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Political Polarization on Twitter

Authors:
Political Polarization on Twitter
M. D. Conover, J. Ratkiewicz, M. Francisco, 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
In this study we investigate how social media shape the
networked public sphere and facilitate communication be-
tween communities with different political orientations. We
examine two networks of political communication on Twit-
ter, comprised of more than 250,000 tweets from the six
weeks leading up to the 2010 U.S. congressional midterm
elections. Using a combination of network clustering algo-
rithms and manually-annotated data we demonstrate that the
network of political retweets exhibits a highly segregated par-
tisan structure, with extremely limited connectivity between
left- and right-leaning users. Surprisingly this is not the case
for the user-to-user mention network, which is dominated by
a single politically heterogeneous cluster of users in which
ideologically-opposed individuals interact at a much higher
rate compared to the network of retweets. To explain the dis-
tinct topologies of the retweet and mention networks we con-
jecture that politically motivated individuals provoke inter-
action by injecting partisan content into information streams
whose primary audience consists of ideologically-opposed
users. We conclude with statistical evidence in support of this
hypothesis.
1 Introduction
Social media play an important role in shaping political dis-
course in the U.S. and around the world (Bennett 2003;
Benkler 2006; Sunstein 2007; Farrell and Drezner 2008;
Aday et al. 2010; Tumasjan et al. 2010; O’Connor et al.
2010). According to the Pew Internet and American Life
Project, six in ten U.S. internet users, nearly 44% of Amer-
ican adults, went online to get news or information about
politics in 2008. Additionally, Americans are taking an ac-
tive role in online political discourse, with 20% of internet
users contributing comments or questions about the politi-
cal process to social networking sites, blogs or other online
forums (Pew Internet and American Life Project 2008).
Despite this, some empirical evidence suggests that politi-
cally active web users tend to organize into insular, homoge-
nous communities segregated along partisan lines. Adamic
and Glance (2005) famously demonstrated that political
blogs preferentially link to other blogs of the same politi-
cal ideology, a finding supported by the work of Hargittai,
Copyright c
2011, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
Gallo, and Kane (2007). Consumers of online political in-
formation tend to behave similarly, choosing to read blogs
that share their political beliefs, with 26% more users do-
ing so in 2008 than 2004 (Pew Internet and American Life
Project 2008).
In its own right, the formation of online communities is
not necessarily a serious problem. The concern is that when
politically active individuals can avoid people and informa-
tion they would not have chosen in advance, their opinions
are likely to become increasingly extreme as a result of being
exposed to more homogeneous viewpoints and fewer credi-
ble opposing opinions. The implications for the political pro-
cess in this case are clear. A deliberative democracy relies on
a broadly informed public and a healthy ecosystem of com-
peting ideas. If individuals are exposed exclusively to people
or facts that reinforce their pre-existing beliefs, democracy
suffers (Sunstein 2002; 2007).
In this study we examine networks of political commu-
nication on the Twitter microblogging service during the
six weeks prior to the 2010 U.S. midterm elections. Sam-
pling data from the Twitter ‘gardenhose’ API, we identi-
fied 250,000 politically relevant messages (tweets) produced
by more than 45,000 users. From these tweets we isolated
two networks of political communication — the retweet
network, in which users are connected if one has rebroad-
cast content produced by another, and the mention network,
where users are connected if one has mentioned another in a
post, including the case of tweet replies.
We demonstrate that the retweet network exhibits a highly
modular structure, segregating users into two homogenous
communities corresponding to the political left and right. In
contrast, we find that the mention network does not exhibit
this kind of political segregation, resulting in users being ex-
posed to individuals and information they would not have
been likely to choose in advance.
Finally, we provide evidence that these network structures
result in part from politically motivated individuals annotat-
ing tweets with multiple hashtags whose primary audiences
consist of ideologically-opposed users, a behavior also doc-
umented in the work of Yardi and boyd (2010). We argue
that this process results in users being exposed to content
they are not likely to rebroadcast, but to which they may
respond using mentions, and provide statistical evidence in
support of this hypothesis.
