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Detecting and Tracking Political Abuse in Social Media
J. Ratkiewicz, M. D. Conover, M. Meiss, B. Gonc¸alves, A. Flammini, F. Menczer
Center for Complex Networks and Systems Research
School of Informatics and Computing
Indiana University, Bloomington, IN, USA
Abstract
We study astroturf political campaigns on microblogging
platforms: politically-motivated individuals and organiza-
tions that use multiple centrally-controlled accounts to create
the appearance of widespread support for a candidate or opin-
ion. We describe a machine learning framework that com-
bines topological, content-based and crowdsourced features
of information diffusion networks on Twitter to detect the
early stages of viral spreading of political misinformation. We
present promising preliminary results with better than 96%
accuracy in the detection of astroturf content in the run-up to
the 2010 U.S. midterm elections.
1 Introduction
Social networking and microblogging services reach hun-
dreds of millions of users and have become fertile ground
for a variety of research efforts. They offer a unique op-
portunity to study patterns of social interaction among far
larger populations than ever before. In particular, Twitter has
recently generated much attention in the research commu-
nity due to its peculiar features, open policy on data shar-
ing, and enormous popularity. The popularity of Twitter,
and of social media in general, is further enhanced by the
fact that traditional media pay close attention to the ebb
and flow of the communication that they support. With this
scrutiny comes the potential for the hosted discussions to
reach a far larger audience than simply the original social
media users. Along with the recent growth of social media
popularity, we are witnessing an increased usage of these
platforms to discuss issues of public interest, as they offer
unprecedented opportunities for increased participation and
information awareness among the Internet-connected pub-
lic (Adamic and Glance 2005). While some of the discus-
sions taking place on social media may seem banal and su-
perficial, the attention is not without merit. Social media of-
ten enjoy substantial user bases with participants drawn from
diverse geographic, social, and political backgrounds (Java
et al. 2007). Moreover, the user-as-information-producer
model provides researchers and news organizations alike
with a means of instrumenting and observing a represen-
tative sample of the population in real time. Indeed, it has
Copyright c
2011, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
been recently demonstrated that useful information can be
mined from Twitter data streams(Asur and Huberman 2010;
Tumasjan et al. 2010; Bollen, Mao, and Zeng 2011).
With this increasing popularity, however, comes a dark
side — as social media grows in prominence, it is natural
that people find ways to abuse it. As a result, we observe
various types of illegitimate use; spam is a common exam-
ple (Grier et al. 2010; Wang 2010). Here we focus on a par-
ticular social media platform, Twitter, and on one particular
type of abuse, namely political astroturf — political cam-
paigns disguised as spontaneous “grassroots” behavior that
are in reality carried out by a single person or organization.
This is related to spam but with a more specific domain con-
text, and potentially larger consequences.
Online social media tools play a crucial role in the suc-
cesses and failures of numerous political campaigns and
causes. Examples range from the grassroots organizing
power of Barack Obama’s 2008 presidential campaign, to
Howard Dean’s failed 2004 presidential bid and the first-
ever Tea Party rally (Rasmussen and Schoen 2010; Wiese
and Gronbeck 2005).
The same structural and systemic properties that enable
social media such as Twitter to boost grassroots political
organization can also be leveraged, even inadvertently, to
spread less constructive information. For example, during
the political campaign for the 2010 midterm election, several
major news organizations picked up on the messaging frame
of a viral tweet relating to the allocation of stimulus funds,
succinctly describing a study of decision making in drug-
addicted macaques as “Stimulus $ for coke monkeys” (The
Fox Nation 2010).
While the “coke monkeys” meme developed organically
from the attention dynamics of thousands of users, it illus-
trates the powerful and potentially detrimental role that so-
cial media can play in shaping public discourse. As we will
demonstrate, a motivated attacker can easily orchestrate a
distributed effort to mimic or initiate this kind of organic
spreading behavior, and with the right choice of inflamma-
tory wording, influence a public well beyond the confines of
his or her own social network.
