Getting Out the Vote in the Social Media Era: Are Digital Tools Changing

the Extent, Nature and Impact of Party Contacting in Elections?1

1 *The UK and U.S. dataset used for this project was funded by the UK Economic and Social Research Council (ESRC) through Research Grant RES-051-27-0299 and is archived at the UK Data Archive http://reshare.ukdataservice.ac.uk/850856/

Abstract:

This paper compares the spread and impact of new digital modes of voter mobilization with more traditional methods (phone, mail and in person canvassing) in recent national elections in the U.S. and UK. We develop hypotheses regarding the relative effects of online contacting and test them using election study data. Our findings show that while online contact by is generally less frequent than the offline form in both countries, this gap is particularly pronounced in the UK. U.S. campaigns also reach a much wider audience than their UK counterparts. In terms of impact, while offline forms remain most effective in mobilizing turnout, online messages are important for campaign participation, particularly among younger citizens when they are mediated through social networks.

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Introduction:

The arrival of the internet and more recently social media has provided parties and candidates

with more personalized ways to engage voters. Research on the growth and effects of these

new types of voter contacting has increased over the past decade. Findings are mixed, with

some studies of email GOTV and registration campaigns showing no discernible effects

(Krueger, 2008; Nickerson, 2007; Stollwerk, 2006), while studies of text messaging have

been more positive (Dale & Strauss, 2007). A prominent Facebook experiment found small

but significant effects of seeing a GOTV message on a friend’s page (Bond et al, 2012).

This paper advances the debate on the effectiveness of online voter contact in three

main ways. First we develop a classificatory framework in which this new mode can be

understood in relation to existing offline modes. This allows us to better specify its

mobilizing effects. We then use election study data from the U.S. and UK, to measure the

extent of online contact in recent national elections. We then compare who received this

contact and its impact on voter turnout and campaign activities in each country. Do online

methods pack an extra mobilizing punch? Does this vary across countries and if so, why?

Literature Review

The study of voter contacting has a long history. Most of the work has focused on the U.S.

and looked at its impact on voter turnout. Studies have covered a range of elections and used

a variety of methods and data including self-reported contact in surveys and controlled

interventions using field experiments. The main conclusion drawn thus far has been that voter

contact, and particularly direct, face-to-face canvassing matters (Blydenburgh 1971, Beck

2002; Bergen et al. 2005; Cutright 1963, Gerber and Green 2000, 2008; Gosnell 1927, Katz

and Eldersveld 1961, Kramer 1971, Merriam and Gosnell 1924; Panagopoulos, and Francia

2009). Indeed some recent research has attributed the upturn in voter turnout in recent

elections to improvements in voter targeting due to the rich demographic, socioeconomic and

consumer or “big” data that campaigns can now access (Panagopoulos and Francia, 2009;

Wielhouwer, 1994).

Analyses beyond the U.S have confirmed the importance of voter contact.

Comparative studies by Beck (2002) and Magalhães (2007, 2010) using survey data from the

Comparative National Election Project (CNEP) covering up to fifiteen countries reported a

significant increase in voting after being contacted. Fieldhouse et al’s (2013) randomized

field experiments in the UK showed that contact matters for British voters. Interestingly

while the authors confirmed that personalized methods were most effective, they also found

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that more remote methods such as direct mail had a stronger and cumulative effect compared

to the U.S.

The arrival of digital communication has provided new and more direct ways for

contacting voters and their mobilizing potential has been studied increasingly over the past

decade. Again studies have focused on the U.S. and looked primarily at the impact of one of

the most common forms of campaign contact - email. Conclusions are mixed. Early

randomized field experiments conducted in state and municipal elections between 2002 to

2005 reported no significant effects of email GOTV and registration campaigns (Stollwerk,

2006; Nickerson (2007). Subsequent work using survey data also found that receiving

unsolicited emails i.e. those which involved no prior sign-up, had no effect on levels of online

or offline political engagement (Krueger, 2010) . Malhotra et al. (2012), however, found that

emails from the local registrar had a small but significant effect on turnout, although those

from civic groups did not.

Studies of text messaging have yielded more positive results. Field experiments

conducted during the 2006 Congressional elections attributed a statistically significant three

percentage point increase in the likelihood of voting in response to a GOTV message (Dale

and Strauss, 2009). Malhoutra et al. (2011) again find a significant effect for messages sent

by local registrars reminding people to vote. Online advertising, however, is seen as

ineffective. Experimental studies of two legislative campaigns in 2012 found that exposure to

Facebook ads had no impact on voters’ name recognition or vote choice (Brockman and

Green, 2013).

The mixed findings about the impact of online contact are somewhat surprising given

the importance attributed to digital tools in Obama’s victory in 2008 (Kenski et al. 2010).

However most candidates would struggle to leverage the resources that Obama devoted to his

e-campaign. Furthermore the lack of a national register of email addresses and mobile phone

numbers means that campaigners are rarely, if ever, reaching an undecided voter through the

Web. The people contacted online are actually more likely to have already signed up to

receive campaign news. Finding mobilization and certainly conversion effects is, therefore,

highly unlikely. Finally even if campaigns could reach a large pool of undecided voters

through these channels their efforts may well be counter-productive. Mobile phones and

platforms like Facebook are highly personalized mediums of communication and unsolicited

messages are likely to be regarded as more intrusive than a ‘cold call’ to a landline or flyer

posted through the mailbox.