The major contributions of this work are:
Creation and release of a network and text dataset derived
from more than 250,000 politically-related Twitter posts
authored in the weeks preceeding the 2010 U.S. midterm
elections (§2).
Cluster analysis of networks derived from this corpus
showing that the network of retweet exhibits clear seg-
regation, while the mention network is dominated by a
single large community (§3.1).
Manual classification of Twitter users by political align-
ment, demonstrating that the retweet network clusters cor-
respond to the political left and right. These data also
show the mention network to be politically heteroge-
neous, with users of opposing political views interacting
at a much higher rate than in the retweet network (§3.3).
An interpretation of the observed community structures
based on injection of partisan content into ideologically
opposed hashtag information streams (§4).
2 Data and Methods
2.1 The Twitter Platform
Twitter is a popular social networking and microblogging
site where users can post 140-character messages, or tweets.
Apart from broadcasting tweets to an audience of followers,
Twitter users can interact with one another in two primary
public ways: retweets and mentions. Retweets act as a form
of endorsement, allowing individuals to rebroadcast content
generated by other users, thereby raising the content’s vis-
ibility (boyd, Golder, and Lotan 2008). Mentions function
differently, allowing someone to address a specific user di-
rectly through the public feed, or, to a lesser extent, refer
to an individual in the third person (Honeycutt and Herring
2008). These two means of communication —retweets and
mentions— serve distinct and complementary purposes, to-
gether acting as the primary mechanisms for explicit, public
user-user interaction on Twitter.
Hashtags are another important feature of the Twitter plat-
form. They allow users to annotate tweets with metadata
specifying the topic or intended audience of a communica-
tion. For example, #dadt stands for “Don’t Ask Don’t Tell”
and #jlot for “Jewish Libertarians on Twitter.” Each hash-
tag identifies a stream of content, with users’ tag choices de-
noting participation in different information channels.
The present analysis leverages data collected from the
Twitter ‘gardenhose’ API (dev.twitter.com/pages/
streaming_api) between September 14th and Novem-
ber 1st, 2010 — the run-up to the November 4th U.S.
congressional midterm elections. During the six weeks of
data collection we observed approximately 355 million
tweets. Our analysis utilizes an infrastructure and website
(truthy.indiana.edu) designed to analyze the spread
of information on Twitter, with special focus on political
content (Ratkiewicz et al. 2011).
2.2 Identifying Political Content
Let us define a political communication as any tweet con-
taining at least one politically relevant hashtag. To identify
Table 1: Hashtags related to #p2,#tcot, or both. Tweets
containing any of these were included in our sample.
Just #p2 #casen #dadt #dc10210 #democrats #du1
#fem2 #gotv #kysen #lgf #ofa #onenation
#p2b #pledge #rebelleft #truthout #vote
#vote2010 #whyimvotingdemocrat #youcut
Both #cspj #dem #dems #desen #gop #hcr
#nvsen #obama #ocra #p2 #p21 #phnm
#politics #sgp #tcot #teaparty #tlot
#topprog #tpp #twisters #votedem
Just #tcot #912 #ampat #ftrs #glennbeck #hhrs
#iamthemob #ma04 #mapoli #palin
#palin12 #spwbt #tsot #tweetcongress
#ucot #wethepeople
Table 2: Hashtags excluded from the analysis due to ambigu-
ous or overly broad meaning.
Excl. from #p2 #economy #gay #glbt #us #wc #lgbt
Excl. from both #israel #rs
Excl. from #tcot #news #qsn #politicalhumor
an appropriate set of political hashtags and to avoid intro-
ducing bias into the sample, we performed a simple tag
co-occurrence discovery procedure. We began by seeding
our sample with the two most popular political hashtags,
#p2 (“Progressives 2.0”) and #tcot (“Top Conservatives
on Twitter”). For each seed we identified the set of hashtags
with which it co-occurred in at least one tweet, and ranked
the results using the Jaccard coefficient. For a set of tweets S
containing a seed hashtag, and a set of tweets Tcontaining
another hashtag, the Jaccard coefficient between Sand Tis
σ(S, T ) = |ST|
|ST|.(1)
Thus, when the tweets in which both seed and hashtag oc-
cur make up a large portion of the tweets in which either
occurs, the two are deemed to be related. Using a similar-
ity threshold of 0.005 we identified 66 unique hashtags (Ta-
ble 1), eleven of which we excluded due to overly-broad or
ambiguous meaning (Table 2). This process resulted in a cor-
pus of 252,300 politically relevant tweets. There is substan-
tial overlap between streams associated with different po-
litical hashtags because many tweets contain multiple hash-
tags. As a result, lowering the similarity threshold leads to
only modest increases in the number of political tweets in
our sample — which do not substantially affect the results
of our analysis — while introducing unrelated hashtags.