Unlike traditional news sources, social media provide lit-
tle in the way of individual accountability or fact-checking
mechanisms. Catchiness and repeatability, rather than truth-
fulness, can function as the primary drivers of information
diffusion. While flame wars and hyperbole are hardly new
phenomena online, Twitter’s 140-character sound bytes are
ready-made headline fodder for the 24-hour news cycle.
In the remainder of this paper we describe a system to an-
alyze the diffusion of information in social media, and, in
particular, to automatically identify and track orchestrated,
deceptive efforts to mimic the organic spread of information
through the Twitter network. The main contributions of this
paper are very encouraging preliminary results on the detec-
tion of suspicious memes via supervised learning (96% ac-
curacy) based on features extracted from the topology of the
diffusion networks, sentiment analysis, and crowdsourced
annotations. Because what distinguishes astoturf from true
political dialogue includes the way they are spread, our ap-
proach explicitly takes into account the diffusion patterns of
messages across the social network.
2 Background and Related Work
2.1 Information Diffusion
The study of opinion dynamics and information diffusion
in social networks has a long tradition in the social, physi-
cal, and computational sciences (Castellano, Fortunato, and
Loreto 2009; Barrat, Barthelemy, and Vespignani 2008;
Leskovec, Adamic, and Huberman 2006; Leskovec, Back-
strom, and Kleinberg 2009). Twitter has recently been con-
sidered as case study for information diffusion. For example,
Galuba et al. (2010) take into account user behavior, user-
user influence, and resource virulence to predict the spread
of URLs through the social network. While usually referred
to as ‘viral,’ the way in which information or rumors diffuse
in a network has important differences with respect to in-
fectious diseases (Morris 2000). Rumors gradually acquire
more credibility as more and more network neighbors ac-
quire them. After some time, a threshold is crossed and the
rumor is believed to be true within a community.
A serious obstacle in the modeling of information prop-
agation in the real world as well as in the blogosphere
is the fact that the structure of the underlying social net-
work is often unknown. When explicit information on the
social network is available (e.g. the Twitter’s follower re-
lations) the strength of the social links are hardly known
and their importance cannot be deemed uniform across
the network (Huberman, Romero, and Wu 2008). Heuris-
tic methods are being developed to face this issue. Gomez-
Rodriguez, Leskovec, and Krause (2010) propose an algo-
rithm that can efficiently approximate linkage information
based on the times at which specific URLs appear in a net-
work of news sites. For the purposes of our study such prob-
lem can be, at least partially, ignored. Twitter provides an
explicit way to follow the diffusion of information via the
tracking of retweets. This metadata tells us which links in the
social network have actually played a role in the diffusion of
information. Retweets have already been considered, e.g., to
highlight the conversational aspects of online social inter-
action (Honeycutt and Herring 2008). and because it is not
published or accessible yet The reliability of retweeted in-
formation has also been investigated. Mendoza, Poblete, and
Castillo (2010) found that false information is more likely
to be questioned by users than reliable accounts of an event.
Their work is distinct from our own in that it does not inves-
tigate the dynamics of misinformation propagation.
2.2 Mining Microblog Data
Several studies have demonstrated that information shared
on Twitter has some intrinsic value, facilitating, e.g., predic-
tions of box office success (Asur and Huberman 2010) and
the results of political elections (Tumasjan et al. 2010). Con-
tent has been further analyzed to study consumer reactions to
specific brands (Jansen et al. 2009), the use of tags to alter
content (Huang, Thornton, and Efthimiadis 2010), its rela-
tion to headline news (Kwak et al. 2010), and the factors that
influence the probability of a meme to be retweeted (Suh et
al. 2010). Romero et al. (2010) have focused on how passive
and active users influence the spreading paths.