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Given the barriers to parties’ reaching voters directly through online means, attention

has shifted to more indirect and mediated methods.. Evidence from a pioneering experiment

by Bond et al. (2012) conducted on 61 million Facebook users has showing that those who

received a reminder to vote message endorsed by a random selection of their friends were

significantly (0.4%) more likely to have voted than those who receive a neutral non-endorsed

message (vote validation was carried out on a sample). Although the increased likelihood was

small, based on the size of the user population of Facebook and likelihood of further

accidental exposure the authors estimated that over a quarter of million more votes were cast

than would otherwise have been the case. The idea that the impact of mobilizing messages’ is

increased when mediated through friends and family chimes with findings from the offline

contact literature. While the causal mechanism has not been extensively theorised it is clear

that social context and particularly discussion networks provide important cues for political

opinion formation and vote decisions (Lazarsfeld et al. 1948; Beck et al. 2002; Huckfeldt et.

al. 1995; Leighley, 1990; Magalhães 2007; Partheymüller and Schmitt-Beck, 2012; Popkin,

1991; Schmitt-Beck, 2008; Sniderman et al., 1991). Contexts of social interaction tend to be

characterized by a similarity of interests and mutual trust, increasing the likelihood that the

cues received are seen as credible and help provide a shortcut in political decision-making

(Huckfeldt and Sprague, 1995). This can be particularly consequential for individuals with

lower levels of political information and awareness (Zaller 1992; Beck et al. 2002). Partisan

messages mediated through online social networks are likely to operate in a similarly

mobilizing fashion.

Research Questions and Hypotheses

Drawing on the findings of the recent literature and prima facie reasoning we move to

formulate some expectations about the effects of online contact on voter behaviour. Given the

literature to date has shown that more personalized face to face contact is the ‘gold standard’

mode we start with the assumption that online methods are likely to be less effective than

offline, particularly door to door canvassing and telephone. This disadvantage is likely to be

compounded by the heavy reliance of online contact on prior sign-up and the extent to which

the channels it uses are seen as spaces for private interaction rather than public or commercial

communication. Countering these limitations, however, are the greater opportunities the

online environment offers to increase personalisation of messages through two-step

communication via social networks. While our dominant expectation, therefore, is that newer

types of online campaign contact will be less influential than traditional modes, we expect

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this to apply particularly to contact direct from the parties. Contact that is mediated through

social networks is likely to be more extensive and equally as effective as that via offline

means. To set out these expectations more clearly we develop a categorisation of contact

based on these two axes of mode and source of contact. The resulting four-fold typology is

presented in Table 1.

Table 1 about here

The first axis centres on the mode of delivery – offline or online. For the former we include

four methods that have varying degrees of personalization but do not rely on digital channels.

For the online mode we include four of the main channels for digital communication - email,

SMS2, social network sites and Twitter. We then divide each mode according to whether the

source of contact is direct and comes via official channels or is indirect and is passed on

through friends or family.

Based on the prior discussion of the literature we advance four specific hypotheses

regarding the relative effects of the different types of voter contact:

H1 Direct offline contact is most likely to increase voter turnout.

H2 Direct online contact is least likely to increase voter turnout.

Our expectations for indirect contact are less specific although we would expect both types to

fall somewhere in between the two direct modes in regard to effectiveness. Given that a

confirmation of H1 and H2 leaves this as the logical or implicit outcome we restrict our

hypotheses to two at this point. 3

As well as examining the effects of contact on turnout we examine its impact on

campaign activity during the election. While the same caveats apply to online contact and the

greater difficulties it faces in reaching undecided voters compared with offline, given that

campaign activities have a cumulative quality and are likely to appeal to more highly engaged

supporters we expect this to reduce some of the disparity in the impact of online and offline

contact. This leads us to moderate the expectations of H1 and H2 as follows:

2 We acknowledge that sms or text messages form something of a hybrid in that they constitute a type of phone contact and thus could be seen as offline. However, to date they have been treated as part of the ‘new’ forms of communication by the studies referred to here. Also, typically now email and other types of web communication are accessed through one’s phone, making the distinction less clear. We treat them as online for purposes of this paper. 3 One might argue that the sheer scale and volume of online indirect contact would mean it would have a more pronounced effect than that occurring offline. However as with direct mobilization, face to face or more personalized efforts at persuasion by friends on behalf of a party may prove significantly more effective than an electronic prompts, countering this quantitative advantage.

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H3 Direct offline contact and direct online contact will both significantly increase the

likelihood of engaging in campaign activities.

H4 Direct online contact will have a stronger effect on campaign activities than on

voting.

Before moving to test our hypotheses we reflect on how the comparative aspect of the

study may moderate our expectations. The cross-national research that exists indicates

significant variance in the incidence of contacting which appears to linked with the age of the

party system and electoral rules in place. (Karp and Banducci, 2007). Established

democracies where parties are well resourced and experienced see more voter contact. If

parties are also competing in single member districts (SMD) under majority/plurality rules

where cultivating the personal vote is important then the rates of contact increase further.

Also because turnout tends to be lower in plurality systems (Powell, 1986) mobilization

efforts have a greater potential to be effective. Given that the U.S. and the UK meet these

criteria we would expect both to exhibit healthy rates of contact, making them appropriate if

not ideal cases for analysis where contact constitutes a key independent variable.

Beyond these general similarities it appears that face to face methods are most

effective in both countries although direct mail appears to work better in the UK. Given that

we have merged these methods into a generic ‘offline’ mode however, we still expect H1 to

hold for both cases. Whether context would affect H2 is less clear based on existing research.