2.3 Political Communication Networks
From the tweets containing any of the politically rele-
vant hashtags we constructed networks representing political
communication among Twitter users. Focusing on the two
primary modes of public user-user interaction, mentions and
retweets, we define communication links in the following
ways. In the retweet network an edge runs from a node rep-
resenting user Ato a node representing user Bif Bretweets
content originally broadcast by A, indicating that informa-
tion has propagated from Ato B. In the mention network
an edge runs from Ato Bif Amentions Bin a tweet, in-
dicating that information may have propagated from Ato
B(a tweet mentioning Bis visible in B’s timeline). Both
networks therefore represent potential pathways for infor-
mation to flow between users.
The retweet network consists of 23,766 non-isolated
nodes among a total of 45,365. The largest connected com-
ponent accounts for 18,470 nodes, with 102 nodes in the
next-largest component. The mention network is smaller,
consisting of 10,142 non-isolated nodes out of 17,752 to-
tal. It has 7,175 nodes in its largest connected component,
and 119 in the next-largest. Because of their dominance we
focus on the largest connected components for the rest of our
analysis. We observe that the retweet and mention networks
exhibit very similar scale-free topology (power-law degree
distribution not shown), with a number of users receiving or
spreading a huge amount of information.
3 Cluster Analysis
Initial inspection of the retweet suggested that users pref-
erentially retweet other users with whom they agree politi-
cally, while the mention network appeared to form a bridge
between users of different ideologies. We explore this hy-
pothesis in several stages. In §3.1 we use network clustering
algorithms to demonstrate that the retweet network exhibits
two highly segregated communities of users, while the men-
tion network does not. In §3.2 we describe a statistical anal-
ysis of political tweet content, showing that messages pro-
duced by members of the same community are more similar
to each other than messages produced by users in different
communities. Finally, in §3.3, by manually annotating users,
we show that the retweet network is polarized on a partisan
basis, while the mention network is much more politically
heterogeneous.
3.1 Community Structure
To establish the large-scale political structure of the retweet
and mention networks we performed community detection
using a label propagation method for two communities.1
Label propagation (Raghavan, Albert, and Kumara 2007)
works by assigning an initial arbitrary cluster membership
to each node and then iteratively updating each node’s label
according to the label that is shared by most of its neighbors.
Ties are broken randomly when they occur. Label propaga-
tion is a greedy hill-climbing algorithm. As such it is ex-
tremely efficient, but can easily converge to different sub-
optimal clusters dependent on initial label assignments and
random tie breaking. To improve its effectiveness and stabil-
ity, we seeded the algorithm with initial node labels deter-
mined by the leading-eigenvector modularity maximization
method for two clusters (Newman 2006).
To confirm that we can produce consistent clusters across
different runs we executed the algorithm one hundred times
1While the partisan nature of U.S. political discourse makes two
a natural number of clusters, in §3.3 we describe the effect on our
analysis of increasing the target number of communities.
Table 3: Minimum, maximum, and average ARI similarities
between 4,950 pairs of cluster assignments computed by la-
bel propagation on the mention and retweet networks.
Network Min Max Mean
Mention 0.80 1.0 0.89
Retweet 0.94 0.98 0.96
for each network and compared the label assignments pro-
duced by every run. Table 3 reports the high average agree-
ment between the resulting cluster assignments for each
graph, as computed by the Adjusted Rand Index (Hubert
and Arabie 1985). Such a high agreement suggests that the
clusters are consistent, and therefore we avoid resorting to
consensus clustering for simplicity.