Recent work has leveraged the collective behavior of
Twitter users to gain insight into a number of diverse phe-
nomena. Analysis of tweet content has shown that some
correlation exists between the global mood of its users and
important worldwide events, including stock market fluc-
tuations (Bollen, Mao, and Pepe 2010; Bollen, Mao, and
Zeng 2011). Similar techniques have been applied to in-
fer relationships between media events such as presiden-
tial debates and affective responses among social media
users (Diakopoulos and Shamma 2010). Sankaranarayanan
et al. (2009) developed an automated breaking news de-
tection system based on the linking behavior of Twitter
users, while Heer and boyd (2005) describe a system for
visualizing and exploring the relationships between users
in large-scale social media systems. Driven by practical
concerns, others have successfully approximated the epi-
center of earthquakes in Japan by treating Twitter users
as a geographically-distributed sensor network (Sakaki,
Okazaki, and Matsuo 2010).
2.3 Political Astroturf and Truthiness
In the remainder of this paper we describe the analysis of
data obtained by a system designed to detect astroturfing
campaigns on Twitter (Ratkiewicz et al. 2011). An illus-
trative example of such campaign has been recently docu-
mented by Mustafaraj and Metaxas (2010). They described
a concerted, deceitful attempt to cause a specific URL to
rise to prominence on Twitter through the use of a network
of nine fake user accounts. These accounts produced 929
tweets over the course of 138 minutes, all of which included
a link to a website smearing one of the candidates in the
2009 Massachusetts special election. The tweets injecting
this meme mentioned users who had previously expressed
interest in the election. The initiators sought not just to ex-
pose a finite audience to a specific URL, but to trigger an in-
formation cascade that would lend a sense of credibility and
grassroots enthusiasm to a specific political message. Within
hours, a substantial portion of the targeted users retweeted
the link, resulting in a rapid spread detected by Google’s
real-time search engine. This caused the URL in question
to be promoted to the top of the Google results page for a
query on the candidate’s name — a so-called Twitter bomb.
This case study demonstrates the ease with which a focused
Bob
bp.com
#oilspill
Event 1: Bob tweets with memes #oilspill and bp.com
(Analysis may infer dashed edges)
Bob
bp.com
#oilspill
Event 2: Alice re-tweets Bob's message
Alice
EDGE
Weight
Source, Target
EVENT
Timestamp
Figure 1: Model of streaming social media events.
effort can initiate the viral spread of information on Twitter,
and the serious consequences of such abuse.
Mass creation of accounts, impersonation of users, and
the posting of deceptive content are behaviors that are likely
common to both spam and political astroturfing. However,
political astroturf is not exactly the same as spam. While the
primary objective of a spammer is often to persuade users
to click a link, someone interested in promoting an astroturf
message wants to establish a false sense of group consen-
sus about a particular idea. Related to this process is the fact
that users are more likely to believe a message that they per-
ceive as coming from several independent sources, or from
an acquaintance (Jagatic et al. 2007). Spam detection sys-
tems often focus on the content of a potential spam mes-
sage — for instance, to see if the message contains a certain
link or set of tags. In detecting political astroturf, we focus
on how the message is delivered rather than on its content.
Further, many legitimate users may be unwittingly complicit
in the propagation of astroturf, having been themselves de-
ceived. Spam detection methods that focus solely on proper-
ties of user accounts, such as the number of URLs in tweets
from an account or the interval between successive tweets,
may therefore be unsuccessful in finding such abuse.
We adopt the term truthy to discriminate falsely-
propagated information from organic grassroots memes. The
term was coined by comedian Stephen Colbert to describe
something that a person believes based on emotion rather
than facts. We can then define our task as the detection of
truthy memes in the Twitter stream. Not every truthy meme
will result in a viral cascade like the one documented by
Mustafaraj and Metaxas, but we wish to test the hypothesis
that the initial stages exhibit identifiable signatures.
3 Analytical Framework
We developed a unified framework, which we call Klatsch,
that analyzes the behavior of users and diffusion of ideas in
a broad variety of data feeds. This framework is designed
to provide data interoperability for the real-time analysis of
massive social media data streams (millions of posts per
day) from sites with diverse structures and interfaces. To
this end, we model a generic stream of social networking
data as a series of events that represent interactions between
actors and memes, as shown in Fig. 1. Each event involves
some number of actors (entities that represent users), some
number of memes (entities that represent units of informa-
tion at the desired level of detail), and interactions among
them. For example, a single tweet event might involve three
or more actors: the poster, the user she is retweeting, and
the people she is addressing. The post might also involve a
set of memes consisting of ‘hashtags’ and URLs referenced
in the tweet. Each event can be thought of as contributing a
unit of weight to edges in a network structure, where nodes
are associated with either actors or memes. The timestamps
associated with the events allow us to observe the changing
structure of this network over time.
