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VOL. 9,

NO. 6,



Detecting Automation of Twitter Accounts:

Are You a Human, Bot, or Cyborg?
Zi Chu, Steven Gianvecchio, Haining Wang, Senior Member, IEEE, and
Sushil Jajodia, Senior Member, IEEE
AbstractTwitter is a new web application playing dual roles of online social networking and microblogging. Users communicate with
each other by publishing text-based posts. The popularity and open structure of Twitter have attracted a large number of automated
programs, known as bots, which appear to be a double-edged sword to Twitter. Legitimate bots generate a large amount of benign
tweets delivering news and updating feeds, while malicious bots spread spam or malicious contents. More interestingly, in the middle
between human and bot, there has emerged cyborg referred to either bot-assisted human or human-assisted bot. To assist human
users in identifying who they are interacting with, this paper focuses on the classification of human, bot, and cyborg accounts on
Twitter. We first conduct a set of large-scale measurements with a collection of over 500,000 accounts. We observe the difference
among human, bot, and cyborg in terms of tweeting behavior, tweet content, and account properties. Based on the measurement
results, we propose a classification system that includes the following four parts: 1) an entropy-based component, 2) a spam detection
component, 3) an account properties component, and 4) a decision maker. It uses the combination of features extracted from an
unknown user to determine the likelihood of being a human, bot, or cyborg. Our experimental evaluation demonstrates the efficacy of
the proposed classification system.
Index TermsAutomatic identification, bot, cyborg, Twitter, social networks



is a popular online social networking and

microblogging tool, which was released in 2006. Remarkable simplicity is its distinctive feature. Its community
interacts via publishing text-based posts, known as tweets.
The tweet size is limited to 140 characters. Hashtag, namely
words or phrases prefixed with a # symbol, can group tweets
by topic. For example, #Justin Bieber and #Womens World
Cup are the two trending hashtags on Twitter in 2011 [1].
Symbol @ followed by a username in a tweet enables the
direct delivery of the tweet to that user. Unlike most online
social networking sites (i.e., Facebook and MySpace),
Twitters user relationship is directed and consists of two
ends, friend and follower. In the case where the user A adds
B as a friend, A is a follower of B while B is a friend of A. In
Twitter terms, A follows B (namely, the following relationship is unidirectional from A to B). B can also add A as his
friend (namely, following back or returning the follow), but
is not required. When A and B follow each other, the
relationship becomes bidirectional. From the standpoint of
information flow, tweets flow from the source (author) to
subscribers (followers). More specifically, when a user posts

. Z. Chu is with Twitter, Inc., 1355 Market St., Suite 900, San Francisco,
CA 94103. E-mail:
. S. Gianvecchio is with the MITRE Corporation, 7515 Colshire Dr.,
McLean, VA 22102. E-mail:
. H. Wang is with the Department of Computer Science, College of William
and Mary, Williamsburg, VA 23185. E-mail:
. S. Jajodia is with the Center for Secure Information Systems, George
Mason University, Fairfax, VA 22030. E-mail:
Manuscript received 20 Dec. 2011; revised 06 June 2012; accepted 14 Aug.
2012; published online 22 Aug. 2012.
For information on obtaining reprints of this article, please send e-mail to:, and reference IEEECS Log Number TDSC-2011-12-0273.
Digital Object Identifier no. 10.1109/TDSC.2012.75.
1545-5971/12/$31.00 2012 IEEE

tweets, these tweets are displayed on both the authors

homepage and those of his followers.
As reported in August 2011, Twitter has attracted
200 million users and generated 8.3 million Tweets per hour
[2]. It ranks the 10th on the top 500 site list according to Alexa
in December 2011 [3]. In November 2009, Twitter emphasized its value as a news and information network by
changing the question above the tweet input dialog box from
What are you doing to Whats happening. To some
extent, Twitter has transformed from a personal microblogging site to an information publishing venue. Many
traditional industries have used Twitter as a new media
channel. We have witnessed successful Twitter applications
in business promotion [4], customer service [5], political
campaigning [6], and emergency communication [7], [8].
The growing user population and open nature of Twitter
have made itself an ideal target of exploitation from
automated programs, known as bots. Like existing bots in
other web applications (i.e., Internet chat [9], blogs [10] and
online games [11]), bots have been common on Twitter.
Twitter does not inspect strictly on automation. It only
requires the recognition of a CAPTCHA image during
registration. After gaining the login information, a bot can
perform most human tasks by calling Twitter APIs. More
interestingly, in the middle between humans and bots have
emerged cyborgs, which refer to either bot-assisted humans
or human-assisted bots. Cyborgs have become common on
Twitter. After a human registers an account, he may set
automated programs (i.e., RSS feed/blog widgets) to post
tweets during his absence. From time to time, he participates to tweet and interact with friends. Different from bots
which greatly use automation, cyborgs interweave characteristics of both manual and automated behavior.
Published by the IEEE Computer Society



Automation is a double-edged sword to Twitter. On one

hand, legitimate bots generate a large volume of benign
tweets, like news and blog updates. This complies with the
Twitters goal of becoming a news and information network. On the other hand, malicious bots have been greatly
exploited by spammers to spread spam. The definition of
spam in this paper is spreading malicious, phishing, or
unsolicited commercial content in tweets. These bots
randomly add users as their friends, expecting a few users
to follow back.1 In this way, spam tweets posted by bots
display on users homepages. Enticed by the appealing text
content, some users may click on links and get redirected to
spam or malicious sites.2 If human users are surrounded by
malicious bots and spam tweets, their twittering experience
deteriorates, and eventually the whole Twitter community
will be hurt. The objective of this paper is to characterize the
automation feature of Twitter accounts, and to classify them
into three categories, human, bot, and cyborg, accordingly.
This will help Twitter manage the community better and
help human users recognize who they are tweeting with.
In the paper, we first conduct a series of measurements
to characterize the differences among human, bot, and
cyborg in terms of tweeting behavior, tweet content, and
account properties. By crawling Twitter, we collect over
500,000 users and more than 40 million tweets posted by
them. Then, we perform a detailed data analysis, and find a
set of useful features to classify users into the three classes.
Based on the measurement results, we propose an automated classification system that consists of four major
the entropy component uses tweeting interval as a
measure of behavior complexity, and detects the
periodic and regular timing that is an indicator of
2. the spam detection component uses tweet content to
check whether text patterns contain spam or not3;
3. the account properties component employs useful
account properties, such as tweeting device makeup,
URL ration, to detect deviations from normal; and
4. the decision maker is based on Random Forest, and
it uses the combination of the features generated by
the above three components to categorize an
unknown user as human, bot, or cyborg.
We validate the efficacy of the classification system through
our test data set. We further apply the system to classify the
entire data set of over 500,000 users collected, and speculate
the current composition of Twitter user population based
on our classification results.
The remainder of this paper is organized as follows:
Section 2 covers related work on Twitter and online social
networks. Section 3 details our measurements on Twitter.
Section 4 describes our automatic classification system on
Twitter. Section 5 presents our experimental results on
classification of humans, bots, and cyborgs on Twitter.
Finally, Section 6 concludes the paper.