Figure 1 shows the retweet and mention networks, laid
out using a force-directed layout algorithm (Fruchterman
and Reingold 1991), with node colors determined by the
assigned communities. The retweet network exhibits two
distinct communities of users, while the mention network
is dominated by a single massive cluster of interconnected
users. Modularity (Newman and Girvan 2004) resulting
from the cluster assignments offers a first measure of seg-
regation, and reinforces the qualitative finding above. The
modularity induced by the communities in the retweet and
mention networks have values of 0.48 and 0.17, respectively.
A direct comparison of the modularity values is how-
ever problematic because of the different size and overall
connectivity of the two networks. We need a way to com-
pare the ‘goodness’ of cluster assignments across different
graphs. To this end we generate, for both retweet and men-
tion graphs, N= 1000 shuffled versions of the graph that
preserve the original degree sequence.
Each randomized network is clustered with the method
described above for the original graphs and associated with
the resulting modularity value. We use the distribution of
these values as a baseline against which to compare the qual-
ity of the clusters in the original graph. The intuition be-
hind this approach is that the degree to which the actual
graphs are more modular than the shuffled graphs tells us
how amenable each is to being split into two clusters —
a measure of segregation. The modularities of the shuffled
graphs can be viewed as observed values of a random vari-
able. We can use these values to compute z-scores for the
modularities of the original networks; they are zr= 11.02
and zm= 2.06 for the retweet and mention networks, re-
spectively. We conclude that the community structure found
in the retweet network is significantly more segregated than
that found in the mention network.2
In summary, the retweet network contains two clusters of
users who preferentially propagate content within their own
communities. However, we do not find such a structure in
the network of mentions and replies among politically ac-
2This discussion assumes that the modularities of the shuffled
graph cluster assignments are distributed normally, which is not
true in general. See Appendix A for an argument that does not need
this assumption, and reaches the same conclusion.
Figure 1: The political retweet (left) and mention (right) networks, laid out using a force-directed algorithm. Node colors reflect
cluster assignments (see §3.1). Community structure is evident in the retweet network, but less so in the mention network. We
show in §3.3 that in the retweet network, the red cluster A is made of 93% right-leaning users, while the blue cluster B is made
of 80% left-leaning users.
tive Twitter users. This structural difference is of particular
importance with respect to political communication, as we
now have statistical evidence to suggest that mentions and
replies may serve as a conduit through which users are ex-
posed to information and opinions they might not choose in
advance. Despite this promising finding, the work of Yardi
and boyd (2010) suggests that cross-ideological interactions
may reinforce pre-existing in-group/out-group identities, ex-
acerbating the problem of political polarization.
3.2 Content Homogeneity
The clustering described above was based only on the net-
work properties of the retweet and mention graphs. An inter-
esting question, therefore, is whether it has any significance
in terms of the actual content of the discussions involved.
To address this issue we associate each user with a profile
vector containing all the hashtags in her tweets, weighted by
their frequencies. We can then compute the cosine similari-
ties between each pair of user profiles, separately for users
in the same cluster and users in different clusters. Figure 2
shows that in the mention network, users placed in the same
cluster are not likely to be much more similar to each other
than users in different clusters. On the other hand, in the
retweet network, users in cluster A are more likely to have
very similar profiles than users in cluster B, and users in dif-
ferent clusters are the least similar to each other. As a result
the average similarity within retweet clusters is higher than
across clusters. Further, we note that in both mention and
retweet networks, one of the clusters is more cohesive than
the other — meaning the tag usage within one community is
more homogeneous.
Retweet Mention
AA0.31 0.31
BB0.20 0.22
AB0.13 0.26
10-1
100
101
0 0.2 0.4 0.6 0.8 1
P(cos(a, b))
cos(a, b)
Clusters A
Cluster B
Different clusters
Figure 2: Cosine similarities among user profiles. The table
on the left shows the average similarities in the retweet and
mention networks for pairs of users both in cluster A, both in
cluster B, and for users in different clusters. All differences
are significant at the 95% confidence level. The plot on the
right displays the actual distributions of cosine similarities
for the retweet network.