3.1 Meme Types
To study the diffusion of information on Twitter it is neces-
sary to identify a specific topic as it propagates through the
social substrate. While there exist sophisticated statistical
techniques for modeling the topics underlying bodies of text,
the small size of each tweet and the contextual drift present
in streaming data create significant complications (Wang et
al. 2003). Fortunately, several conventions shared by Twit-
ter users allow us to sidestep these issues. We focus on the
following features to identify different types of memes:
Hashtags The Twitter community uses tokens prefixed by
a hashmark (#) to label the topical content of tweets.
Some examples of popular tags are #gop,#obama, and
#desen, marking discussion about the Republican party,
President Obama, and the Delaware race for U.S. Senate,
respectively. These are often called hashtags.
Mentions A Twitter user can include another user’s screen
name in a post, prepended by the @symbol. These men-
tions can be used to denote that a particular Twitter user
is being discussed.
URLs We extract URLs from tweets by matching strings of
valid URL characters that begin with ‘http://.’ Honey-
cutt and Herring (2008) suggest that URLs are associated
with the transmission of information on Twitter.
Phrases Finally, we consider the entire text of the tweet it-
self to be a meme, once all Twitter metadata, punctuation,
and URLs have been removed.
Relying on these conventions we are able to focus on the
ways in which a large number of memes propagate through
the Twitter social network. Note that a tweet may be in-
cluded in several of these categories. A tweet containing (for
instance) two hashtags and a URL would count as a member
of each of the three resulting memes.
3.2 Network Edges
To represent the flow of information through the Twitter
community, we construct a directed graph in which nodes
are individual user accounts. An example diffusion network
involving three users is shown in Fig. 2. An edge is drawn
from node Ato Bwhen either Bis observed to retweet a
message from A, or Amentions Bin a tweet. The weight
of an edge is incremented each time we observe an event
connecting two users. In this way, either type of edge can be
understood to represent a flow of information from Ato B.
A
C
B
kin =1
kout =2
sin =1
sout =2
kin =2
kout =1
sin =3
sout =1
kin =1
kout =1
sin =1
sout =2
Figure 2: Example of a meme diffusion network involving
three users mentioning and retweeting each other. The val-
ues of various node statistics are shown next to each node.
The strength srefers to weighted degree, kstands for degree.
Observing a retweet at node Bprovides implicit confirma-
tion that information from Aappeared in B’s Twitter feed,
while a mention of Boriginating at node Aexplicitly con-
firms that A’s message appeared in B’s Twitter feed. This
may or may not be noticed by B, therefore mention edges
are less reliable indicators of information flow compared to
retweet edges.
Retweet and reply/mention information parsed from the
text can be ambiguous, as in the case when a tweet is marked
as being a ‘retweet’ of multiple people. Rather, we rely
on Twitter metadata, which designates users replied to or
retweeted by each message. Thus, while the text of a tweet
may contain several mentions, we only draw an edge to the
user explicitly designated as the mentioned user by the meta-
data. In so doing, we may miss retweets that do not use the
explicit retweet feature and thus are not captured in the meta-
data. Note that this is separate from our use of mentions as
memes (§3.1), which we parse from the text of the tweet.
4 System Architecture
We implemented a system based on the data representation
described above to automatically monitor the data stream
from Twitter, detect relevant memes, collect the tweets that
match themes of interest, and produce basic statistical fea-
tures relative to patterns of diffusion. These features are
then passed to our meme classifier and/or visualized. We
called this system “Truthy.” The different stages that lead
to the identification of the truthy memes are described in the
following subsections. A screenshot of the meme overview
page of our website (truthy.indiana.edu) is shown
in Fig. 3. Upon clicking on any meme, the user is taken to
another page with more detailed statistics about that meme.
