Candidate Interactivity and Mobilization Messages on Twitter in 2010

David S. Lassen

University of Wisconsin, Madison

Leticia Bode

Georgetown University

October 28, 2014

Candidates for Congress are thoroughly strategic actors. Whether their primary goal is to gain office (Mayhew 1974), influence public policy (Fenno 1978), or affect the national discussion of an issue (Boatright 2006), members of and candidates for Congress are goal driven, benefit maximizers. It is then only somewhat surprising that they have often also been resistant to new technology.[footnoteRef:2] Though innovations often offer distinct benefits such as the ability to contact more voters, many candidates, recognizing the small number of elections they will likely stand for, are loathe to vary from previous patterns of successful campaigns. As recent as 1997, for example, one Senator bemoaned that many of his colleagues “wouldn’t even know how to turn on a computer if they had to. They think it’s a not-working television that won’t give you CNN” (Johnson 2004, 62). In 2008 John McCain confided that he was still “learning to get online” by himself (Nagourney and Weaver 2008). Yet as Twitter and other social media become fixtures in American popular culture, candidates for Congress have increasingly adopted the services themselves. [2: In 1869 a young Thomas Edison presented Congress with an original electronic automatic vote counting machine. He was swiftly told “young man, that is just what we do not want” (Davis 2012). Nearly 150 years later, the Senate continues to record votes with paper and pencil.]

The manner in which candidates have incorporated Twitter into their existing communication strategies, however, remains an unresolved, evolving topic of inquiry. While a growing number of studies have considered the rate and timing of candidate tweeting, for example, only a minority has examined the actual language distributed in this manner. Yet the structure of tweets as brief, networked, online messages delivered primarily to subscribers, suggests that they may differ from traditional campaign communications in important ways not captured by patterns of publication alone. In this paper we consider the prevalence of two democratically meaningful types of messages: appeals for action (especially those that may be immediately acted on) and candidate-voter interaction.

To be sure, neither of these types of content is necessarily unique to Twitter posts, yet the specific affordances of the service suggest that they may be deployed in a meaningfully distinct manner in tweets. By examining the specific content of more than 10,000 tweets posted by candidates for the United States Senate during a telling period of Twitter development among political elites—2010—we are able to identify not only patterns of candidate Twitter production, but the manner in which campaigns first began to utilize a tweet’s capacity to engage and inform potential supporters. We find that while many candidates regularly used Twitter in 2010 to achieve these democratically important acts by both making appeals to and—to a lesser extent—interacting with their supporters, these messages were often devoid of context and justification. This has direct implications for the democratic value of congressional Twitter use, implications we explore in our discussion.

Strategic Campaign Messaging

A substantial literature has examined efforts by public office holders to communicate with their constituents, both in the context of campaigns and everyday governance. Classic theories suggest that members of Congress are motivated first by the desire to be reelected (Mayhew 1974) and, as a result, spend the bulk of their time communicating with the public to gain and maintain their support. Members and challengers alike are therefore strategic in their public language, emphasizing policies, affiliations, and work that they anticipate will resonate with key electoral groups. Grimmer (2013), for example, finds that members who are relatively more ideologically out of step with their constituents are more likely to privilege appropriations over policy positions in their public statements when compared with members more aligned with their constituencies. Similarly, in his examination of congressional franked mail, Lipinski (2004) finds that a member of Congress becomes more likely to praise Congress as an institution when her party is in the majority and therefore able to claim credit for national policies. In general, then, sensitivity to prevailing electoral winds can drive candidates to avoid taking public positions on policies (Jones 2003). Even if the consequences of taking a given position are unclear, for many candidates “what counts is the potential damage” (Fiorina (1974), emphasis in original) they may incur.

Similarly, many elites resist direct interaction with voters (Taylor and Kent 2004). Many candidates are wary of allowing even supportive members of the public to shape their message (Stromer-Galley 2000). Those open to the idea of interaction may also have difficulty designing interactive spaces (Williams and Gulati 2012). Member-constituent interaction and responsiveness, however, has long been considered an important element of representation and electoral success (Adler, Gent, and Overmeyer 1998; Serra and Moon 1994, Serra and Cover 1992). Most theories of democracy consider such interaction crucial to an equitable society. A society is most successful when both elites and the public mutually engage and inform one another in the public sphere (Habermas 1962). Barber (1984) contends that democratic societies are strongest and most responsive when citizens are active agents capable of influencing change on a regular basis, change that may be facilitated by direct interaction with candidates for Congress. The elusiveness of elites is therefore troubling.