3.3 Political Polarization
Given the communities of the retweet network identified in
§3.1, their content homogeneity uncovered in §3.2, and
the findings of previous studies, it is natural to investigate
whether the clusters in the retweet network correspond to
groups of users of similar political alignment.
To accomplish this in a systematic, reproducible way we
used a set of techniques from the social sciences known
as qualitative content analysis (Krippendorff 2004; Kolbe
1991). Similar to assigning class labels to training data in su-
pervised machine learning, content analysis defines a set of
practices that enable social scientists to define reproducible
categories for qualitative features of text. Next we outline
our annotation categories, and then explain the procedures
used to establish the rigor of these category definitions.
Our coding goals were simple: for a given user we wanted
to identify whether his tweets express a ‘left’ or ‘right’
political identity, or if his identity is ‘undecidable.’ The
groups primarily associated with a ‘left’ political identity are
democrats and progressives; those primarily associated with
a ‘right’ political identity are republicans, conservatives, lib-
ertarians and the Tea Party. A user coded as ‘undecidable’
may be taking part in a political dialogue, but from the con-
tent of her tweets it is difficult to make a clear determination
about political alignment. Irrelevant non-English and spam
accounts constitute less than 3% of the total corpus and were
excluded from this analysis. We experimented with more de-
tailed categorization rubrics but the simple definitions de-
scribed above yielded the highest inter-annotator agreement
in early trials of the coding process.
Using this coding scheme one author first annotated 1,000
random users who appeared in both the retweet and mention
networks. Annotations were determined solely on the basis
of the tweets present in the six week sample. In line with
the standards of the field, we had a non-author judge with a
broad knowledge of politics annotate 200 random users from
the set of 1,000 to establish the reproducibility of this anno-
tation scheme. The judge was provided a brief overview of
the study and introduced to the coding guidelines described
above, but did not have any other interaction with the authors
during the coding process.
The statistic typically used in the social sciences to mea-
sure the extent to which a coders’ annotations agree with an
objective judge is Cohen’s Kappa, defined as
κ=P(α)P()
1P()(2)
where P(α)is the observed rate of agreement between an-
notators, and P()is the expected rate of random agreement
given the relative frequency of each class label (Krippen-
dorff 2004; Kolbe 1991). For agreement between the ‘left’
and ‘right’ categories we report κ= 0.80 and κ= 0.82
respectively, both of which fall in the “nearly perfect agree-
ment” range (Landis and Koch 1977). For the undecidable
category we found “fair to moderate” agreement (κ= 0.42),
indicating that there are users for whom a political iden-
tity might be discernible in the context of specific domain
knowledge. To address this issue of context-sensitive ambi-
guity we had a second author also annotate the entire set of
1,000 users. This allowed us to assign a label to a user when
either author was able to determine a political alignment, re-
solving ambiguity in 15.4% of users.
For completeness we also report binomial p-values for ob-
served agreement, treating annotation pairs as observations
from a series of Bernoulli trials. Similar to the Kappa statis-
tic results, inter-annotator agreement for the ‘left’ and ‘right’
categories is very high (p < 1012). Agreement on the ‘un-
decidable’ category is again lower (p= 0.18).
Based on this analysis it is clear that a majority of polit-
ically active users on Twitter express a political identity in
their tweets. Both annotators were unable to determine a po-
litical identity in only 8% of users. A more conservative ap-
proach to label assignment does not change this story much;
Table 4: Partisan composition and size of network clusters
as determined by manual inspection of 1,000 random user
profiles.
Network Clust. Left Right Undec. Nodes
Retweet A 1.19% 93.4% 5.36% 7,115
B 80.1% 8.71% 11.1% 11,355
Mention A 39.5% 52.2% 8.18% 7,021
B 9.52% 85.7% 4.76% 154
if we assign a political identity only to users for whom both
annotators agree, we report unambiguous political valences
for more than 75% of users.
