- Regular article
- Open Access
Partisan asymmetries in online political activity
© Conover et al.; licensee Springer. 2012
- Received: 20 January 2012
- Accepted: 4 May 2012
- Published: 18 June 2012
We examine partisan differences in the behavior, communication patterns and social interactions of more than 18,000 politically-active Twitter users to produce evidence that points to changing levels of partisan engagement with the American online political landscape. Analysis of a network defined by the communication activity of these users in proximity to the 2010 midterm congressional elections reveals a highly segregated, well clustered, partisan community structure. Using cluster membership as a high-fidelity (87% accuracy) proxy for political affiliation, we characterize a wide range of differences in the behavior, communication and social connectivity of left- and right-leaning Twitter users. We find that in contrast to the online political dynamics of the 2008 campaign, right-leaning Twitter users exhibit greater levels of political activity, a more tightly interconnected social structure, and a communication network topology that facilitates the rapid and broad dissemination of political information.
- Political Communication
- Qualitative Content Analysis
- Twitter User
- Large Connected Component
- Political Information
Digitally-mediated communication has become an integral part of the American political landscape, providing citizens access to an unprecedented wealth of information and organizational resources for political activity. So pervasive is the influence of digital communication on the political process that almost one quarter (24%) of American adults got the majority of their news about the 2010 midterm congressional elections from online sources, a figure that has increased three-fold since the Pew Research Center began monitoring the statistic during the 2002 campaign . Relax the constraint that a majority of a person’s political news and information must come from online sources and the figure jumps to include the 54% of adult Americans who went online in 2010 to get political information. Critically, this activity precipitates tangible changes in the beliefs and behaviors of voters, with 35% of Internet users who voted in 2010 reporting that political information they saw or read online made them decide to vote for or against a particular candidate .
Within this ecosystem of digital information resources, social media platforms play an especially important role in facilitating the spread of information by connecting and giving voice to the voting public [2–4]. Networked and unmoderated, social media are characterized by the large-scale creation and exchange of user-generated content , a production and consumption model that stands in stark contrast to the centralized editorial and distribution processes typical of traditional media outlets [6, 7].
In terms of political organization and engagement, the benefits of social media use are many. For voters, social media make it easier to share political information, draw attention to ideological issues, and facilitate the formation of advocacy groups with low barriers to entry and participation [8, 9]. The ease with which individual voters can connect with one another directly also makes it easier to aggregate small-scale acts, as in the case of online petitions, fundraising, or web-based phone banking . Together, these features contribute to the widespread use of social media for political purposes among the voting public, with as many as 21% of online adults using social networking sites to engage with the 2010 congressional midterm elections . Moreover, a survey by the Pew Internet and American Life Project finds that online political activity is correlated with more traditional forms of political participation, with individuals who use blogs or social networking sites as a vehicle for civic engagement being more likely to join a political or civic group, compared to other Internet users .
Likewise, candidates and traditional political organizations benefit from a constituency that is actively engaged with social media, finding it easier to raise money, organize volunteers and communicate directly with voters who use social media platforms . Social media also facilitate the rapid dissemination of political frames, making it easy for key talking points to be communicated directly to a large number of constituents, rather than having to subject messages to the traditional media filter.
Considered in this light, it becomes clear why social media were argued to have played such an important role in the political success of the Democratic party in the 2008 presidential and congressional elections [14–16]. Survey data from the Pew Research Center showed that, along the seven dimensions used to measure online political activity, Obama voters were substantially more likely to use the Internet as an outlet for political activity . In particular, Obama voters were more likely than McCain voters to create and share political content, and to engage politically on an online social network . Moreover, a 2009 Edelman report found that in addition to a thirteen million member e-mail list, the Obama campaign enjoyed twice as much web traffic, had four times as many YouTube viewers and five times more Facebook friends compared to the McCain campaign . While the direct effect of any one media strategy on the success of a campaign is difficult to assess and quantify, the data show that Obama campaign had a clear advantage in terms of online voter engagement.