They are also given an opportunity to label the meme as
‘truthy;’ the idea is to crowdsource the identification of
truthy memes, as an input to the classifier described in §5.
4.1 Data Collection
To collect meme diffusion data we rely on whitelisted ac-
cess to the Twitter ‘Gardenhose’ streaming API (dev.
twitter.com/pages/streaming_api). The Gar-
denhose provides detailed data on a sample of the Twitter
corpus at a rate that varied between roughly 4million tweets
Figure 3: Screenshot of the Meme Overview page of our
website, displaying a number of vital statistics about tracked
memes. Users can then select a particular meme for more
detailed information.
per day near the beginning of our study, to around 8mil-
lion tweets per day at the time of this writing. While the
process of sampling edges (tweets between users) from a
network to investigate structural properties has been shown
to produce suboptimal approximations of true network char-
acteristics (Leskovec and Faloutsos 2006), we find that the
analyses described below are able to produce accurate clas-
sifications of truthy memes even in light of this shortcoming.
4.2 Meme Detection
A second component of our system is devoted to scanning
the collected tweets in real time. The task of this meme de-
tection component is to determine which of the collected
tweets are to be stored in our database for further analysis.
Our goal is to collect only tweets (a) with content related
to U.S. politics, and (b) of sufficiently general interest in
that context. Political relevance is determined by matching
against a manually compiled list of keywords. We consider a
meme to be of general interest if the number of tweets with
that meme observed in a sliding window of time exceeds a
given threshold. We implemented a filtering step for each of
these criteria, described elsewhere (Ratkiewicz et al. 2011).
Our system has tracked a total of approximately 305 mil-
lion tweets collected from September 14 until October 27,
2010. Of these, 1.2 million contain one or more of our polit-
ical keywords; the meme filtering step further reduced this
number to 600,000. Note that this number of tweets does not
directly correspond to the number of tracked memes, as each
tweet might contribute to several memes.
4.3 Network Analysis
To characterize the structure of each meme’s diffusion net-
work we compute several statistics based on the topology
of the largest connected component of the retweet/mention
Table 1: Features used in truthy classification.
nodes Number of nodes
edges Number of edges
mean k Mean degree
mean s Mean strength
mean w Mean edge weight in largest con-
nected component
max k(i,o) Maximum (in,out)-degree
max k(i,o) user User with max. (in,out)-degree
max s(i,o) Maximum (in,out)-strength
max s(i,o) user User with max. (in,out)-strength
std k(i,o) Std. dev. of (in,out)-degree
std s(i,o) Std. dev. of (in,out)-strength
skew k(i,o) Skew of (in,out)-degree distribution
skew s(i,o) Skew of (in,out)-strength distribution
mean cc Mean size of connected components
max cc Size of largest connected component
entry nodes Number of unique injections
num truthy Number of times ‘truthy’ button was
clicked
sentiment scores Six GPOMS sentiment dimensions
graph. These include the number of nodes and edges in the
graph, the mean degree and strength of nodes in the graph,
mean edge weight, mean clustering coefficient across nodes
in the largest connected component, and the standard devi-
ation and skew of each network’s in-degree, out-degree and
strength distributions (see Fig. 2). Additionally we track the
out-degree and out-strength of the most prolific broadcaster,
as well as the in-degree and in-strength of the most focused-
upon user. We also monitor the number of unique injection
points of the meme, reasoning that organic memes (such as
those relating to news events) will be associated with larger
number of originating users.
4.4 Sentiment Analysis
We also utilize a modified version of the Google-based
Profile of Mood States (GPOMS) sentiment analysis
method (Bollen, Mao, and Pepe 2010) in the analysis of
meme-specific sentiment on Twitter. The GPOMS tool as-
signs to a body of text a six-dimensional vector with bases
corresponding to different mood attributes (Calm, Alert,
Sure, Vital, Kind, and Happy). To produce scores for a meme
along each of the six dimensions, GPOMS relies on a vocab-
ulary taken from an established psychometric evaluation in-
strument extended with co-occurring terms from the Google
n-gram corpus. We applied the GPOMS methodology to the
collection of tweets, obtaining a six-dimensional mood vec-
tor for each meme.