Using these annotations we can infer the expected politi-
cal makeup of the network communities identified in §3.1.
As shown in Table 4, the network of political retweets ex-
hibits a highly partisan community structure with two ho-
mogenous clusters of users who tend to share the same po-
litical identity. Surprisingly, the mention network does not
exhibit a clear partisan community structure. Instead we find
that it is dominated by a politically heterogeneous cluster ac-
counting for more than 97% of the users, suggests that po-
litically active Twitter users may be exposed to views with
which they do not agree in the form of cross-ideological
mentions.
Increasing the number of target communities in the men-
tion network does not reveal a more fine-grained ideological
structure, but instead results in smaller yet politically hetero-
geneous clusters. Similarly, the retweet network communi-
ties are maximally homogenous in the case of two clusters.
4 Interaction Analysis
The strong segregation evident in the retweet network and
the fact that the two clusters correspond to political ideolo-
gies suggest that, when engaging in political discourse, users
often retweet just other users with whom they agree politi-
cally. The dominance of the mention network by a single
heterogeneous cluster of users, however, suggests that indi-
viduals of different political alignments may interact with
one another much more frequently using mentions. Let us
test these conjectures, and propose an explanation based on
selective hashtag use by politically motivated individuals.
4.1 Cross-Ideological Interactions
To investigate cross-ideological mentions, we compare the
observed number of links between manually-annotated
users with the value we would expect in a graph where users
connect to one another without any knowledge of political
alignment. The intuition for the expected number of links is
as follows: for a set of users with kdirected edges among
them, we preserve the source of each edge and assign the
target vertex to a random user in the graph, simulating a sce-
nario in which users connected irrespective of political ide-
ology. For example, if there are a total of kRlinks originat-
ing from right-leaning users, and the numbers of left-leaning
and right-leaning users are ULand URrespectively, then the
Table 5: Ratios between observed and expected number of
links between users of different political alignments in the
mention and retweet networks.
Mention Retweet
Left Right Left Right
Left 1.23 0.68 1.70 0.05
Right 0.77 1.31 0.03 2.32
expected number of edges going from right-leaning to left-
leaning users is given by:
E[RL] = kR·UL
UL+UR
.(3)
We compute the other expected numbers of edges (RR,
LR,LL) in the same way.
In Table 5 we report the ratio between the observed and
expected numbers of links between users of each political
alignment. We see that for both means of communication,
users are more likely to engage people with whom they
agree. This effect, however, is far less pronounced in the
mention network, where we observe significant amounts of
cross-ideological interaction.
4.2 Content Injection
Any Twitter user can select arbitrary hashtags to annotate
his or her tweets. We observe that users frequently produce
tweets containing hashtags that target multiple politically
opposed audiences, and we propose that this phenomenon
may be responsible in part for the network structures de-
scribed in this study.
As a thought experiment, consider an individual who
prefers to read tweets produced by users from the political
left. This user would frequently see the popular hashtag #p2
(“Progressives 2.0”) in the body of tweets produced by other
left-leaning users, as shown in Table 6. However, if this user
clicked on the #p2 hashtag hyperlink in one of these tweets,
or searched for it explicitly, she would be exposed to content
from users on both sides of the political spectrum. In fact,
because of the disproportionate number of tweets produced
by left- and right-leaning users, nearly 30% of the tweets
in the #p2 search feed would originate from right-leaning
users.
A natural question is why a user would annotate tweets
with hashtags strongly associated with ideologically
opposed users. One explanation might be that he seeks
to expose those users to information that reinforces his
political views. Consider the following tweets:
User A: Please follow @Username for
an outstanding progressive voice! #p2
#dems #prog #democrats #tcot
User B: Couple Aborts Twin Boys For
Being Wrong Gender..http://bit.ly/xyz
#tcot #hhrs #christian #tlot #teaparty
#sgp #p2 #prolife
Table 6: The ten most popular hashtags produced by left- and
right-leaning users in the manually annotated set of users,
including frequency of use in the two retweet communities
and ideological valence.