Motivated by the connection between the widely reported advantage in on-line mobilization and the result of the 2008 presidential election, we seek to understand structural shifts in the American political landscape with respect to partisan asymmetries in online political engagement. We work toward this goal by examining partisan differences in the behavior, communication patterns and social interactions of more than 18,000 politically-active users of Twitter, a social networking platform that allows individuals to create and share brief 140-character messages. Among all social media services, Twitter makes an appealing analytical target for a number of reasons: the public nature of its content, the accessibility of the data through APIs, a strong focus on news and information sharing, and its prominence as a platform for political discourse in America and abroad [18, 19]. These features make a compelling case for using this platform to study partisan political activity.
For this analysis we build on the findings of a previous study which established the macroscopic structure of US domestic political communication on Twitter. In that work we employed clustering techniques and qualitative content analysis to demonstrate that the network of political retweets exhibits a highly segregated, partisan structure . Despite this segregation, we found that politically left- and right-leaning individuals engage in interaction across the partisan divide using mentions, a behavior strongly correlated with a type of cross-ideological provocation we term ‘content injection.’
Having established the large-scale structure of these communication networks, in this study we employ a variety of methods to provide a more detailed picture of domestic political communication on Twitter. We characterize a wide range of differences in the behavior, communication, geography and social connectivity of thousands of politically left- and right-leaning users. Specifically, we demonstrate that right-leaning Twitter users exhibit greater levels of political activity, tighter social bonds, and a communication network topology that facilitates the rapid and broad dissemination of political information, a finding that stands in stark contrast to the online political dynamics of the 2008 campaign.
With respect to individual-level behaviors, we find that right-leaning Twitter users produce more than 50% more total political content and devote a greater proportion of their time to political discourse. Right-leaning users are also more likely to use hyperlinks to share and refer to external content, and are almost twice as likely than left-leaning users to self-identify their political alignment in their profile biographies. At the individual level, these behavioral factors paint a picture of a right-leaning constituency comprised of highly-active, politically-engaged social media users, a trend we see reflected in the communication and social networks in which these individuals participate.
Regarding connectivity patterns among users in these two communities we report findings related to three different networks, described by the set of explicitly declared follower/followee relationships, mentions, and retweets. Casting the declared follower network as the social substrate over which political information is most likely to spread, we find that right-leaning users exhibit a greater propensity for mutually-affirmed social ties, and that right-leaning users tend to form connections with a greater number of individuals in total compared to those on the left. With respect to the way in which information actually propagates over this substrate in the form of retweets, right-leaning users enjoy a network structure that is more likely to facilitate the rapid and broad dissemination of political information. Additionally, right-leaning users exhibit a higher probability to rebroadcast content from and to be rebroadcast by a large number of users, and are more likely to be members of high-order retweet network k-cores and k-cliques, structural features that are associated with the efficient spreading of information and adoption of political behavior and opinions. Pointing definitively to a vocal, socially engaged, densely interconnected constituency of right-leaning users, these topological and behavioral features provide a significantly more nuanced perspective on political communication on this important social media platform. Moreover, through its use of digital trace data to illuminate a complex sociological phenomenon, this article illustrates the explanatory power of data science techniques and underscores the potential of this burgeoning scientific epistemology.
2.1 The Twitter platform
Twitter is a popular social networking and microblogging site where users can post 140-character messages containing text and hyperlinks, called tweets, and interact with one another in a variety of ways. In the present section we describe four of the platform’s key features: follow relationships, retweets, mentions, and hashtags.
Twitter allows each user to broadcast tweets to an audience of users who have elected to subscribe to the stream of content he or she produces. The act of subscribing to a user’s tweets is known as following, and represents a directed, non-reciprocal social link between two users. From a content consumption perspective, each user can sample tweets from a variety of content streams, including the stream of tweets produced by the users he or she follows, as well as the set of tweets containing specific keywords known as hashtags.
A hashtag is a tokens prepended with a pound sign (e.g., #token) which, when displayed, functions as a hyperlink to the stream of recent tweets containing the specified tag . While they can be used to specify the topic of a tweet (e.g., #oil or #taxes), when used in political communication hashtags are commonly employed to identify one or more intended audiences, as in the case of the most popular political hashtags, #tcot and #p2, acronyms for ‘Top Conservative on Twitter’ and ‘Progressives 2.0,’ respectively. In this way, hashtags function to broaden the audience of a tweet, extending its visibility beyond a person’s immediate followers to include all users who seek out content associated with the tag’s topic or audience. For this reason, as outlined in Section 3.1, we restrict our analysis to the set of tweets containing political hashtags, ensuring that the content under study is broadly public and expressly political in nature.