1. Some advanced bots target potential users by keyword search.

2. Due to the tweet size limit, it is very common to use link shortening
service on Twitter, which converts an original link to a short one (i.e., The link illegibility favors bots to allure users.
3. Spam is a good indicator of automation. Most spam messages are
generated by bots, and very few are manually posted by humans.

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NO. 6,



Twitter has been widely used since 2006, and there are some
related literature in twittering [12], [13], [14]. To better
understand microblogging usage and communities, Java
et al. [12] studied over 70,000 Twitter users and categorized
their posts into four main groups: daily chatter (e.g., going
out for dinner), conversations, sharing information or
URLs, and reporting news. Their work also studied 1) the
growth of Twitter, showing a linear growth rate; 2) its
network properties, showing the evidence that the network
is scale-free like other social networks [15]; and 3) the
geographical distribution of its users, showing that most
Twitter users are from the US, Europe, and Japan.
Krishnamurthy et al. [13] studied a group of over 100,000
Twitter users and classified their roles by follower-tofollowing ratios into three groups: 1) broadcasters, which
have a large number of followers; 2) acquaintances, which
have about the same number on either followers or
following; and 3) miscreants and evangelists (e.g., spammers), which follow a large number of other users but have
few followers. Wu et al. [16] studied the information
diffusion on Twitter, regarding the production, flow, and
consumption of information. Kwak et al. [17] conducted a
thorough quantitative study on Twitter by crawling the
entire Twittersphere. Their work analyzed the followerfollowing topology, and found nonpower-law follower
distribution and low reciprocity, which all mark a deviation
from known characteristics of human social networks. Kim
et al. [18] analyzed Twitter lists as a potential source for
discovering latent characters and interests of users. A
Twitter list consists of multiple users and their tweets.
Their research indicated that words extracted from each list
are representative of all the members in the list even if the
words are not used by the members. It is useful for
targeting users with specific interests.
In addition to network-related studies, several previous
works focus on sociotechnological aspects of Twitter [7], [8],
[19], [20], [21], such as its use in the workplace or during
major disaster events.
Twitter has attracted spammers to post spam content,
due to its popularity and openness. Fighting against spam
on Twitter has been investigated in recent works [14], [22],
[23]. Yardi et al. [14] detected spam on Twitter. According
to their observations, spammers send more messages than
legitimate users, and are more likely to follow other
spammers than legitimate users. Thus, a high follower-tofollowing ratio is a sign of spamming behavior. Grier et al.
[22] investigated spam on Twitter from the perspective of
spam and click-through behaviors, and evaluated the
effectiveness of using blacklists to prevent spam propagation. Their work found out that around 0.13 percent of spam
tweets generate a visit, orders of magnitude higher than
click-through rate of 0.003-0.006 percent reported for spam
e-mail. Exploiting the social trust among users, social spam
may achieve a much higher success rate than traditional
spam methods. Thomas et al. [23] studied the behaviors of
spammers on Twitter by analyzing the tweets originated
from suspended users in retrospect. They found that the
current marketplace for Twitter spam uses a diverse set of
spamming techniques, including a variety of strategies for
creating Twitter accounts, generating spam URLs, and
distributing spam.


Compared to previous measurement studies on Twitter,

our work covers a relatively large group of Twitter users
(more than 500,000) and differs in how we link the
measurements to automation, i.e., whether posts are from
humans, bots, or cyborgs. While some similar metrics are
used in our work, such as follower-to-following ratio, we
also introduce some metrics, including entropy of tweet
intervals, which are not employed in previous research. Our
work also detects spam content through Bayesian classification. However, our work focuses on determining the
automation degree of Twitter accounts, and uses spam as
one of the features in the classification.
Twitter is a social networking service, so our work is
also related to recent studies on social networks, such as
Flickr, LiveJournal, Facebook, MySpace, and YouTube [15],
[24], [25]. In [15], with over 11 million users of Flickr,
YouTube, LiveJournal, and Orkut, Mislove et al. analyzed
link structure and uncovered the evidence of power-law,
small-world, and scale-free properties. In [25], Cha et al.
examined the propagation of information through the
social network of Flickr. Their work shows that most
pictures are propagated through the social links (i.e., links
received from friends rather than through searches or
external links to Flickr content) and the propagation is very
slow at each hop. As a result of this slow propagation, a
pictures popularity is often localized in one network and
grows slowly over a period of months or even years. In
[24], Cha et al. analyzed video popularity life cycles,
content aliasing, and the amount of illegal content on
YouTube, a popular video sharing service. While YouTube
is designed to share large content, i.e., videos, Twitter is
designed to share small content, i.e., text messages. Unlike
other social networking services, like Facebook or YouTube, Twitter is a microcontent social network, with
messages being limited to 140 characters.
As Twitter is a text-based message system, it is natural to
compare it with other text-based message systems, such as
instant messaging or chat services. Twitter has similar
message length (140 characters) to instant messaging and
chat services. However, Twitter lacks presence (users
show up as online/offline for instant messaging services or
in specific rooms for chat) but offers 1) more access methods
(web, SMS, and various APIs) for reading or posting and
2) more persistent content. Similar to Twitter, instant
messaging and chat services also have problems with bots
and spam [9], [26]. To detect bots in online chat,
Gianvecchio et al. [9] analyzed humans and bots in Yahoo!
chat and developed a classification system to detect bots
using entropy-based and machine-learning-based classifiers, both of which are used in our classification system as
well. In addition, as Twitter is text-based, e-mail spam
filtering techniques are also relevant [27], [28], [29].
However, Twitter posts are much shorter than e-mails
and spaced out over longer periods of time than for instant
messages, e.g., hours rather than minutes or seconds.
Twitter also differs from most other network services in
that automation, e.g., message feeds, is a major feature of
legitimate Twitter usage, blurring the lines between bot and
human. Twitter users can be grouped into four categories:
humans, bots, bot-assisted humans, and human-assisted
bots. The latter two, bot-assisted humans and humanassisted bots, can be described as cyborgs, a mix between
bots and humans [30].



In this section, we first describe the data collection of over

500,000 Twitter users. Then, we detail our observation of
user behaviors and account properties, which are pivotal
to automatic classification.