Rank Hashtag Left Right Valence
1#tcot 2,949 13,574 0.384
2#p2 6,269 3,153 -0.605
3#teaparty 1,261 5,368 0.350
4#tlot 725 2,156 0.184
5#gop 736 1,951 0.128
6#sgp 226 2,563 0.694
7#ocra 434 1,649 0.323
8#dems 953 194 -0.818
9#twisters 41 990 0.843
10 #palin 200 838 0.343
Total 26,341 53,880
These tweets were selected from the first page of the re-
altime search results for the #tcot (“Top Conservatives on
Twitter”) and #p2 hashtags, respectively, and messages in
this style make up a substantial portion of the results.
This behavior does not go unnoticed by users, as under-
scored by the emergence of the left-leaning hashtag #p21.
According to a crowdsourced hashtag definition site (www.
tagdef.com), #p21 is a hashtag for “Progressives sans
RWNJs” and “Political progressives w/o all the RWNJ spam
that #p2 has,” where RWNJ is an acronym for “Right Wing
NutJob.” This tag appears to have emerged in response to
the efforts by right-leaning users to inject messages into the
high-profile #p2 content stream, and ostensibly serves as a
place where progressives can once again be exposed only to
content aligned with their views.
We propose that when a user is exposed to ideologically
opposed content in this way, she will be unlikely to rebroad-
cast it, but may choose to respond directly to the origina-
tor in the form of a mention. Consequently, the network of
retweets would exhibit ideologically segregated community
structure, while the network of mentions would not.
4.3 Political Valence
To explore the content injection phenomenon in more detail
let us introduce the notion of political valence, a measure
that encodes the relative prominence of a tag among left- and
right-leaning users. Let N(t, L)and N(t, R)be the numbers
of occurrences of tag tin tweets produced by left- and right-
leaning users, respectively. Then define the valence of tas
V(t) = 2 N(t, R)/N(R)
[N(t, L)/N(L)] + [N(t, R)/N(R)] 1(4)
where N(R) = PtN(t, R)is the total number of occur-
rences of all tags in tweets by right-leaning users and N(L)
is defined analogously for left-leaning users. The translation
and scaling constants serve to bound the measure between
1for a tag only used by the left, and +1 for a tag only used
by the right. Table 7 illustrates the usefulness of this measure
by listing hashtags sampled from valence quintiles ranging
Table 7: Hashtags in tweets by users across the political spectrum, grouped by valence quintiles.
Far Left Moderate Left Center Moderate Right Far Right
#healthcare
#judaism #hollywood
#2010elections
#capitalism #recession
#security #dreamact
#publicoption
#topprogs
#aarp #women
#citizensunited
#democratic
#banksters #energy
#sarahpalin
#progressives
#stopbeck #iraq
#democrats #social
#seniors #dnc
#budget #political
#goproud #christian
#media #nobel
#rangel #waste
#saveamerica
#american #gold
#repeal #mexico
#terrorism #gopleader
#palin12
#912project #twisters
#gop2112 #israel
#foxnews #mediabias
#constitution
#patriots #rednov
#abortion
-10 %
0 %
10 %
20 %
30 %
40 %
50 %
60 %
-1 -0.5 0 0.5 1
Percentage of Mentions
Mean User Valence
Sent
Received
Figure 3: Proportion of mentions a user sends and receives
to and from ideologically-opposed users relative to her va-
lence. Points represent binned averages. Error bars denote
95% confidence intervals.
from the far left to the far right, where valence is computed
only for hashtags produced by manually-annotated users.
If hashtag-based content injection is related to the com-
paratively high levels of cross-ideological communication
observed in the mention network, we expect users who use
hashtags in this way to receive proportionally more men-
tions from users with opposing political views. Using com-
munity identities in the retweet network as a proxy for politi-
cal alignment, we plot in Figure 3 the average proportions of
mentions users receive from and direct toward members of
the other community versus the mean valence of all tags pro-
duced by those users. A key finding of this study, these re-
sults indicate that users contributing to a politically balanced
combination of content streams on average receive and pro-
duce more inter-ideological communication than those who
use mostly partisan hashtags. Moreover, Table 6 shows that
the most popular hashtags do not have neutral valence, rul-
ing out that neutral-valence users are simply using the most
popular hashtags.