5 Automatic Classification
As an application of the analyses performed by the Truthy
system, we trained a binary classifier to automatically label
legitimate and truthy memes.
We began by producing a hand-labeled corpus of train-
ing examples in three classes — ‘truthy,’ ‘legitimate,’ and
‘remove.’ We labeled these by presenting random memes to
several human reviewers (the authors of the paper and a few
Table 2: Performance of two classifiers with and without re-
sampling training data to equalize class sizes. All results are
averaged based on 10-fold cross-validation.
Classifier Resampling? Accuracy AUC
AdaBoost No 92.6% 0.91
AdaBoost Yes 96.4% 0.99
SVM No 88.3% 0.77
SVM Yes 95.6% 0.95
Table 3: Confusion matrices for a boosted decision stump
classifier with and without resampling. The labels on the
rows refer to true class assignments; the labels on the
columns are those predicted.
No resampling With resampling
Truthy Legitimate Truthy Legitimate
T 45 (12%) 16 (4%) 165 (45%) 6 (1%)
L 11 (3%) 294 (80%) 7 (2%) 188 (51%)
additional volunteers), and asking them to place each meme
in one of the three categories. A meme was to be classified as
‘truthy’ if a significant portion of the users involved in that
meme appeared to be spreading it in misleading ways —
e.g., if a number of the accounts tweeting about the meme
appeared to be robots or sock puppets, the accounts appeared
to follow only other propagators of the meme (clique behav-
ior), or the users engaged in repeated reply/retweet exclu-
sively with other users who had tweeted the meme. ‘Legit-
imate’ memes were described as memes representing nor-
mal use of Twitter — several non-automated users convers-
ing about a topic. The final category, ‘remove,’ was used for
memes in a non-English language or otherwise unrelated to
U.S. politics (#youth, for example). These memes were
not used in the training or evaluation of classifiers.
Upon gathering 252 annotated memes, we observed an
imbalance in our labeled data (231 legitimate and only 21
truthy). Rather than simply resampling from the smaller
class, as is common practice in the case of class imbal-
ance, we performed a second round of human annotations
on previously-unlabeled memes predicted to be ‘truthy’ by
the classifier trained in the previous round, gaining 103 more
annotations (74 legitimate and 40 truthy). We note that the
human classifiers knew that the additional memes were pos-
sibly more likely to be truthy, but that the classifier was not
very good at this point due to the paucity of training data
and indeed was often contradicted by the human classifica-
tion. This bootstrapping procedure allowed us to manually
label a larger portion of truthy memes with less bias than
resampling. Our final training dataset consisted of 366 train-
ing examples — 61 ‘truthy’ memes and 305 legitimate ones.
In a few cases where multiple reviewers disagreed on the la-
beling of a meme, we determined the final label by reaching
consensus in a group discussion among all reviewers. The
dataset is available online.1
We experimented with several classifiers, as implemented
1cnets.indiana.edu/groups/nan/truthy
Table 4: Top 10 most discriminative features, according to a
χ2analysis under 10-fold cross validation. Intervals repre-
sent the variation of the χ2or rank across the folds.
Feature χ2Rank
mean w 230 ±4 1.0±0.0
mean s 204 ±6 2.0±0.0
edges 188 ±4 4.3±1.9
skew ko 185 ±4 4.4±1.1
std si 183 ±5 5.1±1.3
skew so 184 ±4 5.1±0.9
skew si 180 ±4 6.7±1.3
max cc 177 ±4 8.1±1.0
skew ki 174 ±4 9.6±0.9
std ko 168 ±5 11.5±0.9
by Hall et al. (2009). Since comparing different learning
algorithms is not our goal, we report on the results ob-
tained with just two well-known classifiers: AdaBoost with
DecisionStump, and SVM. We provided each classifier with
31 features about each meme, as shown in Table 1. A few
of these features bear further explanation. Measures relating
to ‘degree’ and ‘strength’ refer to the nodes in the diffusion
network of the meme in question — that is, the number of
people that each user retweeted or mentioned, and the num-
ber of times these connections were made, respectively. We
defined an ‘injection point’ as a tweet containing the meme
which was not itself a retweet; our intuition was that memes
with a larger number of injection points were more likely to
be legitimate. No features were normalized.
