are
divided into upper and lower layers for a given threshold
T ∈ [70%, 90%], then we incrementally increase the votes
per profile in each layer until the false positive rate is <1%.
The false positive rate of each layer is independent (i.e. the
number of votes in the lower layer does not impact votes
in the upper layer), which simplifies the calculations. The
controversial range only effects the false negative rate, and
is ignored from these calculations.
Table 5 shows the minimum number of votes per profile
needed in the upper and lower layers as T is varied. We use
these values in the remainder of our analysis.
Figure 13 shows the average votes per profile in our simulations.
Three of the lines represent two-layer simulations
with different R values. For example, R = [0.2, 0.9] means
that if between 20% and 90% of the turkers classify the profile
as a Sybil, then the profile is considered controversial.
Although we simulated many R ranges, only three repre-Avg. Votes Per Profile
10
8
6
4
2
[0.2, 0.9]
[0.2, 0.5]
0
70 75 80 85 90
Threshold (%)
[0.5, 0.7]
One-Layer
False Negative Rate (%)
100
80
60
40
20
[0.5, 0.7]
One-Layer
[0.2, 0.9]
[0.2, 0.5]
0
70 75 80 85 90
Threshold (%)
False Negative Rate (%)
35
30
25
20
15
10
5
0
1%
0.1%
0 1 2 3 4 5 6 7 8 9 10
Avg. Votes Per Profile
Figure 13. Threshold versus
average votes per profiles.
Figure 14. Threshold versus
false negative rate.
Figure 15. Tradeoff between
votes per profile and desired
accuracy.
sentative ranges are shown for clarity. The number of votes
for the one-layer scheme is also shown.
The results in Figure 13 show that the number of votes
needed in the various two-layer scenarios are relatively stable.
As R varies, the number of profiles that must be evaluated
by both layers changes. Thus, average votes per profile
fluctuates, although the average is always ≤ L + U from
Table 5. Overall, these fluctuations are minor, with average
votes only changing by ≈1.
False Negatives. Judging by the results in Figure 13,
the one-layer scheme appears best because it requires the
fewest votes per profile (and is thus less costly). However,
there is a significant tradeoff for lowering the cost of the
system: more false negatives.
Figure 14 shows the false negative rates for our simulations.
The results for the two-layer scheme are superior:
for certain values of R and thresholds ≥80%, two-layers
can achieve false negative rates <10%. The parameters that
yield the lowest false negatives (0.7%) and the fewest average
votes per profile (6) are R = [0.2, 0.5] and T =90%.
We use these parameters for the remainder of our analysis.
The results in Figures 13 and 14 capture the power of
our crowdsourced Sybil detection system. Using only an
average of 6 votes per profile, the system products results
with false positive and negative rates both below 1%.
Reducing False Positives. In some situations, a social
network may want to achieve a false positive rate significantly
lower than 1%. In order to evaluate how much
this change would affect costs, we re-ran all our simulations
with the target false positive rate set to <0.1%. Figure 15
plots the number of votes per profile versus false negatives
as the target false positive rate is varied. Each point in the
scatter is a different combination of R and T values. The
conclusion from Figure 15 is straightforward: to get <0.1%
false positives, you need two additional votes per turker.
This tradeoff is fairly reasonable: costs increase 33%, but
false positives reduce by an order of magnitude.
Parameterization.
Since our system parameters were
optimized using actual user test results, they may not be
ideal for every system or user population. The key takeaway
is that given a user population, the system can be calibrated
to provide high accuracy and scalability. We do not have
sufficient data to make conjectures about how often or when
systems require re-calibration, but it is likely that a deployed
system might periodically recalibrate parameters such as V
and T for continued accuracy.
6.3 The Costs of a Turker Workforce
Using the parameters derived in the previous section, we
can estimate how many turkers would be needed to deploy
our system. Using the parameters for Renren, each profile
requires 6 votes on average, and turkers can evaluate
one profile every 20 seconds (see Figure 8). Thus, a turker
working a standard 8-hour day (or several turkers working
an equivalent amount of time) can examine 1440 profiles.
Data from a real OSN indicates that the number of turkers
needed for our system is reasonable. According to [3],
Tuenti, a Spanish online social network, has a user-base of
11 million and averages 12,000 user reports per day. Our
system would require 50 full-time turkers to handle this
load. If we scale the population size and reports per day up
by a factor of 10, we can estimate the load for a large OSN
like Facebook. In this case, our system requires 500 turkers.
Our own experience showed that recruiting this many turkers
is not difficult (Table 1). In fact, following our crowdsourcing
experiments on this and other projects [30], we
received numerous messages from crowd requesting more
tasks to perform.
Finally, we estimate the monetary cost of our system.