By contrast, goal-oriented candidates should be likely to produce mobilizing messages. Campaigns for the House and Senate have collectively produced a large number of consistently sophisticated mobilization messages for at least the past 50 years (Abramson, Aldrich, and Rohde 2002; Goldstein and Ridout 2002). A large literature suggests that these messages, especially when delivered in person, can effectively increase participation in a variety of political acts, including voting and campaign fundraising (e.g., Gerber and Green 2000; Nickerson 2007). Existing evidence also suggests that mobilization efforts may be most effective when presented by a trusted source (Bond et al. 2012, Michelson 2003) or in a policy or partisan context that the targeted voter agrees with (Sides and Karch 2008, Wilcox and Sigelman 2001, Panagopoulos 2009). Though these studies find few differences in the relative motivating power of different types of messages, they consistently show that voters respond more favorably to requests from individuals with whom they clearly share an important characteristic or belief. When not immediately apparent to the voter, such similarities can be highlighted in the language of a mobilizing message.

At other times, successful mobilization efforts may not need to establish common footing between the candidate and voter. Dale and Strauss (2009) find that even brief mobilization reminders such as text messages can activate a latent predisposition to act among those who have already signaled a willingness to get involved in the campaign (see also Malhotra et al. (2011)). For these individuals, a reminder that breaks through the everyday demands of life (i.e., is noticeable) may be sufficient to foster increased action. No matter the types of messages used, candidates are likely to learn from each others' relative success rate. Observing the positive electoral outcomes that generally follow campaigns that produce rigorous, sophisticated mobilization efforts, candidates often follow suit and engage in similar efforts as allowed by available resources.

The goal of electoral security therefore suggests that candidates for Congress should highly value direct, inexpensive forms of constituent communication. Free from many of the constraints imposed by the media and party leaders (Highton and Rocca 2005), direct, accessible communication channels such as franked mail or social media offer candidates greater control over the content, volume, and timing of their messages without loss of influence. Existing evidence suggests that direct candidate messages can effectively inform and even persuade voters (Cover and Brumberg 1982, Lipinski 2004). Mere exposure to a candidate’s name (Kam and Zechmeister 2013) or characteristics (Hutchings and Jardina 2009) has been shown to affect individual levels of support. More broadly, public statements from congressional elites also help set the bounds of and definitions used in public policy debates (Bennett 2001), perhaps especially as journalists have embraced social media services and incorporated them into their own work practices (Arceneaux and Weiss 2010; Coddington, Molyneux, and Lawrence 2014).

Many candidates for Congress have therefore begun to use social media tools such as Twitter in their efforts to communicate with constituents. Members of Congress were among the earliest tweeters, posting messages less than 18 months after the service’s introduction (Lassen and Brown 2011). By 2012, Twitter use was nearly ubiquitous among candidates for Congress, with more than 90 percent of major party candidates maintaining at least one account.[footnoteRef:3] Collectively, candidates in 2011 and 2012 published nearly 500,000 tweets (Toff and Lassen 2014), many either disseminating information about the candidate author or directing the reader to longer form content on a candidate’s or news organization’s website (Golbeck, Grimes, and Rogers 2010; Parmalee and Bichard 2012; Evans et al. 2014). [3: In order to comply with institutional regulations concerning the dissemination of campaign material using official resources, most incumbents maintain at least two handles—one for official messages and another for campaign information.]

Prior to 2011, however, elite adoption and use of Twitter was often limited. Seasoned incumbents hesitated to revise their tested methods of constituent interaction (see Stromer-Galley (2000) and Evans and Oleszek (2003) for more on this topic). Instead, young members, new to the chamber and still developing their congressional identity, largely drove adoption (Lassen and Brown 2011). By the midterm election season of 2010, congressional Twitter use was becoming more common (especially among members of the Senate) and even encouraged by some party leaders (see Lassen and Brown 2011), but was by no means an expected feature of a serious campaign. Because of the developing but still limited popularity of the service at the time, 2010 presents a potentially useful window into strategic congressional adoption of the new technology. By 2010 Twitter was neither an unknown novelty, reserved for use by candidates with a particular personal proclivity for new technology, nor was it yet a standard tool fully integrated into common campaign practices.[footnoteRef:4] Candidates in the 2010 election, strategic actors aware of Twitter but under little obligation to use it, represent a useful data source for understanding elite use of a new communication tool. We therefore focus our analyses on this period. [4: For a more detailed treatment of the now systemic integration of Twitter into the ecosystem of political campaigns, see Toff and Lassen (2014).]