3.1 Data Collection

Here, we present the methodology used to crawl the Twitter
network and collect detailed user information. Twitter has
released a set of API functions [31] that support user
information collection. Thanks to Twitters courtesy of
including our test account to its white list, we can make
API calls up to 20,000 per hour. This eases our data
collection. To diversify our data sampling, we employ two
methods to collect the data set covering more than 500,000
users. The first method is Depth-First Search (DFS)-based
crawling. The reason we choose DFS is that it is a fast and
uniformed algorithm for traversing a network. Besides, DFS
traversal implicitly includes the information about network
locality and clustering. Inspired by [32], [33], we randomly
select five users as seeds. For each reached user, we record
its follower list. Taking the following direction, the crawler
continues with the depth constraint set as three. We
customize our crawler with a core module of PHP cURL.
Ten crawler processes work simultaneously for each seed.
After a seed is finished, they move to the next. The crawl
duration lasts one month, and 429,423 users are logged.
Similar to the work in [13] and [14], we also use the
public timeline API to collect the information of active
users, increasing the diversity of the user pool. Twitter
constantly posts the 20 most recent tweets in the global
scope. The crawler calls the timeline API to collect the
authors of the tweets included in the timeline. Since the
Twitter timeline frequently updates, the crawler can
repeatedly call the timeline API. During the same time
window of the DFS crawl, this method contributes 82,984
users to the data set. We totally collect 512,407 users on
Twitter combining both methods.
3.2 Ground-Truth Creation
To develop an automatic classification system, we need a
ground-truth set that contains known samples of human,
bot, and cyborg. Among collected data, we randomly
choose different samples and classify them by manually
checking their user logs and homepages. The ground-truth
set includes 2,000 users per class of human, bot, and cyborg,
and thus in total three are 6,000 classified samples. In
summary, the data set contains 8,350,095 tweets posted by
the sampled users in their account lifetime,4 from which we
can extract useful features for classification, such as
tweeting behaviors and text patterns.
Our log-based classification follows the principle of the
Turing test [34]. The standard Turing tester communicates
with an unknown subject for 5 minutes, and decides
whether it is a human or machine. Classifying Twitter
users is actually more challenging than it appears to be. For
many users, their tweets are less likely to form a relatively
consistent context. For example, a series of successive
tweets may be hardly relevant. The first tweet is the user
status, like watching a football game with my buds.
4. 4,431,923 tweets in the training set, and 3,918,172 tweets in the test set.



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Fig. 1. Numbers of followers and friends.

The second tweet is an automatic update from his blog. The

third tweet is a news report RSS feed in the format of article
title followed by a shortened URL.
For every account, the following classification procedure
is executed. We thoroughly observe the log, and visit the
users homepage (!/username) if necessary. We carefully check tweet contents, visit URLs
included in tweets (if any), and decide if redirected
webpages are related with their original tweets and if they
contain spam or malicious contents. We also check other
properties, like tweeting devices, user profile, and the
numbers of followers and friends. Given a long sequence of
tweets (usually, we check 60 or more if needed), the user is
labeled as a human if we can obtain some evidence of
original, intelligent, specific and human-like contents. In
particular, a human user usually records what he is doing
or how he feels about something on Twitter, as he uses
Twitter as a microblogging tool to display himself and
interact with friends. For example, he may write a post like
I just saw Yankees lost again today. I think they have to
replace the starting pitcher for tomorrows game. The
content carries intelligence and originality. Specificity
means that the tweet content is expressed in relatively
unambiguous words with the presence of consciousness
[34]. For instance, in reply to a tweet like How you like
iPad? a specific response made by human may be I like its
large touch screen and embedded 3G network. On the
other hand, a generic reply could be I like it.
The criteria for identifying a bot are listed as follows. The
first is the lack of intelligent or original content. For
example, completely retweeting tweets of others or posting
adages indicates a lack of originality. The second is the
excessive automation of tweeting, like automatic updates of
blog entries or RSS feeds. The third is the abundant
presence of spam or malicious URLs (i.e., phishing or
malware) in tweets or the user profile. The fourth is
repeatedly posting duplicate tweets. The fifth is posting
links with unrelated tweets. For example, the topic of the
redirected webpage does not match the tweet description.
The last is the aggressive following behavior. In order to
gain attention from human users, bots do mass following
and unfollowing within a short period of time. Cyborgs are
either human-assisted bots or bot-assisted humans. The
criterion for classifying a cyborg is the evidence of both
human and bot participation. For example, a typical cyborg

account may contain very different types of tweets. A large

proportion of tweets carry contents of human-like intelligence and originality, while the rest are automatic updates
of RSS feeds. It represents a usage model, in which the
human uses his account from time to time while the Twitter
widget constantly runs on his desktop and posts RSS feeds
of his favorite news channel. Lastly, the uncertain category
is for non-English users and those without enough tweets to
classify. The samples that are difficult and uncertain to
classify fall into this category, and are discarded. Some
Twitter accounts are set as private for privacy protection,
and their webpages are only visible to their friends. We do
not include such type of users in the classification either,
because of their inaccessibility.

3.3 Data Analysis

As mentioned before, Twitter API functions support
detailed user information query, ranging from profile,
follower, and friend lists to posted tweets. In the above
crawl, for each user visited, we call API functions to collect
abundant information related with user classification. Most
information is returned in the format of XML or JSON. We
develop some toolkits to extract useful information from the
above well-organized data structures. Our measurement
results are presented in the question-answer format.
Q1. In terms of social relationship, do bots have more friends
than followers? A users tweets can only be delivered to those
who follow him. A common strategy shared by bots is
following a large number of users (either targeted with
purpose or randomly chosen), and expecting some of them
will follow back. Following back is considered as a form of
etiquette on Twitter. To increase follower number, some
users blindly follow back all the followers including
spammers, without carefully checking their profiles. Fig. 1
shows the scatter plots of the numbers of followers and
friends for the three categories. For better illustration, the
scale is chopped and a small amount of extraordinary
points are not included. Fig. 1 contains three different
groups of users: group I where the number of ones
followers is clearly greater than the number of its friends;
group II where the situation is reverse; and group III where
the nodes stick around the diagonal.
In the human category, as shown in Fig. 1a, the majority
of the nodes belong to group III, implying that the number
of their followers is close to that of their friends. This result
complies with [15], revealing that human relationships are



Fig. 3. CDF of tweet count.