In addition to broadcasting tweets to the public at large, Twitter users can interact directly with one another in two primary ways: retweets and mentions. Retweets often act as a form of endorsement, allowing individuals to rebroadcast content generated by other users, thus raising the content’s visibility . Mentions allow someone to address a specific user directly through the public feed, or, to a lesser extent, refer to an individual in the third person. In this study, we differentiate between mentions that occur in the body of the tweet and those that occur at the beginning of a tweet, as they correspond to distinct modes of interaction. Mentions located at the beginning of a tweet are known as ‘replies’, and typically represent actual engagement, while mentions in the body of a tweet typically constitute a third-person reference . Together, retweets and mentions act as the primary mechanisms for explicit, public user-user interaction on Twitter.
The analysis described in this article relies on data collected from the Twitter ‘gardenhose’ streaming APIa between September 1st and January 7th, 2011 - the eighteen week period surrounding the November 4th United States congressional midterm elections. The gardenhose provides a sample of approximately 10% of the entire Twitter corpus in a machine-readable format. Each tweet entry is composed of several fields, including a unique identifier, the content of the tweet (including hashtags and hyperlinks), the time it was produced, the username of the account that produced the tweet, and in the case of retweets or mentions, the account names of the other users associated with the tweet.
From this eighteen week period we collected data on 6,747 right-leaning users and 10,741 left-leaning users, responsible for producing a total of 1,390,528 and 2,420,370 tweets, respectively. It’s useful to note that we evaluate all gardenhose tweets associated with each user, rather than just those containing political hashtags, in order to facilitate comparisons between the two groups in terms of relative proportions of attention allocated to political communication.
In order to examine differences in the behavior and connectivity of left- and right-leaning Twitter users we rely on the political hashtags and partisan cluster membership labels established in a previous study on political polarization. In addition to reviewing the approach used to establish these features, we show that the networks and communities under study are representative of domestic political communication on Twitter in general.
3.1 Identifying political content
As outlined in Section 2.1, hashtags are used to specify the topic or intended audience of a tweet, and allow a user to engage a much larger potential audience than just his or her immediate followers. We define the set of pertinent political communication as any tweet containing at least one political hashtag. While an individual can engage in political communication without including a hashtag, the potential audience for such content is limited primarily to his or her immediate followers. Moreover, restricting our analysis to tweets which have been expressly identified as political in nature allows us to define a high-fidelity corpus, avoiding the risk of introducing undue noise through the use of topic detection strategies [24, 25].
Political hashtags related to #p2 and #tcot (acronyms for ‘Progressives 2.0’ and ‘Top Conservatives on Twitter’)
#casen #dadt #dc10210 #democrats #du1 #fem2 #gotv #kysen #lgf #ofa #onenation #p2b #pledge #rebelleft #truthout #vote #vote2010 #whyimvotingdemocrat #youcut
#cspj #dem #dems #desen #gop #hcr #nvsen #obama #ocra #p2 #p21 #phnm #politics #sgp #tcot #teaparty #tlot #topprog #tpp #twisters #votedem
#912 #ampat #ftrs #glennbeck #hhrs #iamthemob #ma04 #mapoli #palin #palin12 #spwbt #tsot #tweetcongress #ucot #wethepeople
Hashtags excluded from the analysis due to ambiguous or overly broad meaning
Excluded from #p2
#economy #gay #glbt #us #wc #lgbt
Excluded from both
Excluded from #tcot
#news #qsn #politicalhumor
To further support the claim that sampling based on this set of hashtags produces a representative set of political tweets, we selected all the tweets in the gardenhose from the study period that included any one of 2,500 hand-selected political keywords related to the 2010 elections . We considered only the 312,560 tweets in this set containing a hashtag because we use this characteristic to define public political communication on Twitter. We found that 26.4% of these tweets are covered by our target set of hashtags. Furthermore, among the ten most popular hashtags not included in our target set (#2010memories #2010disappointments #ff #p2000 #2010 #business #uk #newsjp #asia #sports), only one is explicitly political and its volume accounts for less than 2% of public political communication. This coverage confirms that we have isolated a substantial and representative sample of political communication on Twitter.