Motivations for Twitter Use

We also contend that examinations of elite Twitter use are best when grounded in explorations of theoretically relevant concepts and thereby do more than describe social media for social media’s sake. Our paper therefore makes a second contribution by presenting a more theory driven research design and analysis. Many existing studies of elite Twitter use adopt some combination of an archival (data collection) or inductive (data classification) approach. In the former, congressional tweets are treated as an interesting set of data points that are collected and (often roughly) categorized (e.g., Golbeck, Grimes, and Rogers 2011; Livne, Simmons, and Adamic 2011). Hemphill, Otterbacher, and Shapiro (2013), for example, contend that members of Congress most often use Twitter to direct information at voters and not to appeal for support or recognize the actions of others. At least two major concerns exist with this study and others like it. First, the authors sample from a curious time period (August 2011 to February 2012) that encompasses little of theoretical interest (e.g., a general election). Instead, the sample appears to be one of convenience. Second, the authors’ coding scheme is inductive in nature, primarily striving only to create a small number of categories in which the highest proportion of tweets may be placed, regardless of each category’s ability to inform existing theories of congressional behavior. Other studies collect a more focused sample of tweets, but still frequently center on inductive coding schemes in an effort to give as broad an overview as possible of congressional messaging (e.g., Evans et al. 2014). Such an approach may tell us little if the resultant categories do not align with concepts in existing literature.

This is not to say, of course, that existing efforts have been fruitless. Indeed, studies of the kinds described above have laid the groundwork for important intuition methodologically and theoretically. Their results suggest that candidate tweeting is susceptible to many familiar electoral influences. Candidates in competitive races, for example, may have become more likely to tweet overall (Evans et al. 2014, Amman 2010) and to criticize an opponent (Haber 2011). Similarly, candidate Twitter use appears to vary by the party (Livne, Simmons, Adar, and Adamic 2011), majority status (Lassen and Brown 2011), and incumbency (Glassman, Straus, and Shogan 2010) of the author. Comparing the Twitter adoption and behavior of Dutch and British elites, Graham, Jackson, and Broersma (2014) conclude that the structure and influence of the local party system can also significantly shape new media use.

Still, candidate Twitter feeds may represent a significantly new form of content that varies from and combines existing communication streams. Indeed, the networked nature of Twitter allows the creation of unique hierarchies and paths of influence in political communication (Maireder and Ausserhofer 2013, Freelon and Karpf 2014). The timing of and language used in candidate tweets also appears to vary from that of traditional messaging efforts (Bode et al. 2011) and rhetoric (Mirer and Bode 2013). Tweets are unique from other forms of campaign communication by being simultaneously highly brief, essentially costless, networked, and quasi-public (they are seen first by followers but are generally accessible to anyone). Yet the earliest congressional tweets were often simple extensions of existing communication efforts that did not account for Twitter's unique structure (Williams and Gulati 2012). This reminds us that while the barriers to producing new media content are quite low, effectively utilizing social media may require relevantly trained consultants or staff and a willingness to break with traditional campaign practices.

Anderson and Sheeler (2014) describe the type of new media content candidates might produce when they are ready to break from convention. Tracking online comments surrounding Hillary Clinton's unsuccessful bid for the presidency in 2008, these authors argue that tweets can help shape a candidate's "mediated public identity." Twitter offers an ideally interactive space in which both candidates and their supporters together generate and negotiate the "hyperreal amalgamation of image fragments" that constitute mediated identity: the voting public's perception of the candidate. In other words, these authors contend that new media such as Twitter are uniquely suited to allow candidates to directly interact with and appeal to their supporters for assistance.

Research Questions

We therefore propose a more theoretically grounded examination of strategic Twitter use among congressional candidates. We begin, as described more fully below, by coding for a variety of political statements and references to political actors and organizations in candidate tweets. Most importantly, we identify messages that seek to explicitly invite the reader to engage in one or more political acts as well as those that directly engage the reader by creating a conversation between him or her and the candidate. In doing so we focus on identifying key characteristics potentially associated with interactive and mobilizing messages that happen to be distributed in tweets. We therefore ultimately code a relatively large portion of tweets only for the absence of certain content types.

These codes allow us to address three research questions.

RQ1: How frequently do congressional candidates directly engage their audience on Twitter through direct interaction or appeals for support?

RQ2: What other types of political content do candidates include in their interactive and mobilization tweets?

RQ3: What strategies motivate candidate interactivity and mobilization on Twitter?

Data

To consider these questions, we examined the Twitter behavior of candidates for the United States Senate in 2010. We collected every tweet posted by a major party candidate from January 1, 2009 until election day 2010. We identified candidates with active Twitter accounts through a variety of trusted sources and only included an account when we could visit it and verify that the candidate or her campaign officially operated it, thus excluding accounts associated with a party organization, interest group, or media organization. This resulted in an archive of 10,398 tweets from 71 candidates for Senate.[footnoteRef:5] Over the course of the 2010 election, we archived 10,398 tweets from these Senate candidates. In the first set of models we present our unit of analysis is a single tweet; in the second set our unit of analysis is a single candidate. [5: Two candidates did not have active Twitter accounts during the campaign, and two accounts were deleted before our collection and are therefore not included in our analysis.]