As the number of instances of truthy memes was still less
than instances of legitimate ones, we also experimented with
resampling the training data to balance the classes prior to
classification. The performance of the classifiers is shown in
Table 2, as evaluated by their accuracy and the area under
their ROC curves (AUC). The latter is an appropriate evalu-
ation measure in the presence of class imbalance. In all cases
these preliminary results are quite encouraging, with accu-
racy around or above 90%. The best results are obtained by
AdaBoost with resampling: better than 96% accuracy and
0.99 AUC. Table 3 further shows the confusion matrices for
AdaBoost. In this task, false negatives (truthy memes incor-
rectly classified as legitimate, in the upper-right quadrant
of each matrix) are less desirable than false positives (the
lower-left quadrant). In the worst case, the false negative rate
is 4%. We did not perform any feature selection or other op-
timization; the classifiers were provided with all the features
computed for each meme (Table 1). Table 4 shows the 10
most discriminative features, as determined by χ2analysis.
Network features appear to be more discriminative than sen-
timent scores or the few user annotations that we collected.
6 Examples of Astroturf
The Truthy system allowed us to identify several egregious
instances of astroturf memes. Some of these cases caught
the attention of the popular press due to the sensitivity of the
topic in the run up to the 2010 U.S. midterm political elec-
tions, and subsequently many of the accounts involved were
suspended by Twitter. Let us illustrate a few representative
examples.
#ampat The #ampat hashtag is used by many conserva-
tive users. What makes this meme suspicious is that the
bursts of activity are driven by two accounts, @CSteven
and @CStevenTucker, which are controlled by the
same user, in an apparent effort to give the impression
that more people are tweeting about the same topics. This
user posts the same tweets using the two accounts and has
generated a total of over 41,000 tweets in this fashion.
See Fig. 4(A) for the #ampat diffusion network.
@PeaceKaren 25 This account did not disclose informa-
tion about the identity of its owner, and generated a very
large number of tweets (over 10,000 in four months). Al-
most all of these tweets supported several Republican can-
didates. Another account, @HopeMarie 25, had a simi-
lar behavior to @PeaceKaren 25 in retweeting the ac-
counts of the same candidates and boosting the same web-
sites. It did not produce any original tweets, and in addi-
tion it retweeted all of @PeaceKaren 25’s tweets, pro-
moting that account. These accounts had also succeeded
at creating a ‘twitter bomb:’ for a time, Google searches
for “gopleader” returned these tweets in the first page
of results. A visualization of the interaction between these
two accounts can be seen in Fig. 4(B). Both accounts were
suspended by Twitter by the time of this writing.
gopleader.gov This meme is the website of the Re-
publican Leader John Boehner. It looks truthy because
it is promoted by the two suspicious accounts described
above. The diffusion of this URL is shown in Fig. 4(C).
How Chris Coons budget works- uses tax $ 2 attend din-
ners and fashion shows
This is one of a set of truthy memes smearing Chris
Coons, the Democratic candidate for U.S. Senate from
Delaware. Looking at the injection points of these
memes, we uncovered a network of about ten bot ac-
counts. They inject thousands of tweets with links to
posts from the freedomist.com website. To avoid
detection by Twitter and increase visibility to different
users, duplicate tweets are disguised by adding different
hashtags and appending junk query parameters to the
URLs. To generate retweeting cascades, the bots also
coordinate mentioning a few popular users. When these
targets perceive receiving the same news from several
people, they are more likely to think it is true and spread
it to their followers. Most bot accounts in this network
can be traced back to a single person who runs the
freedomist.com website. The diffusion network
corresponding to this case is illustrated in Fig. 4(D).
These are just a few examples of truthy memes that our
system was able to identify. Two other networks of bots were
shut down by Twitter after being detected by Truthy.