3.3 Inferring political identities from communication networks
In a previous study we used the set of political tweets from the six weeks preceding the 2010 midterm election to build a network representing political retweet interactions among Twitter users. In this network an edge runs from a node representing user A to a node representing user B if B retweets content originally broadcast by A, indicating that information has propagated from A to B. This network consists of 23,766 non-isolate nodes among a total of 45,365, with 18,470 nodes in its largest connected component and 102 nodes in the next-largest component. We describe the construction of an analogous network of political mentions in Section 5.3.
To establish the reproducibility of these results we had two authors, working independently, determine whether the content of a user’s tweets express a ‘left,’ ‘right’ or ‘undecidable’ political identity according to the coding rubric developed in a previous study . These annotations were compared against the work of an independent non-author judge, and using a well-established measure of inter-annotator agreement we report ‘nearly perfect’ inter-annotator agreement between author and non-author annotations for the ‘left’ and ‘right’ classes (Cohen’s Kappa values of 0.80 and 0.82, respectively) and ‘fair to moderate’ agreement for the ‘undecidable’ category (Cohen’s Kappa value of 0.42) [29, 30]. From these high levels of inter-annotator agreement we conclude that an objective outside party would be able to reproduce our class assignments for most users.
Partisan composition of retweet cluster communities as determined through manual annotation of random users. (See Section 3.3 )
In the following sections we leverage these data to explore, in detail, how users from the political left and right utilize this important social media platform for political activity in different ways.
Before examining structural differences in the social and communication networks of left- and right-leaning Twitter users, we first focus on political activity at the individual level. In this section we compare users in the left- and right-leaning communities in terms of their relative rates of content production, the amount of attention they allot to political communication, their respective rates of political self-identification, and their propensity for sharing information resources in the form of hyperlinks.
Right-leaning users are substantially more active and politically engaged with this social media platform. Specifically, our analysis shows that left-leaning users produce less total political content, allocate proportionally less time to creating political content, are less likely to reveal their political ideology in their profile biography, and are less likely to share resources in the form of hyperlinks. All of these findings stand in stark contrast to survey data and media reportage of the 2008 online political dynamics, and provide evidence in support of the notion that right-leaning voters are becoming more politically engaged online.
4.1 Political communication
4.2 Partisan self-identification
In addition to devoting a larger proportion of tweets to political content, right-leaning users are much more likely to use their 140-character profile ‘biography’ to explicitly self-identify their political alignment. A survey of the biographies of 400 random users from the set of individuals selected for qualitative content analysis (Section 3.3) reveals that 38.7% of right-leaning users included reference to their political alignment in this valuable space, as compared with only 24.6% of users in the left-leaning community. Taken together, this analysis demonstrates that right-leaning users are much more likely to use Twitter as an outlet for political communication, and are substantially more inclined to view the Twitter platform as an explicitly political space.
4.3 Resource sharing
One of the key functions of the Twitter platform is to serve as a medium for sharing information in the form of hyperlinks to external content . Given the constraints of the 140-character format, hyperlinking activity is especially important to the dissemination of detailed political information among members of a constituency.
With respect to this aspect of online political engagement, too, we see that right-leaning users are more active then those individuals in the left-leaning community. Among all tweets produced by users in the right-leaning community, 43.4% contained a hyperlink, compared with 36.5% of all tweets from left-leaning users. This trend is even more pronounced if we consider only resource sharing within the set of political tweets, with left-leaning users including a hyperlink in 50.8% of political tweets, as compared to right-leaning users, who include hyperlinks 62.5% of the time. From these observations we conclude that right-leaning users are more inclined to treat Twitter as a platform for aggregating and sharing links to web-based resources, an activity crucial to the efficient spread of political information on the Twitter platform.
Next, we turn our attention to structural differences in social interaction and communication networks of left- and right-leaning users.
5.1 Follower network
Follower network statistics for the subgraphs induced by the set of edges among users of the same political affiliation
These observations lead us to conclude that there are substantial structural differences in the fundamental patterns of social connectivity among politically left- and right-leaning Twitter users, a finding supported by the seminal work of Adamic and Glance  on the connectivity patterns of high-profile partisan bloggers. Specifically, the right-leaning community is much more densely interconnected, with more users tightly integrated into the right-leaning social network. In contrast, the network of follower/followee relations among left-leaning users exhibits a much more decentralized, loosely-interconnected structure, with far fewer mutually-affirmed social connections.