Coding

In addition to the frequency of tweets posted, five features of the language of each tweet are also relevant for an analysis of candidate-voter interaction: explicit appeals for reader action (including appeals that could be immediately acted on), opportunities for direct reader interaction, issue-specific content, references to election-specific events or concepts, and references to general political organizations or concepts. Four trained coders (including the authors) utilized a database coding system to view and classify each tweet according to these and other characteristics. Intercoder reliability was high, with an average Krippendorff’s alpha of 0.65 across all coded elements and coders. As a final check, the authors also reviewed and moderately modified the definition and assignment of all codes given to each tweet identified as an appeal for reader action. The resulting dataset, used for all analyses presented here, therefore uses the following definitions for key tweet characteristics, each coded with a binary measure of the presence of each feature.[footnoteRef:6] [6: The complete codebook is available upon request.]

First, we coded all language directly inviting the reader to engage in political behavior in support of one or more candidates as an explicit appeal for reader action. Tweets that merely evaluated a political actor or organization, discussed one or more candidates’ relative likelihood of election, or declared that the voters’ will and behavior defined the outcome of a campaign were not coded as including an appeal for action. Instead, appeal tweets included a specific request for the voter to vote, donate money, read or watch another message, or engage in some type of candidate-centered election activity—even if the specific action encouraged was ambiguous. Many tweets included appeals of more than one type, calling for readers to vote and read a recent news report on the campaign, for example, while others suggested only one action. In an attempt to capture all appeals, we also coded requests for action that directly assisted a candidate other than the author of the tweet.

As a subset of appeal tweets we also coded tweets that invited direct reader action that could be completed immediately online. These actions included those that could be completed by posting a tweet, following a link provided in a tweet, or visiting another website. We refer to these as actionable appeals. We considered actionable appeals to be time sensitive and therefore only used the code for appeals that were actionable at the time the tweet was posted. Tweets that invited action that could only be accomplished at a future time—such as following a campaign’s live tweeting of an upcoming debate—were not considered actionable though they it would be possible to complete them online at another time.

Second, we identified all tweets that included opportunities for direct reader interaction with the campaign or candidate.[footnoteRef:7] We consider a tweet to be interactive if it includes either a request for the reader to contact the campaign to express an opinion or if the tweet rebroadcasts language originally composed by a constituent. The latter appears most frequently as a retweet but at times may also be included as an anecdote in an original tweet. Other scholars have labeled this type of language as “human-to-human” (Stromer-Galley 2000) or “person” (Novak 1996) interactivity and have distinguished it from media or machine interactivity. We therefore consider a tweet to be interactive if it calls for or facilitates a direct, interpersonal discussion between the reader and the candidate’s campaign. Tweets that merely encourage the reader to visit a website, read a message, or interact with another individual (potentially by an appeal to invite their friends to vote) are not considered interactive in this project. [7: Note that the broadcast nature of Twitter makes it functionally impossible for candidates to restrict such opportunities to only his or her constituents.]

Next, we coded for all issue-specific content. Using the same method employed by the Wisconsin Advertising Project[footnoteRef:8] to code issue content in candidate television ads, coders identified the presence of any of 60 different issues in each tweet, later collapsing these codes into six broad categories: economic issues (kappa=.77), social issues (kappa=.70), law and order (kappa=.69), environment and energy (kappa=.66), social welfare (kappa=.87), and foreign affairs and defense (kappa=.87). Because some of these categories were somewhat resistant to reliable coding, in each of the analyses presented here we collapsed all mentions of one or more issues into a single, dichotomous measure identifying the presence or absence of issue-specific language in a given tweet. [8: For more information on the project’s origins please visit http://wiscadproject.wisc.edu/. To learn about the current version of the project housed at Wesleyan University, please visit http://mediaproject.wesleyan.edu/. ]

Finally, we coded for references to individuals, events, and concepts there were either election-specific or generally political. Election-specific content includes horse race language publicizing polling results, endorsements, and funding totals as well as election media,[footnoteRef:9] events, and the author’s opponent. Other content categorized as generally political includes references to a political party, ideological language such as “conservative” or “liberal”, and mention of a political elite that is neither the candidate nor his opponent. Together, these codes identify the presence of a variety of traditional types of political content we may expect to see in campaign messages using other media. [9: The definition of “election media” has become increasingly ambiguous in recent years as online sources of both amateur and professional political news and analysis continue to proliferate. In order to maintain tractability, we coded only references to significant, professional news organizations as media references. This includes some online sources such as the Huffington Post, but excludes many bloggers that some might consider news sources.]