Fig. 2. CDF of account reputation.

typically reciprocal in social networks. Meanwhile, there are

quite a few nodes belonging to group I with far more
followers than friends. They are usually accounts of
celebrities and famous organizations. They generate interesting media contents and attract numerous subscribers. For
example, the singer Justin Timberlake has 1,645,675
followers and 39 friends (the ratio is 42,197-to-1).
In the bot category, many nodes belong to group II, as
shown in Fig. 1b. Bots add many users as friends, but few
follow them back. Unsolicited tweets make bots unpopular
among the human world. However, for some bots, the
number of their followers is close to that of their friends.
This is due to the following reason. Twitter imposes a limit
on the ratio of followers over friends to suppress bots. Thus,
some more advanced bots unfollow their friends if they do
not follow back within a certain amount of time. Those bots
cunningly keep the ratio close to 1.
Besides, we have observed that, normal human users are
more likely to follow famous or reputable accounts. We
define and normalize
Account Reputation

follower no
follower no frined no

A celebrity usually has many followers and few friends

(such as Justin Timberlake), and his reputation is close to
1. In contrast, for a bot with few followers and many
friends, its reputation is close to 0. Fig. 2 shows the
cumulative distribution function (CDF) of account reputation for three categories. The human category has the
largest account reputation, closely followed by cyborg.
However, bots value is much lower. Around 60 percent of
bots have fewer followers than friends, causing account
reputation less than 0.5.
Inspired by account reputation, we define account taste
as average account reputation of all the friends of the
account. Intuitively, the user freely chooses whom to follow
(namely, friends), and this reflects his taste. If the account
follows spammers, its taste is bad. By doing this, it helps
spread spam to more users, making itself a supporter of
spammers. We have observed spammer clusters in our
data set where spam accounts tend to follow each other.
The CDF result of account taste is similar with Fig. 2, and is
not presented due to the space limit.
Q2. Does automation generate more tweets? To answer this
question, we measure the number of tweets posted in a

users lifetime.5 Fig. 3 shows the CDF of the tweet counts,

corresponding to the human, bot, and cyborg category. It is
clear that cyborg posts more tweets than human and bot. A
large proportion of cyborg accounts are registered by
commercial companies and websites as a new type of
media channel and customer service. Most tweets are
posted by automated tools (i.e., RSS feed widgets, Web 2.0
integrators), and the volume of such tweets is considerable.
Meanwhile, those accounts are usually maintained by
some employees who communicate with customers from
time to time. Thus, the high tweet count in the cyborg
category is attributed to the combination of both automatic
and human behaviors in a cyborg. It is surprising that bot
generates fewer tweets than human. We check the bot
accounts, and find out the following fact. In its active
period, bot tweets more frequently than human. However,
bots tend to take long-term hibernation. Some are either
suspended by Twitter due to extreme or aggressive
activities, while the others are in incubation and can be
activated to form bot legions.
Q3. Does automation generate higher tweeting frequency?
Extended from the previous question, here we examine
accounts tweeting frequency in active status. Fig. 4 plots the
interarrival timing distribution of three categories. Due to
space limit, each category contains 100 accounts. Tweets
posted by an account are sorted on timestamp, and the
timestamp of the first tweet is set as 0. The tweeting
interarrival sequence of each account is denoted as a vertical
strip in the figure, and each of its tweets is denoted as a tiny
segment in the strip. We observe the wide existence of
burstiness (namely, a block of intensive tweets) in bot,
whereas human exhibits less intensive interarrival distribution. Automated programs used by bot accounts can
constantly operate in the background and intensively post
tweets. Most human users tweet with large interarrivals
(such as hours, and even days for some inactive users), and
manual behavior cannot generate tweeting frequency as
high as bot. The statistics in Table 1 may present a better
insight into interarrival distributions of three categories. The
average and median of interarrivals of bot are the smallest
among three categories. This fact matches the frequent
appearance of bursts in Fig. 4b. In contrast, interarrivals of
5. It is the duration from the time when his account was created to the
time when our crawler visited it.



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Fig. 4. Interarrival timing distribution of accounts.

human are relatively sparser, and the large standard

deviation suggests the irregularity of human behavior.
Q4. Is tweeting behavior regular or complex? In our
measurement, we have observed that, many bots are
driven by timers to post tweets at fixed interarrivals, and
thus exhibit regular behavior. In contrast, human behavior
carries the inherent complexity [9], [11], [35]. We use
entropy rate to measure periodic or regular timing of
accounts posting behavior. More theoretical details of
entropy are presented in Section 4.1. For normalization, we
define relative entropy as the entropy rate of an account
over the maximum entropy rate in the ground-truth set.
Fig. 5 demonstrates the CDF of relative entropy of the three
categories. Entropy clearly separates bot from human.
High entropy indicates irregularity, a sign of manual
behavior, whereas low entropy indicates regularity, a sign
of automation.
Q5. How do users post tweets? manually or via auto piloted
tools? Twitter supports a variety of channels to post tweets.
The device name appears below a tweet prefixed by from.
Our whole data set includes 41,991,545 tweets posted by
Interarrival Distribution Statistics

Fig. 5. CDF of accounts relative entropy.

3,648 distinct devices. The devices can be roughly divided

into the following four categories:
Web, a user logs into Twitter and posts tweets via
the website.
2. Mobile devices, there are some programs exclusively
running on mobile devices to post tweets, like Txt
for text messages, Mobile web for web browsers on
handheld devices, TwitterBerry for BlackBerry, and
twidroid for Android mobile OS.
3. Registered third-party applications, many third
parties have developed their own applications using
Twitter APIs to tweet, and registered them with
Twitter. From the application standpoint, we can
further categorize this group into subgroups including website integrators (twitpic,, Facebook),
browser extensions (Tweetbar and Twitterfox for
Firefox), desktop clients (TweetDeck and Seesmic
Desktop), and RSS feeds/blog widgets (twitterfeed
and Twitter for Wordpress).
4. APIs, for those third-party applications not registered or certificated by Twitter, they appear as API
in Twitter.
Fig. 6 shows the makeup of the above tweeting device
categories. Among them, the website of Twitter is the
most widely used and generates nearly half of the tweets
(46.78 percent), followed by third-party devices (40.18 percent). Mobile devices and unregistered API tools contribute
6.81 and 6.23 percent, respectively. Table 2 lists the top 10

Fig. 6. Tweeting device makeup.