Online mobilization has achieved a higher profile in the U.S. than in the UK. and several

scholars have pointed out how the institutional environment of the former intensifies levels of

web campaigning (Anstead and Chadwick, 2008; Vaccari 2013). The candidate-centered

nature of campaigns, high cost and high frequency of elections in the U.S. creates a strong

incentive to use the more individualized and inexpensive medium of the web. Thus while one

would expect H2 to hold in the U.S. one might expect it to hold more strongly in the UK

given the greater restraint of parties toward online campaigning.

Conversely we would expect hypotheses 3 and 4 to gain stronger support in the U.S.

particularly in regard to presidential campaigns which are not only vastly longer than the

national campaign in the UK, but also much more costly. Candidates are responsible for

generating their own election resources unlike in the UK where they are typically supplied

through the party organization. This creates yet further pressure to exploit all available

options to reach voters over a sustained period of time.

Data and Methods

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To test our hypotheses in the U.S. we use the 2012 American National Election Study

(ANES).4 The survey included indicators of all four forms of contact: online and offline

direct contact and offline and online indirect contact.5 In the UK we used two data sources

due to the fact that no single survey allowed us to test all four hypotheses. The first is a post-

election, face-to-face survey conducted by BMRB, a UK polling company.6 The survey

included indicators of three of the four forms of contact: online direct contact, offline direct

contact, and online indirect contact and our dependent variable voter turnout.7 This allowed

us to test H1 and H2. The second dataset is the post-election, cross-section, face-to-face

sample of the 2010 British Election Study (BES)8. This survey included detailed indicators of

online and offline direct contact9 and a measure of campaign participation but did not allow

us to split indirect contact into online and offline forms. It was used to test H3 and H410

4 Data collection for the ANES 2012 Time Series Study started in early September and ended in January, 2013. Pre-election interviews were conducted with study respondents during the two months prior to the 2012 elections and were followed by post-election interviews beginning November 7, 2012. The overall N was 2,056. Note that the contacting variables are drawn from the data gathered from running the CSES module IV battery.5 These indicators are derived from the following questions: during the campaign, did a party or candidate contact you in person or by any other means? (formal contact); during the campaign, did a friend, family member or other acquaintance try to persuade you to vote for a particular party or candidate? (informal contact); did they contact (try to persuade) you in person/face-to-face?; did they contact (try to persuade) you by mail?; did they contact (try to persuade) you by phone? (offline contact); Did they contact (try to persuade) you by text-message or SMS?; did they contact (try to persuade) you by email?; did they contact (try to persuade) you through a social network site or other web-based methods? (online).6 The fieldwork was conducted between the 20th and 26th of May, 2010. Control of quotas affecting likelihood of being at home (age and working status within sex) was applied following a one stage ACORN and region stratification. The data was weighted to ensure that demographic profiles matched those for all residents in Great Britain aged eighteen or over. The overall N was 1,960.7 The question wordings were: for direct contact – “In the course of the recent election did anyone from a political party, campaign or political organisation contact you to ask about how you were planning to vote through any of the following methods? Online or internet-based contact (i.e. through email or any internet/web-related technology); By telephone, mail or face to face; I have not been contacted; Don’t know or not sure”. For indirect online contact: “In the course of the recent election, did you receive any campaign-related political messages or content through the internet from a friend, a member of your family or someone at work? Yes; No; Don’t know or not sure”.8 The fieldwork was conducted between the 7th of May and the 5th of September 2010 by TNS-BMRB. The sampling followed a multi-stage stratified random design. Weighting was then used to ensure survey respondents are representative of residents in private households in Great Britain aged 18 years or older. The overall N was 3,075.9 These indicators are derived from the following battery: a) “Did any of the political parties contact you during the recent election campaign?”- b) “Which of the political parties contacted you during the recent election campaign?” - and c) “Please tell me all the ways that the party X contacted you during the recent election campaign: Telephone call; Leaflets or other material delivered to your home; Email or text message; Visit to your home; Contact in the street; Facebook, Twitter, YouTube”.10 This question did not specify through which method the contact took place (offline or online). The exact wording was: “Did anyone, for example, a friend, a member of your family, someone at work, or some other person try to convince you which party to vote for in the recent general election?”

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Findings

We report first the rates of different types of contact in our two cases (Table 2) and then

profile the recipients (Table 3). As noted while we can provide information on all four types

of contact in the U.S. we are missing a specific measure of indirect offline contact in the UK.

Table 2 about here

As expected, Table 2 shows that overall rates of contact in both countries are robust. Offline

direct contact was particularly frequent during the British campaign with around half of the

electorate reporting contact from a party, campaign or political organisation by telephone,

mail or face-to-face canvassing. Contact from friends and family was lower with BES data

showing that 16% of the electorate had experienced this type of informal persuasion. The

BMRB data allow us to probe the mode of this contact further and indicate that a lot of it

occurred online, with 15% of voters receiving messages or campaign-related content from

people they knew. Online direct contact by parties by contrast was much lower with only 2%

of the electorate reporting this type of messaging. In the U.S. while rates of offline direct

contact were slightly lower than in the U.S. all other types of contact were substantially

higher. This was particularly the case for online direct contact which reached almost one in

five voters according to ANES results.

Table 3 presents the main demographics and political characteristics of those

contacted through online and offline methods in both countries. We use the BMRB data here

since it allowed us to more precisely specify the source and mode of offline contact than did

the BES.