To this data we added a number of measures of candidate and race-level characteristics. This context data includes candidate demographics, party identification, and incumbency status as well as each candidate’s total funds raised and votes received in the 2010 general election. Because our analyses focus on the language used in the tweets, these data are used primarily as additional covariates in order to improve the precision of our models’ estimates. The coefficients associated with these variables are often unreported. We also avoid reporting these coefficients to avoid confusion about the true significance of their relationship with the outcomes of interest. As we discuss more fully below, samples of the size we sometimes use (i.e., 10,000 observations or more) can artificially depress p-values and make some relationships appear meaningful when they are not.

Results

Table One presents an overview of several key features of each tweet in our sample. The first column presents the total tweets with each feature as a proportion of total tweets posted during our time frame. The second column presents tweets with the same feature as a proportion of all sampled tweets that included an appeal for action. Note that a single tweet may include one or more of these features and that many did include multiple appeals. The total number of appeal tweets in the sample as a whole was modest, with 27 percent of tweets including at least one appeal. Of these appeals about half were to read or watch some additional information. This information was often part of a media report or original content posted on the candidate’s website. Many appeals also focused on vote mobilization, an expected pattern that has existed for as long as federal officials have used Twitter (e.g., Lassen and Brown 2011). Note also that a significant majority of coded appeals encouraged readers to engage in some kind of online action, as more than 63 percent of appeals were actionable. At the same time, however, few tweets engaged the public directly with less than 10 percent of all tweets including any kind of direct interaction.

[TABLE ONE ABOUT HERE]

To develop a basic intuition of the language used in appeal tweets, we next ran a series of bivariate t tests to compare the relative frequency with which coded features appeared in tweets with and without appeals (Table Two). These tests begin to identify the manner in which candidates presented themselves when appealing for individual support. The results immediately suggested that appeals tweets included little more than the appeals themselves. Appeal tweets were significantly less likely than non-appeal tweets to include issue content. More broadly, posts with language urging readers to act most often contained none of the other political content we coded for while this was true for less than 40 percent of non-appeal tweets. The most common content candidates paired with their appeals were references to the media and campaign events, most often mentioned as part of the appeal itself. Collectively, the results in Table Two suggest that facing the limited size of a single tweet, candidates and their staff were hesitant to include any policy or additional political statements in their appeals for support, perhaps assuming that their broader messaging on Twitter and elsewhere would provide adequate context. Thus, appeal tweets were relatively bare and seem to have often lacked information about the candidate, election, or the proposed action itself.

[TABLE TWO ABOUT HERE]

We next constructed a series of multivariate probit regression models to predict the presence of an appeal in each tweet in our sample. The results are presented in Table Three. Moving alphabetically, each new model includes an additional block of theoretically related covariates. While the general pattern of covariate significance changes little with the addition of these variables, the results of likelihood ratio tests indicate that their inclusion does improve the later models’ predictive power. Before discussing the specific results from these models, note too that, given their large sample size, traditional signals of significant relationships should be approached with caution in our initial models. Samples as large as ours have been shown to quickly depress p-values simply because of the sheer number of observations in the data used (Lin, Lucas, and Shmueli 2013). Because of this potential for over-confidence, inferences in the models presented in Table Three and Table Four are best drawn from consistently substantively large results as well as those that fail to achieve conventional significance levels even given the large sample size. As an exploratory check on the results reported in these tables we also re-ran each model using a number of subsamples of 1,000 tweets randomly drawn from the complete dataset. The results of these models corroborate the substantively significant findings from the larger sample. Those variables with consistently large substantive effects in the aggregate models continued to attain standard levels of significance in each subsample while those with smaller significant effects in the aggregate models were only periodically significant in the exploratory samples.

[TABLE THREE ABOUT HERE]

Consistent with the results of the bivariate analysis in Table Two, the results of the models presented in Table Three again suggest that candidates’ appeal tweets in 2010 were focused almost entirely on the appeals themselves. The largest effects in the model are due to the absence of issue content and the presence of associated media and event references. Somewhat surprisingly, this also suggests that Senate campaigns of all partisan and competitive stripes did little in 2010 to encourage or justify their appeals as they made them. Elements that would seem commonly associated with requests for support such as direct reader interaction, references to the author’s opponent, and a variety of other political statements are either entirely insignificant in these models or have a negative coefficient. Instead, appeal tweets are marked by their sparseness.

The results reported in Table Two and Table Three paint a telling picture of candidate use of social media to appeal for voter support, but shed little light on the nature of other types of candidate tweets. We therefore applied the same pattern of covariates used in Model C to candidate tweets with two other types of content: direct interaction and actionable appeals. The results of these models are reported in Table Four. Most of the findings from Table Three appear again, with both interactive and actionable tweets being less likely than other, similar tweets posted in similar races to include issue content and other political statements, although in each case the pattern is less definitive.