Top 10 Tweeting Devices

devices used by the human, bot, and cyborg categories, and

the whole data set.6
More than half of the human tweets are manually posted
via the Twitter website. The rest of top devices are mobile
applications (Tweetie, UberTwitter, Mobile web, Txt,
TwitterBerry) and desktop clients (TweetDeck, Echofon,
and Seesmic). In general, tweeting via such devices requires
human participation. In contrast, the top tools used by bots
are mainly auto piloted, and 42.39 percent of bot tweets are
generated via unregistered API-based tools. Bots can abuse
APIs to do almost everything they want on Twitter, like
targeting users with keywords, following users, unfollowing those who do not follow back, or posting prepared
tweets. Twitterfeed, RSS2Twitter, and Proxifeed are RSS
feed widgets that automatically pipeline information
(usually in the format of the page title followed by the
URL) to Twitter via RSS feeds. Twitter Tools and Twitme
for WordPress are popular WordPress plug-ins that integrate blog updates to Twitter. Assetize is an advertising
syndicator mainly targeting at Twitter, and twitRobot is a
bot tool that automatically follows other users and posts
tweets. All these tools only require minimum human
participation (like importing Twitter account information,
or setting RSS feeds and update frequency), and thus
indicate great automation.
Overall, humans tend to tweet manually and bots are
more likely to use auto piloted tools. Cyborgs employ the
typical human and bot tools. The cyborg group includes
many human users who access their Twitter accounts from
time to time. For most of the time when they are absent, they
leave their accounts to auto piloted tools for management.
Q6. Do bots include more external URLs than humans? In
our measurement, we find out that, most bots tend to
include URLs in tweets to redirect visitors to external
webpages. For example, spam bots are created to spread
unsolicited commercial information. Their topics are similar
to those in e-mail spam, including online marketing and
affiliate programs, working at home, selling fake luxury
brands or pharmaceutical products.7 However, the tweet
size is up to 140 characters, which is rather limited for
spammers to express enough text information to allure
users. Basically, a spam tweet contains an appealing title
followed by an external URL. Fig. 7 shows the external
6. The whole data set contains around 500,000 users, and the human, bot,
and cyborg categories equally contain 1,000 users in the training data set.
7. A new topic is attracting more followers on Twitter. It follows the style
of pyramid sales by asking newly joined users to follow existing users in the
spam network.

URL ratios (namely, the number of external URLs included

in tweets over the number of tweets posted by an account)
for the three categories, among which the URL ratio of bot is
highest. Some tweets by bots even have more than one
URL.8 The URL ratio of cyborg is very close to the bots
level. A large number of cyborgs integrate RSS feeds and
blog updates, which take the style of webpage titles
followed by page links. The URL ratio of human is much
lower, on average it is only 29 percent. When a human
tweets what is he doing or what is happening around him,
he mainly uses text and does not often link to web pages.
Q7. Are there any other temporal properties of Twitter users
helpful for differentiation among human, bot, and cyborg?
Many research works like [36] and [37] have shown the
weekly and diurnal access patterns of humans in the
Internet. Figs. 9a and 9b present the tweeting percentages
of the three different categories on daily and hourly bases,
respectively. The weekly behavior of Twitter users shows
clear differences among the three categories. While humans are more active during the regular workdays, from
Monday to Friday, and less active during the weekend,
Saturday and Sunday, bots have roughly the same activity
level every day of the week. Interestingly, cyborgs are the
most active ones on Monday and then slowly decrease
their tweeting activities during the week; on Saturday,
cyborgs reach their lowest active point but somehow
bounce back a bit on Sunday. Such a cyborg activity trend
is mainly caused by their message feeds and the high level
of news and blog activities at the start of a week. Similarly,
the hourly behavior of human is more active during the
daytime, which mostly overlaps with office hours. The bot
activity is nearly even except a little drop in the deep of
night. Some more advanced bots have the setting of only
tweet from a time point to another, which helps save API
calls [38]. Thus, they can tweet more in the daytime to
better draw the attention of humans.
Fig. 8 shows account registration dates grouped by
quarter. We have two observations from the figure. First, the
majority of accounts (80.0 percent of humans, 94.8 percent of
bots, and 71.1 percent of cyborgs) were registered in 2009. It
confirms the dramatic growth of Twitter in 2009. Second, we
do not find any bot or cyborg in our ground-truth data set
earlier than March 2007. However, human registration has
continued increasing since Twitter was founded in 2006.
Thus, old accounts are less likely to be bots.
8. Many such accounts belong to a type of bot that always appends a
spam link to tweets it retweets.



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NO. 6,


Fig. 7. External URL ratio in tweets.

Fig. 8. Account registration date (grouped by quarter).

Q8. Are users aware of privacy and identity protection on

Twitter? Twitter provides a protected option to protect
user privacy. If it is set as true, the users homepage is only
visible to his friends. However, the option is set as false by
default. In our data set of over 500,000 users, only
4.9 percent of them are protected users. Twitter also verifies
some accounts to authenticate users real identities. More
and more celebrities and famous organizations have
applied for verified accounts. For example, Bill Gates has
his verified Twitter account at
However, in our data set, only 1.8 percent of users have
verified accounts.

based on Random Forest algorithm analyzes the features

identified by the other three components and makes a
decision: human, cyborg, or bot.


This section describes our automated system for classification of Twitter users. The system classifies Twitter users
into three categories: human, bot, and cyborg. The system
consists of several components: the entropy component, the
spam detection component, the account properties component, and the decision maker. The high-level design of our
Twitter user classification system is shown in Fig. 10.
The entropy component uses corrected conditional
entropy to detect periodic or regular timing, which is a
sign of automation. The spam detection component uses a
variant of Bayesian classification to detect text patterns of
known spam on Twitter. The account properties component
uses account-related properties to catch bot deviation from
the normal human distribution. Lastly, the decision maker

Fig. 9. Tweets posted.

4.1 Entropy Component

The entropy component detects periodic or regular timing
of the messages posted by a Twitter user. On one hand, if
the entropy or corrected conditional entropy is low for the
intertweet delays, it indicates periodic or regular behavior,
a sign of automation. More specifically, some of the
messages are posted via automation, i.e., the user may be
a potential bot or cyborg. On the other hand, a high entropy
indicates irregularity, a sign of human participation.
4.1.1 Entropy Measures
The entropy rate is a measure of the complexity of a process
[39]. The behavior of bots is often less complex than that of
humans [40], [41], which can be measured by entropy rate.
A low entropy rate indicates a regular process, whereas a
high entropy rate indicates a random process. A medium
entropy rate indicates a complex process, i.e., a mix of order
and disorder [42].
The entropy rate is defined as either the average entropy
per random variable for an infinite sequence or as the
conditional entropy of an infinite sequence. Thus, as real
data sets are finite, the conditional entropy of finite
sequences is often used to estimate the entropy rate. To
estimate the entropy rate, we use the corrected conditional
entropy [43]. The corrected conditional entropy is defined
as follows:


Fig. 10. Classification system.

A random process X fXi g is defined as a sequence of

random variables. The entropy of such a sequence of
random variables is defined as
HX1 ; . . . ; Xm 


P xi log P xi ;


where P xi is the probability P Xi xi .