Table 3 about here

The table reveals some differences among those receiving offline and online contact in both

countries although the differences are more pronounced in the UK, particularly with regard to

age. Online contactees in the UK are typically younger, more likely to be male, highly

educated and more interested in politics than those receiving offline contact. The greater

convergence in the profiles of online and offline contacting in the U.S. is interesting in that it

suggests the former is becoming a more mainstream strategy and that our expectation about

the moderating effects of context on our hypotheses is correct. U.S. parties do appear to be

more advanced and proficient than their UK counterparts in targeting a wider range of voters

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through online methods and overcoming the self-selection biases associated with this

approach. More specifically this supports the idea that H2 (weaker effects for online contact)

will be more strongly supported in the UK.

Mobilization Effects

To test our hypotheses we examine the effects of different types of contact on turnout and

campaign participation in the U.S. and the UK using survey data gathered in recent national

elections. In doing so we recognize that analysing voting behaviour using survey data

presents challenges to an investigation of causal claims in general11 and more specifically

with regard to questions of mobilization due to the perennial problem of over-reporting . On

this latter point our data clearly form no exception. Seventy five percent of the BMRB sample

report having voted in the general election (77% in the BES sample) while actual turnout in

2010 was 65 percent.12 In the United States the problem is slightly worse with ANES self-

reported turnout standing at 71% compared to the actual rate of 58%13. The problem is further

compounded by the fact that our key independent variable (campaign contact) is also self-

reported. In the case that unobservable errors in both self-reports correlate, estimates of

campaign effects will be biased upward, driving results to be at least partly spurious (Vavreck

2005).

While fully correcting these problems with observational cross-sectional data is not

possible we can introduce some additional steps to our analysis that allow us to diagnose the

extent to which they affect our analysis and also to ameliorate some of their effects. In

addition it is worthwhile to note that survey do possess some strengths as well as weaknesses

for investigating questions of voter mobilization.14 One obvious means of reducing the

problem of over-reporting introduced by survey data is to rely on validated turnout data. We

have these data for the UK sample and replications of our models using validated vote do not

show any significant changes to our results. However, as Vavreck (2005) has shown, biases

in estimation are typically driven by errors in self-reports of campaign contact, not errors in

self-reports of turn-out. Furthermore work by Berent, Krosnick and Lupia (2011) has found 11 A survey is, in effect, an all-at-once self-report of what is happening inside respondents’ heads at the single time point of the interview and thus do not provide an ideal basis for discriminating competing claims of causality. Experiments, in this view, are close to the “gold standard” for assessing causality 12 Source: The Electoral Commission: http://www.electoralcommission.org.uk/13 Source: United States Election Project: http://elections.gmu.edu/Turnout_2012G.html14 While experiments may constitute the ‘gold standard’ for addressing questions of causality, randomized surveys are the optimal method for assessing distributions of opinions and behaviors in populations. As such, survey-embedded experiment may be the ideal combination for the type of analyses we undertake here. In the absence of such data, however, and given our main interest in this inferences to the population we opt for the survey approach while maintaining caution on any claims of causality.

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that over reporting is much less frequent than assumed and that over self-reports of turnout

may be more accurate than vote validation. Validated turnout data can introduce new sources

of error resulting from erroneous government-records that in some cases might be even more

severe than errors associated with self-reported behaviour.To the extent that we can address

the problems in likely over-reporting of campaign contact we apply two main measures. First

we introduce a control for whether the voter resides in a battleground state in the US or a

marginal constituency in the UK on the basis that those in close fought campaigns are more

likely to over report campaign exposure than those in non-battleground states. Second, we

add a measure of civic duty which as Vavreck (2005) has shown in a large field-experiment,

is the variable that is most potent in purging correlations between errors in self-reported

political activities. All our models include this predictor in order to keep correlations of the

errors at a minimum.

Having implemented these corrective steps we proceeded to examine our first two

hypotheses dealing with the effect of contact on turnout. Given our dependent variable is a

dichotomous measure of voting, binary logistic regression was used as our estimation

method. Our main independent variables are the four types of contact, direct online and

offline and indirect online and offline in the U.S. and the three available to us in the UK

BMRB data. Each of these is dichotomous, coded 1 if the respondent had been contacted in

that fashion. Given that the UK model lacked a measure of offline indirect contact we

included an indicator of political discussion as a proxy to capture and control for some of its

effects. A standard set of demographic and attitudinal controls were included in all models:

gender, age, education, socioeconomic status (income in the US, social class in the UK),

interest in politics and strength of party identification. For all models age was measured in

categories (18-34; 35-54 and 55+). This was done to ensure comparability across the models

since age was not available as a continuous variable in the ANES due to privacy restrictions.

The reference category in all models is the ‘middle’ age group (35-54). The U.S model also

included controls for race (Hispanic and black). Finally given the interesting differences we

observed in the age profile of those receiving online contact across the two countries we

added interaction terms between contact type and age. Essentially this variable was designed

to explore whether the effects of different types of contact were more effective among older

or younger people.

To control for campaign intensity and also the likely over-reporting of contact as

noted above, dummy variables were added to indicate a marginal constituency (UK) or

battleground state (US). Finally we included a measure of whether the respondent had signed

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up to receive online information from the campaign. Crucially this variable allowed us to

control for the likely the self-selection effect associated with direct online contact and thus

more accurately assess its mobilizing effects. Full details of the wording and coding of all the

variables can be found in the appendix.