Fig. 4 also shows the diffusion networks for four le-
gitimate memes. One, #Truthy, was injected as an ex-
periment by the NPR Science Friday radio program. An-
other, @senjohnmccain, displays two different commu-
nities in which the meme was propagated: one by retweets
A B
C)
CD
E)
E F
G)
GH
Figure 4: Diffusion networks of sample memes from our dataset. Edges are represented using the same notation as in Fig. 2.
Four truthy memes are shown in the top row and four legitimate ones in the bottom row. (A) #ampat (B) @PeaceKaren 25
(C) gopleader.gov (D) “How Chris Coons budget works- uses tax $ 2 attend dinners and fashion shows” (E) #Truthy
(F) @senjohnmccain (G) on.cnn.com/aVMu5y (H) “Obama said taxes have gone down during his administration. That’s
ONE way to get rid of income tax — getting rid of income”
from @ladygaga in the context of discussion on the re-
peal of the “Don’t ask, don’t tell” policy on gays in the mil-
itary, and the other by mentions of @senjohnmccain. A
gallery with detailed explanations about various truthy and
legitimate memes can be found on our website (truthy.
indiana.edu/gallery).
7 Discussion
Our simple classification system was able to accurately de-
tect ‘truthy’ memes based on features extracted from the
topology of the diffusion networks. Using this system we
have been able to identify a number of ‘truthy’ memes.
Though few of these exhibit the explosive growth charac-
teristic of true viral memes, they are nonetheless clear ex-
amples of coordinated attempts to deceive Twitter users.
Truthy memes are often spread initially by bots, causing
them to exhibit, when compared with organic memes, patho-
logical diffusion graphs. These graphs show a number of pe-
culiar features, including high numbers of unique injection
points with few or no connected components, strong star-
like topologies characterized by high average degree, and
most tellingly large edge weights between dyads.
In addition, we observed several other approaches to de-
ception that were not discoverable using graph-based prop-
erties only. One case was that of a bot network using unique
query string suffixes on otherwise identical URLs in an ef-
fort to make them look distinct. This works because many
URL-shortening services ignore query strings when process-
ing redirect requests. In another case we observed a number
of automated accounts that use text segments drawn from
newswire services to produce multiple legitimate-looking
tweets in between the injection of URLs. These instances
highlight several of the more general properties of truthy
memes detected by our system.
The accuracy scores we obtain in the classification task
are surprisingly high. We hypothesize that this performance
is partially explained by the fact that a consistent propor-
tion of the memes were failed attempts of starting a cascade.
In these cases the networks reduced to isolated injection
points or small components, resulting in network properties
amenable to easy classification.
Despite the fact that many of the memes discussed in this
paper are characterized by small diffusion networks, it is im-
portant to note that this is the stage at which such attempts
at deception must be identified. Once one of these attempts
is successful at gaining the attention of the community, the
meme spreading pattern becomes indistinguishable from an
organic one. Therefore, the early identification and termina-
tion of accounts associated with astroturf memes is critical.
Future work could explore further crowdsourcing the an-
notation of truthy memes. In our present system, we were
not able to collect sufficient crowdsourcing data (only 304
clicks of the ‘truthy’ button, and mostly correlated with
meme popularity), but these annotations may well prove use-
ful with more data. Several other promising features could
be used as input to a classifier, such as the age of the ac-
counts involved in spreading a meme, the reputation of users
based on other memes they have contributed, and other fea-
tures from bot detection methods (Chu et al. 2010).
Acknowledgments. We are grateful to A. Vespignani, C.
Catutto, J. Ramasco, and J. Lehmann for helpful discussions, J.
Bollen for his GPOMS code, T. Metaxas and E. Mustafaraj for
inspiration and advice, and Y. Wang for Web design support. We
thank the Gephi toolkit for aid in our visualizations and the many
users who have provided feedback and annotations. We acknowl-
edge support from NSF (grant No. IIS-0811994), Lilly Foundation
(Data to Insight Center Research Grant), the Center for Complex
Networks and Systems Research, and the IUB School of Informat-
ics and Computing.
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