The conditional entropy of a random variable given a
previous sequence of random variables is
HXm j X1 ; . . . ; Xm1
HX1 ; . . . ; Xm  HX1 ; . . . ; Xm1 :

Then, based on the conditional entropy, the entropy rate

of a random process is defined as
HX lim HXm j X1 ; . . . ; Xm1 :

The corrected conditional entropy is computed as a

modification of (4). First, the joint probabilities, P X1
x1 ; . . . ; Xm xm are replaced with empirically derived
probabilities. The data are binned into Q bins, i.e., values
are converted to bin numbers from 1 to Q. The empirically
derived probabilities are then determined by the proportions
of bin number sequences in the data. The entropy estimate
and conditional entropy estimate, based on empirically
derived probabilities, are denoted as EN and CE, respectively. Second, a corrective term, percXm  ENX1 , is
added to adjust for the limited number of sequences for
increasing values of m [43]. The corrected conditional
entropy, denoted as CCE, is computed as


manually posted by humans. Thus, the presence of spam

patterns usually indicates automation. Since tweets are text,
determining if their content is spam can be reduced to a text
classification problem. The text classification problem is
formalized as f : T  C ! f0; 1g, where f is the classifier,
T ft1 ; t2 ; . . . ; tn g are the texts to be classified, and C
fc1 ; c2 ; . . . ; ck g are the classes [44]. A value of 1 for fti ; cj
indicates that text ti belongs to class cj , whereas a value of 0
indicates it does not belong to that class. Bayesian classifiers
are very effective in text classification, especially for e-mail
spam detection, so we employ Bayesian classification for
our machine learning text classification component.
During the creation of ground-truth set in Section 3.2,
when the human inspector encounters spam content in
tweets, or tweets contain URLs caught by the blacklists, their
content (URLs are excluded if any) is added to the spam
content set. In contrast, the nonspam content set contains
ham tweets posted by human. As a conservative measure,
the set does not contain content posted by bot or cyborg.
In Bayesian classification, deciding if a message belongs
to a class, e.g., spam, is done by computing the corresponding probability based on its content, e.g., P C spamjM,
where M is a message and C is a class. If the probability is
over a certain threshold, then the message is from that class.
The probability that a message M is spam, P spamjM, is
computed from Bayes theorem:
P MjspamP spam
P MjspamP spam

P MjspamP bot P Mjnot spamP not spam


P spamjM

The message M is represented as a feature vector

hf1 ; f2 ; . . . ; fn i, where each feature f is one or more words
in the message and each feature is assumed to be
conditionally independent
P fi jspam
P spamjM P spam

P fi jspam P not spam
P spam


P fi jnot spam :


CCEXm j X1 ; . . . ; Xm1
CEXm j X1 ; . . . ; Xm1 percXm  ENX1 ;

where percXm is the percentage of unique sequences of

length m and ENX1 is the entropy with m fixed at 1 or the
first-order entropy.
The estimate of the entropy rate is the minimum of the
corrected conditional entropy over different values of m.
The minimum of the corrected conditional entropy is
considered to be the best estimate of the entropy rate from
the limited number of sequences.

4.2 Spam Detection Component

The spam detection component examines the content of
tweets to detect spam. We have observed that most spam
tweets are generated by bots and only very few of them are

The calculation of P spamjM varies in different implementations of Bayesian classification. The implementation used for our machine learning component is CRM114
[45]. CRM114 is a powerful text classification system that
offers a variety of different classifiers. The default classifier
for CRM114 is Orthogonal Sparse Bigram (OSB), a variant of
Bayesian classification, which has been shown to perform
well for e-mail spam filtering. OSB differs from other
Bayesian classifiers in that it treats pairs of words as
features. OSB first chops the whole input into multiple basic
units with five consecutive words in each unit. Then, it
extracts four word pairs from each unit to construct
features, and derives their probabilities. Finally, OSB
applies Bayes theorem to compute the overall probability
that the text belongs to one class or another.



4.3 Account Properties Component

Besides intertweet delay and tweet content, some Twitter
account-related properties are very helpful for the user
classification. As shown in Section 3.3, obvious difference
exists between the human and bot categories. The first
property is the URL ratio. The ratio indicates how often a
user includes external URLs in its posted tweets. External
URLs appear very often in tweets posted by a bot. Our
measure shows, on average the ratio of bot is 97 percent,
while that of human is much lower at 29 percent. Thus, a
high ratio (e.g., close to 1) suggests a bot and a low ratio
implies a human. The second property is tweeting device
makeup. According to Table 2, about 70 percent tweets of
human are posted via web and mobile devices (referred as
manual devices), whereas about 87 percent tweets of bot are
posted via API and other auto-piloted programs (referred
as auto devices). The third property is the followers to
friends ratio.
The fourth property is link safety, i.e., to decide whether
external links in tweets are malicious/phishing URLs or
not. We run a batch script to check a URL in five blacklists:
Google Safe Browsing, PhishingTank, URIBL, SURBL, and
Spamhaus [46], [47], [48], [49], [50]. Google Safe Browsing
checks URLs against Googles constantly updated lists of
suspected phishing and malware pages. PhishingTank
focuses on phishing websites. The mechanisms of URIBL,
SURBL, and Spamhaus are similar. They contain those
suspicious websites that have appeared in spam e-mails,
primarily Unsolicited Bulk/Commercial E-mail (UBE/
UCE). If the URL appears in any of the blacklists, the
feature of link safety is set as false.
The fifth property is whether a Twitter account is
verified. No bot in our ground-truth data set is verified.
The account verification suggests a human. The sixth
property is the account registration date. According to
Fig. 8, 94.8 percent of bots were registered in 2009. The last
two properties are the hashtag ratio and mention ratio.
Hashtag ratio of an account is defined as the number of
hashtags included in the tweets over the number of tweets
posted by the account. Mention ratio is defined similarly.
The account properties component extracts these properties from the user log, and sends them to the decision
maker. It assists the entropy component and the spam
detection component to improve the classification accuracy.
4.4 Decision Maker
Our classification problem can be formulated as follows:
Given an unknown user U represented by the feature
vector, the decision maker determines the class C to which
U belongs to. Namely,
U <f1; f2; . . . ; fn> ! C fhuman; bot; cyborgg:
We select Random Forest [51] as the machine learning
algorithm, and implement the decision maker based on it.
Random Forest creates an ensemble classifier consisting
of a set of decision trees. The algorithm applies the random
feature selection in [51] and bagging idea in [52] to construct
a collective forest of decision trees with controlled
variation. The decision tree contains two types of nodes,
the leaf node labeled as a class, and the interior node

VOL. 9,

NO. 6,


associated with a feature. We denote the number of features

in the data set as M, and the number of features used to
make the decision at a node of the tree as m M. Each
decision tree is built top-down in a recursive manner. For
every node in the construction path, m features are
randomly selected to reach a decision at the node. The
node is then associated with the feature that is the most
informative. Entropy is used to calculate the information
gain contributed by each of the m features (namely, how
informative a feature is). In other words, the recursive
algorithm applies a greedy search by selecting the candidate feature that maximizes the heuristic splitting criterion.
We denote D as the data set of labeled samples, and C as
the class with k values, C fC1 ; C2 ; . . . ; Ck g. The information required to identify the class of a sample in D is
denoted as Info(D) = Entropy(P), where P , as the probability
distribution of C, is
jC1 j jC2 j
jCk j
jDj jDj
If we partition D based on the value of a feature F into
subsets fD1 ; D2 ; . . . ; Dn g,
InfoF ; D

jDi j
InfoDi :

