Getting Personal? The Impact of Social Media on Preferential Voting

Transcription

1 Polit Behav (2014) 36: DOI /s ORIGINAL PAPER Getting Personal? The Impact of Social Media on Preferential Voting Niels Spierings Kristof Jacobs Published online: 18 April 2013 Ó Springer Science+Business Media New York 2013 Abstract Ever since the successful presidential campaign of Barack Obama in 2008, attention has been drawn to the political impact of social media. However, it remains to be seen whether the successful Obama campaign is the exception or the rule. Our research focuses specifically on the impact of social media on preference voting. First it seeks to establish whether candidates make use of social media during election campaigns and whether voters in turn follow politicians. Afterwards it examines to what extent social media make a difference and yield a preference vote bonus. Theoretically, two types of effects are outlined, namely a direct effect of the number of followers a candidate has and a statistical interaction effect whereby a higher number of followers only yields more votes when the candidate actively uses the social media. To carry out our analysis, we make use of a unique dataset that combines data on social media usage and data on the candidates themselves (such as position on the list, being wellknown, exposure to the old media, gender, ethnicity and incumbency). The dataset includes information on all 493 candidates of the 10 parties that received at least one seat in the Dutch 2010 election. It turns out that candidates are eager to use social media, but that relatively few people follow candidates. There is a significant interaction effect of social media usage and the number of followers, but that effect appears to be relatively small. Keywords Social media Preference voting Voting behavior Political parties Political Communication Twitter Niels Spierings and Kristof Jacobs contributed equally to this study. N. Spierings (&) K. Jacobs Institute for Management Research, Department of Political Science, Radboud University, Thomas van Aquinostraat , 6525 GD Nijmegen, The Netherlands n.spierings@fm.ru.nl

2 216 Polit Behav (2014) 36: Introduction The use of social media in election campaigns has drawn a lot of attention recently (Gibson 2009; Hoff 2010; Karlsen 2011; Shirky 2011), and Barack Obama s 2008 presidential campaign is perhaps the best-known example of a politician successfully using social media to mobilize electoral support (Crawford 2009; Swigger 2012). However, it remains to be seen whether the successful Obama campaign is the exception or the rule. After all, the American political system is deeply focused on individual politicians; it is very personalized. Moreover, the American context is one of weak parties, a long-standing tradition of grassroots campaigning, and relatively liberal campaign finance rules. In that sense, it was the most likely case for social media to have an impact, and one should thus be careful when generalizing findings on the Obama campaign (cf. Gibson 2009), as the context and the electoral system in particular may influence the effect of social media (Karlsen 2011). Unfortunately, empirical work on the impact of social media on politics is rare and this holds even more for statistical studies (Shirky 2011:2). 1 For now the question remains: what kind of impact do social media have? This study enters the debate on the impact of social media by offering theoretical propositions outlining two different types of impact and providing a large-scale statistical analysis of the effect of social media on preference voting in the Dutch 2010 election. The Netherlands is representative for most European democracies in two important contextual matters: just like many other European democracies it has a flexible list ballot structure and it has a fairly typical internet penetration rate (89.5 % as of 31 December 2011; As such, it is an excellent country to provide an exploratory test of our theoretical propositions. The analyses themselves rest on a unique dataset that includes the social media usage of all 493 candidates that stood for the 10 biggest parties in the 2010 elections. We start by providing descriptives for the social media usage of Dutch politicians. Afterwards we embark on a causal analysis of the number of preference votes the politicians received. We control for usual suspects such as incumbency, gender, and position on the list, as well as other campaigning and media attention variables. While it is too early to speak of causal effects in a definitive manner, our findings are nonetheless suggestive. The analyses show that some social media have a small but significant effect, in particular when candidates frequently update and use their accounts. Theoretical Background: Explanations of Preference Voting Below we shall discuss how the usage of new social media by political candidates can be expected to influence the number of votes they receive. However, before we do so, we briefly want to discuss the traditional explanations of preference voting 1 The little work that has been done on social media or web 2.0 focuses on majoritarian democracies or the Irish (personalized) STV-system, and it remains to be seen whether these findings also hold in a list- PR setting (Gibson and McAllister 2011; Rackaway 2007; Sudulich and Wall 2010).