[TABLE FOUR ABOUT HERE]

The pattern of results that deviate from those associated with the appeal tweet models is telling as well. While the models in Table Three predict that mentions of both the media and election events are common companions of appeals, the same is not true for tweets with direct reader interaction (where both media and event mentions are negatively signed) or actionable appeals (where only media mentions are positively associated with the outcome of interest). These results provide more compelling evidence that candidates’ tweets were focused on a single purpose and included few words not directly associated with that goal. Tweets that are meant to provide readers with an opportunity to participate in the campaign online, for example, would have little use for details about a live campaign event and therefore are unlikely to include such.

The results presented to this point focus on the language used in individual tweets posted by candidates in 2010. Ultimately, however, they are largely unable to speak to the strategies pursued by individual candidates on social media. In Table Five we therefore present summary measures of Twitter use by candidate. Note that while many candidates only infrequently posted tweets with any given characteristic, at least one candidate routinely published on the subject. Consider tweets with a reference to the author’s opponent. Though 40 of the 71 candidates referenced their opponent (by name or otherwise) in a tweet fewer than 15 times, Harry Reid tweeted about his opponent 419 times[footnoteRef:10] (approximately 66 percent of his total tweets). There is therefore a large amount of variation in candidate usage of each type of tweet both by volume and proportion. [10: Reid’s total was nearly double that of the next most prolific name dropper: Blanche Lincoln who mentioned her opponent 224 times.]

[TABLE FIVE ABOUT HERE]

To examine this variance, we constructed a series of regression models to treat candidates as the unit of analysis. The nature of tweeting as a set of discrete accumulating acts suggests that a count model is most appropriate here. We therefore created several negative binomial regression models[footnoteRef:11] to examine the factors correlated with a relatively greater volume of tweets of each kind. The results of these models are presented in Table Six. In these models we used the same set of covariates as in the models in Table Three and Table Four. Many of the results are consistent with those from our tweet-level models. Candidates who posted a relatively greater number of tweets that referenced either a political party or an elite other than the candidate's opponent posted relatively fewer requests for action. The previous negative relationship between appeals and issue content does not appear in these models, however. Instead, candidates that posted more tweets with issue content were no less likely to also publish appeals for action online or off. [11: The results of an overdispersion test for each model indicate that a negative binomial model is most appropriate in each case.]

[TABLE SIX ABOUT HERE]

Candidates who posted a greater number of interactive tweets, however, were significantly less likely to post issue content. This suggests that candidate attempts at interaction with the public on social media may have been largely superficial. Instead of engaging with voter preferences and desires, candidates avoided issue discussions across their Twitter profile. This may indicate a continued reluctance among candidates to fully engage voters, a decision that would necessitate relinquishing some message control. Instead, candidates were more likely to pair interactive tweets with mentions of their opponent. For some candidates, interactive tweets may have been little more than an alternative form of advertising, a method of highlighting voter sentiment, especially when it ran contrary to their opponent. Strictly speaking, this does not achieve the democratic potential of interactivity. By dragging their heels in this area, candidates also fail to take advantage of the networked nature of Twitter and may undermine their own efforts to stimulate action among supporters.

Discussion

With these results in hand, consider again our research questions.

RQ1: How frequently do congressional candidates directly engage their audience on Twitter through direct interaction or appeals for support?

Our data indicates that candidates for Senate in 2010 engaged their Twitter audience reasonably frequently. Though interactive tweets were rare (less than 10 percent of all tweets posted), nearly a third of posted tweets included at least one appeal for action. By comparison, sampled candidates were less likely to mention a policy issue, political party, their opponent, or the state of the election. While candidates posted a variety of content over the course of the election, a significant portion was designed to directly engage with their audience.

RQ2: What other types of political content do candidates include in their interactive and mobilization tweets?

In short: very little. The models presented in Table Three and Table Four suggest that candidate tweets were single-minded messages that included little additional content. In addition to the appeal itself, mobilization messages, for example, were likely to include only media and event references, information likely connected to many requests for action. Interactive tweets were even less likely to include other coded content, with no evidence of any other content types regularly appearing in tweets that explicitly sought to foster candidate-voter interactivity. Indeed, the coefficients associated with the interactive tweet model in Table Four are consistently negative, nearly across the board.