After the value of feature F is obtained, the corresponding

gain in information due to F is denoted as
GainF ; D InfoD  InfoF ; D;

As Gain favors features that have a large number of

values, to compensate for this GainRatio is defined as
GainRatioF ; D

GainF ; D
SplitInfoF ; D


where SplitInfo(F,D) is the information due to the splitting

of D based on the value of attribute F . Thus,
jD1 j jD2 j
jDn j
: 11
SplitInfoA; D Entropy
jDj jDj
More details of decision tree learning can be found in
[53]. To classify an unknown sample, it is push downwards
in the tree, and assigned with the class of the leaf node
with which it ends up. Every decision tree determines a
classification decision on the sample. Random Forest
applies the majority voting of all the individual decisions
to reach the final decision.


In this section, we first evaluate the accuracy of our

classification system based on the ground-truth set. Then,
we apply the system to classify the entire data set of over
500,000 users collected. With the classification results, we
further speculate the current composition of Twitter user
population. Finally, we discuss the robustness of the
proposed classification system against possible evasions.

5.1 Methodology
As shown in Fig. 10, the components of the classification
system collaborate in the following way. The entropy


component calculates the entropy (and corrected conditional entropy) of intertweet delays of a Twitter user. The
entropy component only processes logs with more than
100 tweets.9 This limit helps reduce noise in detecting
automation. A lower entropy indicates periodic or regular
timing of tweeting behavior, a sign of automation, whereas
a higher entropy implies irregular behavior, a sign of
human participation. The spam detection component
determines if the tweet content is either spam or not, based
on the text patterns it has learned. The content feature value
is set to 1 for spam but 0 for nonspam. The account
properties component checks all the properties mentioned
in Section 4.3, and generates a real-number-type value for
each property. Given a Twitter user, the above three
components generate a set of features and input them into
the decision maker. For each class, namely human, bot, and
cyborg, the decision maker computes a classification score
for the user, and classifies it into the class with the highest
score. The training of the classification system and cross
validation of its accuracy are detailed as follows.

5.2 Classification System Training

The spam detection component of the classification system
requires training before being used. It is trained on spam
and nonspam data sets. The spam data set consists of spam
tweets and spam external URLs, which are detected during
the creation of the ground-truth set. Some advanced spam
bots intentionally inject nonspam tweets (usually in the
format of pure text without URLs, such as adages10) to
confuse human users. Thus, we do not include such vague
tweets without external URLs. The nonspam data set
consists of all human tweets and cyborg tweets without
external URLs. Most human tweets do not carry spam.
Cyborg tweets with links are hard to determine without
checking linked webpages. They can be either spam or
nonspam. Thus, we do not include this type of tweets in
either data set. Training the component with up-to-date
spam text patterns on Twitter helps improve the accuracy.
In addition, we create a list of spam words with high
frequency on Twitter to help the Bayesian classifier capture
spam content.
5.3 Cross Validation of Accuracy
We use Weka, a machine learning tool [54], to implement
the Random Forest-based classifier. We apply cross
validation with 10-folds to train and test the classifier
over the ground-truth set [55]. The data set is randomly
partitioned into 10 complementary subsets with equal
size. In each round, one out of 10 subsets is retained as
the test set to validate the classifier, while the remaining
nine subsets are used as the training set to train the
classifier. At the beginning of a round, the classifier is
reset and retrained. Thus, each round is an independent
9. The intertweet span could be wild on Twitter. An account may be
inactive for months, but suddenly tweets at an intensive frequency for a
short-term, and then enters hibernation again. It generates noise to the
entropy component. Thus, the entropy component does not process logs
with less than 100 tweets. Besides, in practice, it is nearly impossible to
determine automation based on a very limited number of tweets.
10. A typical content pattern is listed as follows: Tweet 1, A friend in
need is a friend in deed. Tweet 2, Danger is next neighbor to security. Tweet
3, Work home and make $3k per month. Check out how, http://


Confusion Matrix

classification procedure, and does not affect subsequent

ones. The individual results from 10 rounds are averaged
to generate the final estimation. The advantage of cross
validation is that, all samples in the data set are used for
both training and validation, while each sample is
validated exactly once. The confusion matrix listed in
Table 3 demonstrates the classification results.
The Actual rows in Table 3 denote the actual classes of
the users, and the Classified columns denote the classes
of the users as decided by the classification system. For
example, the cell in the junction of the Human row and
column means that 1,972 humans are classified (correctly)
as humans, whereas the cell of Human row and
Cyborg column indicates that 27 humans are classified
(incorrectly) as cyborgs. There is no misclassification
between human and bot.
We examine the logs of those users being classified by
mistake, and analyze each category as follows:

For the human category, 1.4 percent of human users

are classified as cyborg by mistake. One reason is
that the overall scores of some human users are
lowered by spam content penalty. The tweet size is
up to 140 characters. Some patterns and phrases are
used by both human and bot, such as I post my
online marketing experience at my blog at http:// Please ReTweet it. Another reason is
that the tweeting interval distribution of some
human users is slightly lower than the entropy
means, and they are penalized for that.
For the bot category, 2.3 percent of bots is wrongly
categorized as cyborg. The main reason is that, most
of them escape the spam penalty from the spam
detection component. Some spam tweets have very
obscure text content, like you should check it out
since its really awesome.
Without checking the spam link, the component
cannot determine if the tweet is spam merely based
on the text.
For the cyborg category, 3.3 percent of cyborgs are
misclassified as human, and 5.1 percent of them are
misclassified as bot. In analyzing those samples
misclassified as human, we find out a common fact
that, some owners of cyborg accounts interact with
followers from time to time, and use manual devices
to reply or retweet to followers. Besides, the manual
behavior of owners increases the entropy of tweeting
interarrivals. The two factors tend to influence the
classifier to make decisions in favor of human. The
difficulty here is that, a cyborg can be either a
human-assisted bot or a bot-assisted human. A strict
policy could categorize cyborg as bot, while a loose
one may categorize it as human.