Facebook pays turkers from oDesk $1 per hour to moderate
images [10]. If we assume the same cost per hour
per turker for our system, then the daily cost for deployment
on Tuenti (i.e. 12,000 reports per day) would only be
$400. This compares favorably with Tuenti’s existing practices:
Tuenti pays 14 full-time employees to moderate content
[3]. The estimated annual salary for Tuenti employeesare roughly e30,000 3 , which is about $20 per hour. So the
Tuenti’s moderation cost is $2240 per day, which is significantly
more than the estimated costs of our turker workforce.
6.4 Privacy
Protecting user privacy is a challenge for crowdsourced
Sybil detection. How do you let turkers evaluate user profiles
without violating the privacy of those users? This issue
does not impact our experiments, since all profiles are from
public accounts. However, in a real deployment, the system
needs to handle users with strict privacy settings.
One possible solution is to only show turkers the public
portions of users’ profiles. However, this approach is problematic
because Sybils could hinder the detection system by
setting their profiles to private. Setting the profile to private
may make it more difficult for Sybils to friend other users,
but it also cripples the discriminatory abilities of turkers.
A better solution to the privacy problem is to leverage the
OSNs existing “report” filter. Suppose Alice reports Bob’s
profile as malicious. The turker would be shown Bob’s profile
as it appears to Alice. Intuitively, this gives the turker
access to the same information that Alice used to make her
determination. If Alice and Bob are friends, then the turker
would also be able to access friend-only information. On
the other hand, if Alice and Bob are strangers, then the
turker would only have access to Bob’s public information.
This scheme prevents users from abusing the report system
to leak the information of random strangers.
7 Related Work
The success of crowdsourcing platforms on the web has
generated a great deal of interest from researchers. Several
studies have measured aspects of Amazon’s Mechanical
Turk, including worker demographics [12, 24] and task
pricing [5, 11, 19]. There are studies that explore the pros
and cons to use MTurk for user study [16].
Many studies address the problem of how to maximize
accuracy from inherently unreliable turkers. The most common
approach is to use majority voting [17, 25], although
this scheme is vulnerable to collusion attacks by malicious
turkers [26]. Another approach is to pre-screen turkers with
a questionnaire to filter out less reliable workers [22]. Finally,
[26] proposes using a tournament algorithm to determine
the correct answer for difficult tasks.
In this study, we propose using crowdsourcing to solve
a challenging OSN security problem. However, many studies
have demonstrated how crowdsourcing can be used by
attackers for malicious ends. Studies have observed malicious
HITs asking turkers to send social spam [30], per-
3 http://www.glassdoor.com/Salary/
Tuenti-Salaries-E245751.htm
form search engine optimization (SEO) [21], write fake reviews
[23], and even install malware on their systems [15].
8 Conclusion and Open Questions
Sybil accounts challenge the stability and security of today’s
online social networks. Despite significant efforts
from researchers and industry, malicious users are creating
increasingly realistic Sybil accounts that blend into the
legitimate user population. To address the problem today,
social networks rely on ad hoc solutions such as manual inspection
by employees.
Our user study takes the first step towards the development
of a scalable and accurate crowdsourced Sybil detection
system. Our results show that by using experts to calibrate
ground truth filters, we can eliminate low accuracy
turkers, and also separate the most accurate turkers from
the crowd. Simulations show that a hierarchical two-tiered
system can both be accurate and scalable in terms of total
costs.
Ground-truth. Our system evaluation is constrained by
the ground-truth Sybils used in our user study, i.e. it is possible
that there are additional Sybils that were not caught and
included in our data. Thus, our results are a lower bound
on detection accuracy. Sybils that can bypass Facebook or
Renren’s existing detection mechanisms could potentially
be caught by our system.
Deployment. Effective deployment of crowdsourced
Sybil detection mechanisms remains an open question. We
envision that the crowdsourcing system will be used to complement
existing techniques such as content-filtering and
statistical models. For example, output from accurate turkers
can teach automated tools which fields of the data can
most easily identify fake accounts. Social networks can further
lower the costs of running this system by utilizing their
own users as crowdworkers. The social network can replace
monetary payments with in-system virtual currency, e.g.
Facebook Credits, Zynga Cash, or Renren Beans. We are
currently discussing internal testing and deployment possibilities
with collaborators at Renren and LinkedIn.
Countermeasures. An effective solution must take into
account possible countermeasures by attackers. For example,
ground-truth profiles must be randomly mixed with test
profiles in order to detect malicious turkers that attempt to
poison the system by submitting intentionally inaccurate results.
The ground-truth profiles must be refreshed periodically
to avoid detection. In addition, it is possible for attackers
to infiltrate the system in order to learn how to improve
fake profiles to avoid detection. Dealing with these “undercover”
attackers remains an open question.References
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