This has potentially deleterious consequences for the effect of mobilizing messages on Twitter. Though little evidence exists that the specific content of recruitment efforts exerts a significant influence on their success, a number of studies have found that mobilization messages are significantly more effective when presented by a source that the voter trusts or identifies with. Thus, voters respond more positively to appeals made by individuals who share the same racial identity or partisan attachment, a fact that can be reinforced by referencing policies or familiar partisan objects in the mobilizing tweet, thereby reminding readers of the candidate’s trustworthiness.

RQ3: What strategies motivate candidate interactivity and mobilization on Twitter?

This question was most resistant to our empirical approach. Summary statistics presented in Table Five suggest a large degree of heterogeneity among candidates, with large standard errors associated with every coded feature. The findings from our negative binomial regression models, the results of which are presented in Table Six, give some insight. There is evidence that candidates were generally more likely to pursue a one-dimensional approach to Twitter—those that posted more interactive tweets, for example, were less likely to discuss specific issues—though the effect sizes are not large. Note too, however, the relative lack of significance associated with candidate level characteristics. These models predict remarkably few differences among candidates based on—among other things—party, incumbency status, electoral success, and fundraising success. In general, then, our models predict that candidates will engage in interactivity and mobilization efforts on Twitter when they abandon other forms of tweet content.

Conclusion

By 2010, major party candidates for Congress had begun to use Twitter in a variety of democratically meaningful ways. While candidates frequently used their tweets to interact with and appeal to their readers for active support, only rarely did they also discuss other policy and political topics and actors. In this manner, candidates took partial advantage of Twitter’s unique affordances as a costless, networked communication channel. Existing research suggests that sampled candidates’ appeals likely helped increase public support for our sampled candidates, but considering the isolated manner in which any given tweet from a candidate likely appears in followers’ individual feeds, candidates would likely have increased the efficacy of their appeals by reminding readers—perhaps through strategic use of hashtags—of key policy preferences and individual attributes they possess. In general, then, our results indicate that by 2010 candidates for Senate were using Twitter in sophisticated, if still developing, ways that went beyond traditional campaign communication efforts

Table One

Summary Features of Candidate Tweets

Feature

Pct of Total

Pct of Appeals

Donation Appeal

1.7

6.1

Vote Appeal

7.8

28.8

Read/Watch Info

Appeal

13.6

50.4

Other Appeal

7.9

29.4

Actionable Appeals

17.2

63.4

1+ Appeals

27.0

Interactive Tweets

9.6

10.1

Any Issue Content

24.3

9.3

Election Status

9.5

8.8

Party Mention

13.8

14.6

Opponent Mention

24.3

15.0

NOTE: Columns do not add to 100 because each

tweet may contain more than one of these types

of content.

Table Two

Features of Appeal Tweets

Issue

Percent

Mention

Percent

Economy

4.6 (-1.2)1

Voter Interaction

10.1 (n.s.)

Social

0.5 (-2.0)

Political Party

14.6 (+1.0)

Law and Order

0.2 (-1.0)

Ideology

9.7 (-4.0)

Energy

0.4 (-1.0)

Opponent

14.9 (-13.0)

Welfare

2.2 (-3.0)

Other Elite

12.3 (-3.0)

Defense

1.2 (-2.0)

Event

23.5 (+6.8)

Other

1.0 (-3.0)

Media

22.3 (+11.3)

None

90.7 (+21.0)

No Coded Content

46.8 (+16.5)

One Content Category

52.1 (-17.4)

1Percent difference between tweets with and without appeals

when p<0.05. Based on results of t tests.

29

Table Three

Aggregate Models of Appeal Tweets

Variable

Appeal for Action

Marginal Effect2

Model A1

Model B

Model C

General Statements

Any Issue Content

-0.81**

(0.037)3

-0.76**

(0.038)

-0.77**

(0.039)

-0.205

Mention Party

0.02

(0.039)

-0.02

(0.040)

-0.03

(0.040)

Mention Ideology

-0.24**

(0.043)

-0.21**

(0.045)

-0.19**

(0.046)

-0.056

Mention Elite

-0.18**

(0.039)

-0.32**

(0.041)

-0.33**

(0.041)

-0.095

Election Statements

Direct Reader Interaction

0.06

(0.046)

0.06

(0.047)

Mention Opponent

-0.34**

(0.036)

-0.35**

(0.037)

-0.103

Mention Event

0.33**

(0.036)

0.32**

(0.036)

+0.109

Mention Media

0.67**

(0.039)

0.69**

(0.039)

+0.244

Mention Election Status

-0.04

(0.048)

-0.03

(0.049)

Candidate Characteristics

Candidate Level Covariates?

No

No

Yes

Constant

-0.41**

(0.017)

-0.50**

(0.022)

-0.42**

(0.077)

N

10,398

Pseudo R Squared

0.049

0.084

0.090

PRE

0.00

7.89

7.56

p < **0.01 *0.05

1Results of probit regression models.