3 Polit Behav (2014) 36: first. After all, one of the most important tasks when analyzing the impact of social media is to minimize the chance that findings in support of one s hypotheses are spurious. Indeed, positive findings may well merely reflect candidates who are, for whatever reasons, strong and successful, who happen to have active social media presences, and who ultimately do well in elections. Though it is impossible to rule out this possibility definitively, by carrying out a careful analysis that controls for the most important other explanations that have been identified by earlier studies, one can come a long way. In this section we therefore briefly discuss the traditional explanations of preference voting, many of which pertain directly to the strength of the candidates and their campaigns. Each of these will be included in our analysis and thus help minimize the risk that the observed effects are merely spurious. 2 Two broad clusters of explanations can be identified in the literature on preference voting. A first group of scholars has highlighted the effects of ballot position (Darcy and McAllister 1990; Geys and Heyndels 2003; Krebs 1998; McDermott 1997; Lutz 2010; Wauters, Weekers and Maddens 2010), while a second group focused mainly on the impact of campaign activities (Thijssen and Jacobs 2004; Gibson and McAllister 2012; Smits and Spierings, 2012). Ballot position effects are based on the contention that voters only have a limited capacity and willingness to gather, store, and process information about candidates. In the Dutch 2010 elections for instance, there were 493 candidates in total, and often 50 or more candidates on one and the same party list. As getting in-depth information on all these candidates is virtually impossible, it is argued that voters start evaluating candidates from the top of the ballot and work their way down looking for a candidate they want to vote for (Lutz 2010, p. 169). Based on this reasoning, voters can be expected to vote for higher-ranked candidates. Additionally, one can expect that a substantial number of voters cast their preference votes for the first woman on the list or the first candidate of a certain non-white ethnic group, in order to express the importance they attach to substantial and descriptive representation (Wauters, Weekers and Maddens 2010). The second group of authors focused on campaign activities and identified three sets of campaign variables, namely the overall experience of the candidate, the media attention for a candidate, and campaigning activities by the candidates themselves. Each of these three increases the saliency and name recognition of a given candidate, which in turn increases the chances that (s)he gets more preference votes (Thijssen and Jacobs 2004; Gibson and McAllister 2012). This causal mechanism is obviously the most straightforward in the case of media attention, which directly affects the candidate s salience and name recognition. Experience also matters: not only can experienced candidates build on prior name recognition, they also have more knowledge about how campaigning is best handled and can often rely on a more experienced campaign team. Lastly, campaign activities can also influence a candidate s salience and name recognition. Which campaign activities are most successful is not the same for all countries, while for instance door-to-door canvassing is very common in the US and other Anglo-Saxon countries, it is less so in continental European countries (Karp et al. 2008). In the 2 As these variables are only included as controls, they will not be translated into hypotheses.

4 218 Polit Behav (2014) 36: Netherlands canvassing is relatively unimportant (cf. Andeweg and Irwin 2005, p. 94), and the main tools individual candidates have are flyers, posters, and personal websites. Especially the latter can be very powerful, as they are easily accessible to any voter and can thus reach a broader audience but still allow candidates to speak directly to their potential voters, without high costs (Gibson and McAllister 2012). In sum, social media are a new tool in personal campaigning, a tool with low costs and (possibly?) high benefits. How social media might affect preference voting is discussed below. The Impact of Social Media on Preference Voting Barack Obama can be cited as the first to have genuinely mastered the full potential of social media, and the Obama campaign was especially successful in mobilizing his grass roots supporters (Zhang et al. 2010). Yet, social media can also impact voting itself, as they can persuade a voter to cast a preference vote for a particular candidate, 3 and we shall focus on this particular impact. There are at least two conceivable ways social media can have an impact: a direct effect of the number of social media followers on the number of preference votes a candidate receives and an interaction effect combining the number of followers and actual social media usage. 4,5 A Direct Effect The most optimistic expectation is that even minimal social media use will already have an effect on the number of preference votes. In such cases social media can serve as a showcase or a campaign poster. This may work especially well for social media such as Hyves and Facebook, which allow users to add details about their hobbies, interests, political views, socio-demographic characteristics etc. As such, they allow for more personality-centered campaigns that give potential voters insight in the life and interests of a politician (Vergeer et al. 2011, p. 5). Next to this simple advertisement effect, merely having a social media account can also have a symbolic value, signaling to voters that a given candidate is modern or, conversely, that (s)he is not old-fashioned. In both cases, one can expect that the more followers a candidate has, the larger the effect potentially is. As in addition to these potential benefits, social media also have a fairly low cost, the combination of low costs and (possible) substantial benefits may make such tools attractive for candidates. The mechanism as described above implies a simple one-on-one effect, whereby only the voters following a candidate are affected. However, the effect of social 3 A preference vote is a vote to indicate a preference for (an) individual candidate(s) on a party list that includes multiple candidates. 4 We use the word follower in a broad sense, not to refer merely to Twitter followers. As used here it means all people being social media friends or followers of a candidate. 5 To be clear, the label interaction effect refers to the statistical type of effect, and not to the effect of interaction between candidates and their followers.