5.2 Retweet network
Retweet network statistics for the subgraphs induced by the set of edges among users of the same political affiliation
We also find that a substantially higher proportion of right-leaning user participate in fully-connected subgraphs of size k, known as k-cliques. This result is especially important in the context of the complex contagion hypothesis, which posits that repeated exposures to controversial behaviors are essential to the adoption of these behaviors. Work by Romero, Meeder and Kleinberg focused specifically on online social networks indicates that this effect is particularly pronounced for political discourse on Twitter . With fewer users in high-order k-cores, individuals in the left-leaning community will be less likely to encounter multiple users discussing the same partisan talking points or calls to action, exactly the kind of contentious content whose propagation is most likely to benefit from repeated exposure.
5.3 Mention network
Mention network statistics for the subgraphs induced by the set of edges among users of the same political affiliation
In addition to characterizing differences in behavior and connectivity, we can also examine the geographic distribution of individuals in these two communities. Here we present a cartogram in which the color of each state has been scaled to correspond to the degree to which, in that state, the observed number of tweets originating from the left-leaning community exceeds what we should expect by chance alone.
Because fewer than one percent of Twitter users provide precise geolocation data, we instead rely on the self-declared ‘location’ field of each user’s profile to enable geographic analysis of data at the scale of this study. As a free-text field, users are able to enter in arbitrary data, and non-location responses such as ‘the moon’ do appear in the results. Complicating this analysis further, some users do not report any location data, though we do not report a partisan bias in terms of non-entries. Despite these caveats, a large number of users do report actual locations, and using the Yahoo Maps Web Service API,b we are able to make a best-guess estimate about the state with which a user most strongly identifies.
Initial inspection of this figure reveals that the geographic distribution of individuals from the left-leaning network community corresponds strongly to the traditional political geography of the United States. We see that left-leaning individuals feature prominently on the coasts and North East, and tend to be underrepresented in the midwest and plains states.
Looking more closely, however, we find that there are some places in which the partisan makeup of tweets is quite different from what might be hypothesized intuitively. For example, Utah, a traditionally conservative state which at the time of this writing had two Republican senators, exhibits a dramatically higher volume of left-leaning content than should be expected by chance alone. One possible explanation for this observation could be that individuals in some states with an ideologically homogeneous population turn to social media as an outlet for political expression. While this is but one possible explanation among many, and a more rigorous analysis is required to support any definitive claim, this example illustrates the ways in which novel hypotheses can derive from data-driven analyses of political and sociological phenomena.
In this study we have described a series of techniques and analyses that indicate a shifting landscape with respect to partisan asymmetries in online political engagement. We find that, in contrast to what might be expected given the online political dynamics of the 2008 campaign, right-leaning Twitter users exhibit greater levels of political activity, tighter social bonds, and a communication network topology that facilitates the rapid and broad dissemination of political information.
In terms of individual behavior, politically right-leaning Twitter users not only produce more political content and devote a greater proportion of their time to political discourse, but are also more likely to view the Twitter platform as an explicitly political space and identify their political leanings in their profiles. With respect to social interactions, the right-leaning community exhibits a higher proportion of reciprocal social and mention relationships, are more likely to rebroadcast content from a large number of sources, and are more likely to be members of high-order retweet network k-cores and k-cliques. Such structural features are directly associated with the efficient spreading of information and adoption of political behavior. Taken together, these features are indicative of a highly-active, densely-interconnected constituency of right-leaning users using this important social media platform to further their political views.
This study is characteristic of an emerging mode of inquiry in the political and social sciences, whereby large-scale behavioral data are aggregated and analyzed to shed quantitative light on questions whose scale was previously considered outside the realm of tractable analysis . Using structural features of a digital communication network one can make high-fidelity inferences about the political identities of thousands of individuals. Such data provide a deeper understanding of the changing landscape of American online political activity. Looking forward, techniques such as these are likely to become increasingly important as the political and social sciences rely in greater measure on large-scale digital trace data describing human opinion and behavior.
MDC and BG collected the data and performed the analysis. MDC, BG, AF and FM conceived the experiments and wrote the manuscript.
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