The results of the turnout models are reported in Tables 4 and 5. Both tables report

two models, the first being the basic model and the second with the interactions added. A

general point to note is that in both countries our control variables are largely statistically

significant, substantively strong, and appropriately signed.

Tables 4 and 5 about here

The results of the basic model in the UK (Table 4) show that as expected offline direct

contacting has the strongest effect on turnout with no other mode of contact proving to be

significant. Our proxy for offline indirect contact, discussion is also positive and significant

although as noted this refers to a much broader type of political ‘talk’ than our concept of

indirect contact specifies. The effect of online direct contact, while not significant, is

surprisingly large in comparison to the other modes including offline direct. While this might

suggest rejection of H2 the estimate is seen as unreliable due to the very small N associated

with this variable, something which is supported by its large standard error in both models.

When we add the interactions with age in model 2 the impact of offline direct contact remains

positive for all age groups although none are now significant, a change that is most likely due

to the reduced N in each category and loss of statistical power. More interesting changes are

observed in the impact of indirect online contact by age. Here we find that younger people

who received digital messages from friends and family were significantly more likely to vote

than those aged 35-54. This finding holds even controlling for prior sign-up. In a further twist

the effect of offline indirect contact for the middle age group which as the reference category

is now the ‘main’ effect becomes negative and significant indicating that this age group are

actually less likely to turnout to vote after receiving this type of informal online contact. This

result is somewhat surprising and cannot be fully explained through these data. One

explanation might be that this age group are the ‘digital immigrants’ who do not experience

the internet and social media as a natural extension of their daily life (as do younger digital

natives) and who also rely most heavily on it in their professional lives. Thus they may be

inclined to regard political messages received through their social network or email as

inappropriate and/or intrusive on their time and to react negatively.

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Turning to our turnout model for the U.S. (Table 5) we find that as expected the ‘gold

standard’ of direct offline contact is significant and has the strongest effect on turnout.

Indirect offline contact is also positive and statistically significant. When we add our age

interactions the main effects of offline direct and indirect contact remain positive and

significant indicating that it is among the middle age group where these effects are most

strongly observed. Interestingly the effect of offline direct contact for the oldest voters

becomes negative indicating that its impact for this group is significantly lower than for the

middle age group. As in the UK no online methods are significant in our initial model,

however, when the interaction terms are added we again do see differences by age, with

online direct contact being significant for older voters after controlling for prior sign-up. This

finding is interesting and may be explained by the fact that older voters would be more used

to receiving offline contact from parties than the other age groups and so would be more

likely to experience the online version as a new phenomenon and respond to it.

In terms of our first two hypotheses, therefore, both cases appear to support H1 with

the U.S. providing the strongest and most consistent support for the importance of traditional

modes of contact on voter mobilization. The results for H2 are largely inconclusive in the

UK, while in the U.S. is it more clearly rejected in that while on first glance direct online

contact appears to have the weakest impact on voters overall, when we look across age

groups it appears to have a particularly strong effect among older voters.

Moving on to the campaign participation models we followed a similar modelling

strategy whereby we ran basic models and then more complex versions with age interactions.

For the UK we switched to use the BES dataset since BMRB lacked a measure of campaign

participation. This introduced certain constraints to the model which reduced its accuracy and

robustness. Firstly, while we retained specific measures of online and offline direct contact

we could not distinguish indirect contact by mode since the BES question did not specify

how friends and family had contacted the respondent. In addition we lacked the ‘sign-up’

variable which controlled for selection effects in receiving online direct contact. Finally the

dependent variable was measured through expectation of future involvement on a scale of

zero to 10 rather than actual behaviour. Diagnostics revealed this variable was highly

positively skewed (61% of the sample reported the lowest likelihood of engagement) which

meant negative binomial regression was substituted for OLS. In the U.S. analysis we used a

dichotomous variable measuring whether the respondent had worn a campaign button or

bumper sticker, attended a rally, given money to either party or candidate, or done other work

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for candidates. Similar to other recent U.S. campaigns, 22% of the sample reported having

done at least one of these campaign activities.

The results for the UK are reported in Table 6. In general we find fewer controls are

significant than in the turnout model although political interest and strength of party

identification remain strong and significant (this is true also for the U.S.). More importantly,

however, the results show that in contrast with the turnout model, the importance of offline

direct contact disappears and indirect contact comes to the fore, as does direct online contact.

Once the interactions are added indirect contact is found to be particularly influential for the

middle age group while for younger people the sign is negative and significant indicating that

young people are substantially less likely to sign up to help a campaign based on this type of

contact than older voters. The effect of online direct contact disappears entirely. While this is

likely due to the N becoming too small across age groups to detect effects, a major caveat is

attached to concluding any mobilizing effect for direct online contact in this model to begin

with given the lack of the prior sign-up variable. While this variable was not significant when

included in the BMRB turnout model (Table 4) one can argue that it is likely to be a

particularly common action among those who would help a campaign and so is more likely to

be significant here. Overall then the findings provide qualified support for H3 and H4 in the

UK. Evidence exists to support an effect for online direct mobilization but there is no

evidence to suggest that offline direct contact mobilizes campaign activity. Also it appears

that online direct contact is more relevant to campaign participation than turnout. The main

finding from the model that was not predicted by our hypotheses, however, is that political

messages mediated through conversations and interactions with friends and family are most

likely to stimulate involvement in the campaign than those coming from parties themselves.