5 Conclusions
In this study we have demonstrated that the two major mech-
anisms for public political interaction on Twitter — men-
tions and retweets — induce distinct network topologies.
The retweet network is highly polarized, while the mention
network is not. To explain these observations we highlight
the role of hashtags in exposing users to content they would
not likely choose in advance. Specifically, users who apply
hashtags with neutral or mixed valence are more likely to
engage in communication with opposing communities.
Although our findings could be interpreted as encouraging
evidence of cross-ideological political discourse, we empha-
size that these interactions are almost certainly not a panacea
for the problem of political polarization. While we know
for certain that ideologically-opposed users interact with
one another, either through mentions or content injection,
they very rarely share information from across the divide
with other members of their community. It is possible that
these users are unswayed by opposing arguments and facts,
or that the social pressures that lead to group polarization
are too strong for most users to overcome (Sunstein 2002).
Whatever the case, political segregation, as manifested in the
topology of the retweet network, persists in spite of substan-
tial cross-ideological interaction.
Qualitatively speaking, our experience with this body of
data suggests that the content of political discourse on Twit-
ter remains highly partisan. Many messages contain senti-
ments more extreme than you would expect to encounter in
face-to-face interactions, and the content is frequently dis-
paraging of the identities and views associated with users
across the partisan divide. If Yardi and boyd (2010) are cor-
rect, and our experience suggests this may be the case, these
interactions might actually serve to exacerbate the problem
of polarization by reinforcing pre-existing political biases.
Further study of the content of inter-ideological communi-
cation, including sentiment analysis, as well as studies of
network topology that include the follower network, could
help to illuminate this issue.
The fractured nature of political discourse seems to be
worsening, and understanding the social and technological
dynamics underlying this trend will be essential to atten-
uating its effect on the public sphere. We have released a
public dataset based on the information accumulated dur-
ing the course of this study, in hopes that it will help others
explore the role of technologically-mediated political inter-
action in deliberative democracy. The dataset is available at
cnets.indiana.edu/groups/nan/truthy.
Acknowledgments
We are grateful to A. Vespignani, J. Bollen, T. Metaxas and
E. Mustafaraj for helpful discussions, A. Ratkiewicz for pro-
viding annotations to help evaluate our qualitative content
analysis methodology, and S. Patil for assistance in the de-
velopment of the Truthy website. We acknowledge 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 In-
formatics and Computing.
A Modularity Distributions
In §3.1 we computed the z-score of our modularity values with re-
spect to the distribution of modularity values that arise from clus-
tering degree-preserving shuffles of the original graph. We used
these z-scores to argue that the retweet network is significantly
more segregated than the mention network. However, this argu-
ment relied on the assumption that the modularities are normally
distributed. By testing the sampled modularities with the omnibus
K2statistic (D’Agostino 1971), we find that this assumption is not
true: we have p < 1010 that variations between the observed data
and the best-fit normal distribution are due to random chance. This
is the case for the modularities sampled based on both the retweet
and mention networks.
Fortunately we can reach the same statistical conclusion without
relying on the assumption of normality. Let us use Chebyshev’s
inequality:
P(|Xµ| ≥ ) = P(|z| ≥ k)1
k2,(5)
which gives us a very conservative bound on the probability that the
random variable X, being the modularity of a sampled graph, will
take on the value of the original graph’s modularity. We therefore
compute zmand zrfor the modularities of clusters in the 1,000
shuffled versions of each of the the mention and retweet networks,
respectively. Using Equation 5 and the modularities of the original
graph clusters, we find:
P(zzm)1
z2
m
= 0.24 (6)
P(zzr)1
z2
r
= 0.008 (7)
for zm= 2.06 and zr= 11.02 (§3.1). Thus, since a network that
can be clustered as well as the retweet network is much less likely
to arise randomly (relative to the mention network), we confirm
that the retweet network is much more segregated.
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