Feature Weights

There is negligible misclassification between human and bot. The classifier clearly separates these
two classes.
Overall, our classification system can accurately differentiate human from bot. However, it is much more challenging for a classification system to distinguish cyborg from
human or bot. After averaging the true positive rates of the
three classes with equal sample size, the overall system
accuracy can be viewed as 96.0 percent.
Among the set of features used in classification, some
play a more important role than others. Now, we evaluate
the discrimination weight of each feature. In every test,
we only use one feature to independently cross validate
the ground-truth set. Table 4 presents the results sorted
on accuracy. The entropy feature has the highest accuracy
at 82.8 percent. It effectively captures the timing difference
between regularity of automated behavior and complexity
of manual behavior. Limited by tweet size, bot usually
relies on URLs to redirect users to external websites. This
fact makes the URL ratio feature have a relatively high
accuracy at 74.9 percent. Recognizing the tweeting device
makeup (manual or automated) and detecting spam
content also help the classification. By comparing the
collective performance in Table 3 and individual performance in Table 4, we observe that, no single feature
works perfectly well, and the combination of multiple
features improves the classification accuracy.

5.4 Twitter Composition

We further use the classification system to automatically
classify our whole data set of over 500,000 users. We can
speculate the current composition of Twitter user population based on the classification results. The system classifies
53.2 percent of the users as human, 36.2 percent as cyborg,
and 10.5 percent as bot. Thus, we speculate the population
proportion of human, cyborg and bot category roughly as
5:4:1 on Twitter.
5.5 Resistance to Evasion
Now, we discuss the resistance of the classification system
to possible evasion attempts made by bots. Bots may
deceive certain features, such as the followers to friends
ratio as mentioned before. However, our system has two
critical features that are very hard for bots to evade. The
first feature is tweeting device makeup, which corresponds to the manual/auto device percentage in Table 4.

VOL. 9,

NO. 6,


Manual device refers to web and mobile devices, while

auto device refers to API and other auto-piloted programs
(see Section 3.3, Q5). Tweeting via web requires a user to
log in and manually post via the Twitter website in a
browser. Posting via HTTP form is considered by Twitter
as API. Furthermore, currently it is impractical or
expensive to run a bot on a mobile device to frequently
tweet. As long as Twitter can correctly identify different
tweeting platforms, device makeup is an effective metric
for bot detection. The second feature is URL ratio.
Considering the limited tweet length that is up to 140
characters, most bots have to include a URL to redirect
users to external sites. Thus, a high URL ratio is another
effective metric for bot detection. If we exclude the
features of URL ratio and tweeting device makeup, and
retrain the classifier, the overall classification accuracy
drops to 88.9 percent. Bot may try to bypass some features
when it knows our detection strategy. For timing entropy,
bot could mimic human behaviors but at the cost of much
reduced tweeting frequency. For spam content, bot could
intermix spam with ham tweets to dilute spam density.
We will continue to explore new features emerging with
the Twitter development for more effective bot detection
in the future.


In this paper, we have studied the problem of automation by

bots and cyborgs on Twitter. As a popular web application,
Twitter has become a unique platform for information
sharing with a large user base. However, its popularity and
very open nature have made Twitter a very tempting target
for exploitation by automated programs, i.e., bots. The
problem of bots on Twitter is further complicated by the key
role that automation plays in everyday Twitter usage.
To better understand the role of automation on Twitter,
we have measured and characterized the behaviors of
humans, bots, and cyborgs on Twitter. By crawling Twitter,
we have collected one month of data with over 500,000
Twitter users with more than 40 million tweets. Based on
the data, we have identified features that can differentiate
humans, bots, and cyborgs on Twitter. Using entropy
measures, we have determined that humans have complex
timing behavior, i.e., high entropy, whereas bots and
cyborgs are often given away by their regular or periodic
timing, i.e., low entropy. In examining the text of tweets, we
have observed that a high proportion of bot tweets contain
spam content. Lastly, we have discovered that certain
account properties, like external URL ratio and tweeting
device makeup, are very helpful on detecting automation.
Based on our measurements and characterization, we
have designed an automated classification system that
consists of four main parts: the entropy component, the
spam detection component, the account properties component, and the decision maker. The entropy component
checks for periodic or regular tweet timing patterns; the
spam detection component checks for spam content; and
the account properties component checks for abnormal
values of Twitter-account-related properties. The decision
maker summarizes the identified features and decides
whether the user is a human, bot, or cyborg. The


effectiveness of the classification system is evaluated

through the test data set. Moreover, we have applied the
system to classify the entire data set of over 500,000 users
collected, and speculated the current composition of Twitter
user population based on the classification results.

The authors are grateful to the anonymous referees for their
insightful feedback. This work was partially supported by
ARO grant W911NF-11-1-0149 and US National Science
Foundation (NSF) grant 0901537. The work of Sushil Jajodia
was partially supported by the Air Force Office of Scientific
Research under grant FA9550-09-1-0421 and by the Army
Research Office under MURI grant W911NF-09-1-0525 and
DURIP grant W911NF-11-1-0340.






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Zi Chu received the PhD degree in computer
science from the College of William and Mary,
Virginia, in 2012. He is a software engineer with
Twitter, Inc., San Francisco, California. His
research interests include web technologies,
machine learning, data mining, Internet anomaly
detection, and security privacy.

Steven Gianvecchio received the PhD degree

in computer science from the College of William
and Mary in 2010. He is a senior scientist and
principal Investigator at the MITRE Corporation
in McLean, Virginia. His research interests
include networks, distributed systems, network
monitoring, intrusion detection, traffic modeling,
and covert channels.

VOL. 9,

NO. 6,


Haining Wang received the PhD degree in

computer science and engineering from the
University of Michigan at Ann Arbor in 2003.
He is an associate professor of computer
science at the College of William and Mary,
Williamsburg, Virginia. His research interests lie
in the area of security, networking system, and
distributed computing. He is a senior member of
the IEEE.

Sushil Jajodia received the PhD degree from

the University of Oregon, Eugene. He is a
university professor, BDM International professor, and the director of Center for Secure
Information Systems in the Volgenau School of
Engineering at the George Mason University,
Fairfax, Virginia. The scope of his current
research interests encompasses information
secrecy, privacy, integrity, and availability. He
has authored or coauthored six books, edited 38
books, and conference proceedings, and published more than 400
technical papers in the refereed journals and conference proceedings.
He is also a holder of 10 patents and has several patent applications
pending. He has supervised 26 doctoral dissertations. Nine of these
graduates hold tenured positions at the US universities; four are US
National Science Foundation (NSF) CAREER awardees and one is US
Department of Energy (DoE) Young Investigator awardee. Two
additional students are tenured at foreign universities. He received the
1996 IFIP TC 11 Kristian Beckman award, 2000 Volgenau School of
Engineering Outstanding Research Faculty Award, 2008 ACM SIGSAC
Outstanding Contributions Award, and 2011 IFIP WG 11.3 Outstanding
Research Contributions Award. He was recognized for the most
accepted papers at the 30th anniversary of the IEEE Symposium on
Security and Privacy. His h-index is 71 and Erdos number is 2. He is a
senior member of the IEEE. The URL for his webpage is http://

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