Unfortunately we cannot determine whether it is online or offline interactions are provide the

strongest stimulus since the BES did not differentiate this type of contact by mode.

The results for campaign activity in the U.S. are presented in Table 7. The first model

shows that offline direct contact remains the strongest and indeed only significant predictor

of whether someone volunteers to help a candidate or party. When we then parse the effects

by age we see that the mobilizing effects of offline direct contact are concentrated in the over

55’s. Even more interestingly, however, is the fact that the effects of both direct and indirect

online contact now become evident. Online direct contact emerges as significant in

mobilizing campaign participation, particularly among people aged 35 to 54. Interestingly its

effects are significantly lower for those over 55, which runs somewhat counter to the

previous findings of stronger effects for online contact on turnout among older voters. Thus,

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it seems that while older voters will respond to online messages as an encouragement to vote

they clearly need a push through more conventional face to face methods to get them active

in the campaign.

Online indirect contact also appears to vary in importance for age groups but appears

to be particularly influential among young people (18-34). While this would seem to run

counter to the UK findings that indirect contact was significantly less important for younger

voters than those who are mid-aged, it is not possible to directly compare with the UK here

since the indirect contact variable mixed both online and offline forms and it may have been

if they were separated that similar findings emerged. Overall the results from the U.S. on

campaign participation provide partial support for H3 in that in a ‘straight fight’ offline

contact beats online contact in absolute mobilizing power, however when we break down

their effects by age group it seems that while offline direct contact matters most for older

respondents, online direct is actually more important (based on the size of the coefficients)

for the mid-aged group. Furthermore and in an unanticipated finding, online indirect is

actually most influential for the youngest voters. Finally, in regard to H4 the story is also not

clear cut in that the findings from the basic models show that online direct contact is not

influential in mobilizing either turnout or campaign participation. However, again age

moderates this conclusion in that the effect of online direct contact by parties among voters in

the mid 30s to mid 50s appears to actually be much more influential in mobilizing campaign

participation than turnout.

Discussion and Conclusions

This analysis has sought to understand how widespread and effective online methods of voter

contact are in comparison to offline methods and across different national contexts. Our

findings have shown that although direct or official online contact from parties and

candidates typically reach a smaller audience than offline methods, the gap is much smaller

in the U.S. than in the UK, indicating that American campaigns retain their vanguard status in

the adoption of new electioneering technologies. We have also found that U.S. campaigners

are reaching a more diverse audience with their online messages that is quite similar to those

receiving more traditional forms of contact. This convergence suggests online voter

communication is becoming more mainstream, at least in U.S. national elections. One caveat

to the country divide that emerged is in rates of informal online political contacting which is

much higher in the UK than that from parties and comparable to U.S. levels. Thus it seems

15

that while UK parties have not yet ‘bought’ into digital communication with voters, the

voters themselves are quite comfortable in sharing election related information online.

In terms of impact our expectations are largely confirmed in both countries with

offline contact from parties emerging as most influential in mobilizing turnout among the

electorate as a whole. It is not possible, however, to entirely dismiss an effect for online

contact which does appear to be able mobilize certain segments of the electorate, particularly

older voters in the U.S. Our expectations for campaign participation are also broadly

supported in that the effects of online direct contact appear to be stronger than for turnout,

again particularly for voters in the U.S.

In the course of the research some unexpected but highly interesting findings emerged

about the effects of indirect contact. This mediated messaging was important in mobilizing

both turnout and campaign participation. Most notably the online version was particularly

important in mobilizing younger voters to get involved in the campaign. These findings are

exciting in that they signal that the internet may be reviving the ‘two-step’ flow model of

voter communication and presenting new opportunities to reach an important demographic

group that are typically seen as more disengaged. As well as expanding the comparative

focus of this analysis and examining the impact of online and offline forms of contact outside

of the UK and U.S., future research should also look more closely at disaggregating the

mobilizing effect of different modes and sources of contact to see if this more nuanced

pattern holds elsewhere.

16

Table 1: Modes of Voter Contact

MODE

OFFLINE ONLINE

Source of Contact

Direct (campaign)Q1F2FMail

PhoneLeaflet

Q3Email

Social networkTwitterWebsite

Indirect (F&F)Q2F2FMail

PhonePassing on leaflet

Q4Email

Social networkTwitter

Website Leaflet

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Table 2 – Percentage of citizens contacted in the last elections in the UK and the U.S.:

UK BMRB 2010

UK BES 2010 U.S. ANES 2012

Direct – Online 2.6 1.6 17.3

Direct - Offline 46.7 50.6 36.2

Indirect – Online 15.116.5

22.8

Indirect – Offline NA 38.9

Note: in the BES 2010, indirect contact does not specify mode.

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Table 3 Demographic and Political Correlates of Voter Contact

UK US

Direct Offline

Direct Online

Indirect Offline

IndirectOnline

DirectOffline

DirectOnline

Indirect Offline

IndirectOnline

Age (UK: mean; US: median) 48.6 40.3 NA 38.2 5(55-64)

5(55-64)

3(35-44)

3(35-44)

Men (%) 46.3 69.1 NA 57.5 54.9% 53.4% 52.2% 55.0%

Women (%) 53.7 30.1 NA 42.5 45.1% 46.6% 47.8% 45.0%

Education (median) 3(College)

4(Further between

college and university)

NA

4(Further between

college and university)

3(Some

College)

3(Some

College)

3(Some

College)

3(Some

College)

Interest in politics (median) 2(Some)

3(Quite a lot) NA 3

(Quite a lot)

4(Most of the

Time)

4(Most of the

Time)

4(Most of the

Time)

4(Most of the

Time)

Party identification (median)2

(Fairly strong)

2(Fairly strong)

NA 2(Fairly strong)

3(Weak)

3(Weak)

3(Weak)

3(Weak)

 Sources: BMRB post-election face to face survey (UK); ANES post-election face to face survey (US).