2Based on Model C. Other covariates held at their mean values.

3Standard errors reported in parentheses.

Table Four

Aggregate Models of Interactive and Actionable Tweets

Variable

Direct Interaction1

Marginal

Effect2

Actionable

Appeals

Marginal

Effect

General Statements

Any Issue Content

-0.33**

(0.051)3

-0.040

-0.56**

(0.042)

-0.114

Mention Party

0.09^

(0.053)

0.08^

(0.043)

Mention Ideology

-0.26**

(0.064)

-0.031

-0.18**

(0.050)

-0.039

Mention Elite

0.02

(0.056)

-0.38**

(0.047)

-0.079

Election Statements

Direct Voter Interaction

-0.20**

(0.054)

-0.043

Appeal for Action

0.07^

(0.042)

Mention Opponent

-0.09*

(0.048)

-0.013

-0.21**

(0.040)

-0.048

Mention Event

-0.80**

(0.064)

-0.077

-0.02

(0.040)

Mention Media

-0.70**

(0.073)

-0.067

0.56**

(0.041)

+0.160

Mention Election Status

-0.40**

(0.072)

-0.043

0.05

(0.051)

Candidate Characteristics

Candidate Level Covariates

Yes

Yes

Constant

-0.54**

(0.107)

-0.91**

(0.084)

N

10,398

10,398

Pseudo R Squared

0.105

0.059

PRE

0.00

0.28

p < **0.01 *0.05 ^0.10

1Results of probit regression models.

2Other covariates held at their mean values.

3Standard errors reported in parentheses.

Table Five

Candidate Summary Statistics

Feature

Median

Total

Maximum

Total

SD

Median

Proportion1

SD

Appeal for Action

28.5

218

39.95

0.26

0.185

Actionable Appeal

18

133

27.38

0.15

0.163

0.632

0.227

Any Issue Content

19.5

282

46.71

0.22

0.164

Mention Party

6

291

46.53

0.07

0.169

Mention Ideology

1

208

46.12

0.01

0.201

Mention Elite

12

153

27.59

0.12

0.107

Direct Voter Interaction

3.5

135

26.32

0.04

0.105

Mention Opponent

9

419

65.51

0.13

0.197

Mention Event

20.5

127

25.88

0.17

0.156

Mention Media

11

144

26.78

0.12

0.086

Mention Election Status

7.5

104

17.34

0.08

0.068

Total Tweets

92

771

150.02

1Proportion of all tweets posted by a given candidate.

2Proportion of all appeal tweets posted by a given candidate.

Table Six

Candidate-Level Models of Tweet Features

Variable1

Appeal for

Action

Actionable

Appeal

Direct

Interaction

General Statements

Any Issue Content

-0.05

(0.042)2

-0.04

(0.051)

-0.24*

(0.096)

Mention Party

-0.05*

(0.020)

-0.04^

(0.025)

0.14^

(0.079)

Mention Ideology

-0.02

(0.002)

-0.03

(0.023)

0.01

(0.053)

Mention Elite

-0.15*

(0.060)

-0.02**

(0.073)

0.13

(0.149)

Election Statements

Direct Voter Interaction

0.02

(0.040)

0.01

(0.048)

Appeals for Action

-0.09

(0.103)

Mention Opponent

0.04

(0.031)

0.03

(0.038)

0.30**

(0.086)

Mention Event

0.19**

(0.058)

0.02*

(0.070)

-0.12

(0.095)

Mention Media

-0.05

(0.101)

-0.04

(0.122)

-0.89**

(0.187)

Mention Election Status

0.08

(0.104)

0.12

(0.125)

0.16

(0.161)

Candidate Characteristics3

Republican

>0.01**

(0.174)

-0.05

(0.213)

0.08

(0.313)

Incumbent

-0.08

(0.222)

-0.06

(0.266)

-0.41

(0.413)

2010 Vote

-0.01

(0.009)

-0.01

(0.011)

-0.02

(0.017)

2010 Funding ($100,000s)

>0.01

(0.006)

>-0.01

(0.008)

-0.03^

(0.016)

Additional Candidate Covariates

Yes

Yes

Yes

Constant

3.29**

(0.553)

2.32**

(0.661)

1.68^

(0.989)

N

71

α

0.29**

(0.060)

0.42**

(0.087)

0.62**

(0.142)

Likelihood Ratio χ2

87.18**

71.72**

91.29**

Table Six (continued)

Candidate-Level Models of Tweet Features

1Results from negative binomial regression models. All tweet level

covariates reported as effect due to additional 10 tweets with the

respective feature.

2Standard errors reported in parentheses.

3Candidate level coefficients are the result of the presence of each

characteristic.