Table 4 Logistic regression models of voting, UK 2010

Coef. Std. Err. Coef. Std. Err.Gender (female) 0.06 0.14 0.07 0.14Age 18-34 -0.75** 0.17 -1.02** 0.28Age 35-54 (ref.) - - - -Age 55+ 0.93** 0.18 0.95** 0.28Education 0.14* 0.06 0.15* 0.06Social class 0.28** 0.06 0.29** 0.06Interest in politics 0.48** 0.07 0.49** 0.07Party Id. 0.58** 0.08 0.60** 0.08Signed up online -0.10 0.41 -0.08 0.42Online indirect contact 0.14 0.21 -0.84* 0.33Online direct contact 0.59 0.51 1.46 0.98Offline direct contact 0.39** 0.14 0.18 0.23Discuss 0.41** 0.15 0.65** 0.23Online indirect contact*Age 18-34 1.49** 0.43Online direct contact*Age 18-34 -1.29 1.16Offline direct contact*Age 18-34 0.43 0.33Discuss*Age 18-34 -0.31 0.33Online indirect contact*Age 55+ 1.58 0.84Online direct contact*Age 55+ -1.34 2.10Offline direct contact*Age 55+ 0.21 0.37Discuss*Age 55+ -0.57 0.37Constant -2.07** 0.26 -2.08** 0.29

Pseudo-R2 0.233 0.243N 1,545

Source: BMRB post-election face to face survey**p<0.01, *p<0.05

Table 5. Logistic regression models of voting, U.S. 2012

Coef. Std. Err. Coef. Std. Err.Gender (female) -0.17 0.16 -0.17 0.16Age 18-34 -0.69** 0.21 -0.57** 0.25Age 35-54 (ref.)Age 55+ 0.37** 0.20 0.66** 0.29Education 0.37** 0.09 0.36** 0.09Income 0.25** 0.06 0.24** 0.06Race: white (ref.)Race: black 0.67* 0.24 0.69* 0.24Race: Hispanic -0.18 0.21 -0.18 0.21Interest in politics 0.26* 0.10 0.27* 0.10Party Id. 0.50** 0.07 0.50** 0.07Voting is a duty 1.94** 0.26 2.02** 0.28Voting is a choice 0.79** 0.26 0.90** 0.26Voting neither duty nor choice (ref.)Signed up online 1.37* 0.48 1.50** 0.50Battleground state 0.32 0.21 0.31 0.20Online indirect contact -0.19 0.32 -0.33 0.53Online direct contact 0.17 0.38 -0.79 0.61Offline indirect contact 0.38** 0.23 0.70** 0.38Offline direct contact 0.55** 0.24 1.12** 0.38Online indirect contact*Age 18-34 -0.01 0.74Online direct contact*Age 18-34 0.00 0.86Offline indirect contact*Age 18-34 -0.25 0.52Offline direct contact*Age 18-34 0.04 0.53Online indirect contact*Age 55+ 0.41 0.79Online direct contact*Age 55+ 3.74** 1.14Offline indirect contact*Age 55+ -0.80 0.61Offline direct contact*Age 55+ -1.18** 0.49Constant -4.32** 0.39 -4.54** 0.44

Pseudo-R2 0.31 0.32N 1,737

Source: ANES post-election face to face survey.**p<0.01, *p<0.05

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Table 6. Negative Binomial Regression Models of Campaign Participation, UK 2010

Coef. Std. Err. Coef. Std. Err.Gender (female) -0.17** 0.07 -0.16** 0.07Age 18-34 0.34** 0.09 0.55** 0.13Age 35-54 (ref.)Age 55+ -0.19** 0.08 -0.16 0.13Education 0.04 0.03 0.05 0.03Social class 0.03 0.04 0.04 0.04Interest in politics 0.31** 0.04 0.30** 0.04Party ID. Strength 0.29** 0.04 0.30** 0.04Voting is a duty: agree 0.17 0.12 0.15 0.12Voting is a duty: disagree -0.42* 0.16 -0.42** 0.16Neither agree nor disagree (ref.)Marginal constituency 0.01 0.08 0.01 0.08Indirect contact 0.23** 0.09 0.42** 0.16Online direct contact 0.61** 0.25 0.60 0.45Offline direct contact -0.03 0.07 0.05 0.12Indirect contact*Age 18-34 -0.45** 0.21Online direct contact*Age 18-34 0.09 0.62Offline direct contact*Age 18-34 -0.21 0.18Indirect contact*Age 55+ 0.02 0.25Online direct contact*Age 55+ 0.03 0.62Offline direct contact*Age 55+ -0.03 0.17Constant -1.34* 0.18 -1.45** 0.19

Pseudo-R2 0.035 0.036N 2,934

Source: BES post-election face to face survey.**p<0.01, *p<0.05

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Table 7. Logistic Regression Models of Campaign Participation, U.S. 2012

Coef. Std. Err. Coef. Std. Err.Gender (female) 0.32 0.18 0.35 0.18Age 18-34 0.56** 0.22 0.17 0.37Age 35-54 (ref.)Age 55+ 0.62** 0.22 0.04 0.36Education 0.08 0.11 0.10 0.11Income -0.02 0.07 -0.02 0.07Race: white (ref.)Race: black 0.59** 0.23 0.57** 0.23Race: Hispanic -0.48 0.36 -0.49 0.35Interest in politics 0.32** 0.10 0.30** 0.10Party Id. 0.51** 0.09 0.52** 0.08Voting is a duty 0.29 0.32 0.27 0.32Voting is a choice 0.09 0.36 0.03 0.35Voting neither duty nor choice (ref.)Signed up online 1.03** 0.28 1.05** 0.29Battleground state 0.30 0.21 0.32 0.21Online indirect contact 0.10 0.27 -0.56 0.40Online direct contact 0.44 0.28 1.29** 0.51Offline indirect contact 0.16 0.22 0.39 0.39Offline direct contact 0.81** 0.25 -0.12 0.45Online indirect contact*Age 18-34 1.33** 0.59Online direct contact*Age 18-34 -0.75 0.84Offline indirect contact*Age 18-34 -0.51 0.50Offline direct contact*Age 18-34 0.81 0.70Online indirect contact*Age 55+ 0.71 0.57Online direct contact*Age 55+ -1.30** 0.62Offline indirect contact*Age 55+ -0.19 0.51Offline direct contact*Age 55+ 1.53** 0.54Constant -5.57** 0.68 -5.34** 0.68

Pseudo-R2 0.20 0.22N 1,736

Source: ANES post-election face to face survey.**p<0.01, *p<0.05

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Appendix

Variables in US data (ANES 2012):

Voting: 1=Voted in the 2012 Presidential election; 0=Didn’t vote.

Campaign participation: 1= wore campaign button or bumper sticker, attended rally, did other works for candidates, gave money to either party or candidate; 0= didn’t do any of these activities.

Online direct, offline direct, online indirect, offline indirect contact: 1= Contacted; 0= Not contacted.

Gender: 1=Female; 0=Male.

Age: 3 binary variables coded 1 for ages 18-34, 35-54, and 55+ respectively.

Education: 1= No High School, 2= High School; 3= Some college; 4= 4 years of college/Bachelor/post-graduate.

Family income: 1=less than $10,000, 2=$10,000-14,999, 3=$15,000-$24,999, 4=$25,000-$49,999, 5= $50,000-$99,999, 6=$100,000-$149,999, 7=$150,000-$249,999, 8=over$250,000.

Race: Black (1=yes), Hispanic (1=yes), White (reference category).

Party Identification: 1=Pure independents; 2= independents who lean either Democratic or Republican; 3=weak Democratic and weak Republican; 4= strong Democrat and strong Republican.

Interest in politics: “How often do you pay attention to what is going on in politics?” 1=never; 2=some of the time; 3=half the time; 4=most of the time; 5=always.

Civic duty: three binary variables coded 1for voting considered a duty, a choice or neither a duty nor a choice, respectively.

Signing-up online: “Prior to, or during the campaign, did you use the internet or your mobile phone to sign up for information or alerts (e.g. e-newsletters, text messages, RSS or blog feed) from a party or candidate”. 1= Yes, 0= No.

Battleground State: 0= non battleground state; 1=battleground state (based on Real Clear Politics poll averages)

Variables in UK data (BMRB 2010):

Voting: 1=Voted in the 2012 Presidential election; 0=Didn’t vote.

Online direct, offline direct, online indirect contact: 1= Contacted; 0= Not contacted.

Gender: 1=Female; 0=Male.

27

Age: 3 binary variables coded 1 for ages 18-34, 35-54, and 55+ respectively.

Education: 1=no Secondary; 2=Secondary; 3=College; 4=Further between college and University; 5= University degree or post-graduate.

Social class: 1= E Non Working; 2= D Working class; 3= C2 Skilled working class; 4= C1 Lower middle class; 5= AB Upper middle class or middle class.

Party Identification: 0=Not close to any party; 1= not very strong Party Id.; 2= Fairly strong party Id.; 3= Very strong party Id.

Interest in politics: 0=None at all; 1= Not very much; 2=Some; 3= Quite a lot; 4= A great deal.

Discuss politics: with family or friends: 1= Yes (in the past 12 months); 0= No.

Variables in UK data (BES 2010):

Campaign participation: How likely to work for a party or candidate in a campaign, 0= Very unlikely; 10=Very likely.

Online direct, offline direct, indirect contact: 1= Contacted; 0= Not contacted.

Gender: Female (1=Yes).

Age: 3 binary variables coded 1 for ages 18-34, 35-54, and 55+ respectively.

Education: 1=no Secondary; 2=Secondary; 3=College; 4=Further between college and University; 5= University degree or post-graduate.

Social class: 1= Never worked or other; 2= Manual occupations, including sales and service; 3= Small business owner; 4= Non-manual, including clerical.

Party Identification: 0=Not close to any party; 1= not very strong Party Id.; 2= Fairly strong party Id.; 3= Very strong party Id.

Interest in politics: 0=None at all; 1= Not very much; 2=Some; 3= Quite a lot; 4= A great deal.

Marginal Constituency: 0 = non marginal; 1 = marginal (constituencies in which the difference in vote share between the first and second parties was under 10% in 2005).

Civic duty: three binary variables coded 1for agreement with considering voting a duty, disagreement with considering voting a duty and neither agreement nor disagreement, respectively.

28

29