5 Polit Behav (2014) 36: media can also be multiplicative: the information transmitted through social media from the candidate to his or her followers can spread out through the digital and real-life social network of these followers (Bond et al. 2012). The entire social media network can be influenced by low-cost tools such as retweets and shared updates, hereafter termed messages. This multiplicative mechanism evidently boosts the possible impact of social media. Additionally, it needs to be noted that the causal mechanism, as described so far, implies an overall positive effect. Reading tweets and updates from politicians can clearly foster the image people have of them, but it can also lead to a more negative image, if voters dislike the messages. Nevertheless, the overall effect can still be expected to be positive, as a sizable effect of candidates social media messages is not about the content but about (name) recognition (as holds for other campaign activities) whereby merely receiving one or more messages from a candidate is more important than the exact content of the message(s) (Grimmer, Messing, and Westwood 2012). Hence, one can expect that the positive effects of reaching out to voters will overall outweigh possible small negative side effects. Summarizing, in the simplest form the effect of being active on social media on the number of preference votes is linked to the number of followers a politicians has. Specifically, one can expect that: Hypothesis 1 The larger candidates group of followers, the more preference votes they receive. An Interaction Effect Some scholars, however, have stressed that merely having a lot of followers does not do the trick. The mere pretence of presence just having an account, even with a lot of followers, but not using it is not enough to convince people to vote for a candidate (Crawford 2009, p. 530). Indeed, such political zombies are unlikely to ignite much voter passion (Wilson 2009). This does not mean that social media have no impact per se, but rather that followers only yield an electoral dividend when a candidate actively mobilizes them. A candidate thus needs to make actual use of the platform for the effect to materialize. 6 Hence, one can expect an interaction effect to occur between the number of followers and the number of social media messages. For social media to have a substantial impact on the number of preferences votes, politicians have to assemble a large number of followers and maintain communication with them. Sending out a message only leads to more votes if voters receive the message, and the more voters do the more preferences votes can be expected. We thus expect that: Hypothesis 2 The more candidates make use of their social media accounts, the larger the effect of the number of followers on the number of votes they receives. 6 This causal mechanism is in line with the first mechanism where we contend that the number of messages is important in creating a connection with voters and increasing the probability of being remembered.

6 220 Polit Behav (2014) 36: Table 1 Main institutional characteristics of the Dutch political system (2010) Topic Overall form of government Parliament layout Electoral system a The act of voting Preference voting Description Parliamentary system Two chambers Lower house ( Tweede Kamer ; 150 MPs) Upper house ( Eerste Kamer ; 75 MPs) List-proportional system; de facto one single district with an electoral threshold of 0.67 % Each voter has one vote. People can only vote for one candidate within a party list From 1997 onwards; a number of votes equal to 25 % of the electoral quota is needed for a candidate to be elected out of the list order. In 2010 the reference threshold was at votes. a From here on we focus our attention to the lower house The Political Institutional Context of the Netherlands As our analysis focuses on the 2010 Dutch Lower House elections, we will shortly outline the Dutch political system, with a particular focus on the ballot structure (see Table 1). Ever since it was introduced in 1917, the Dutch proportional electoral system has remained fairly stable. The Dutch electoral system is a list system: parties present themselves to the voters through a list of candidates. Voters are only allowed to cast one vote for one candidate on one of the party lists; they cannot vote for a party, they can only vote for a candidate. All the votes that are cast for the candidates on a party list are added up and this total number of votes determines the number of seats that this party gets (cf. Andeweg 2005). The votes also determine which candidates get the seats. Whenever candidates cross the preference threshold of 25 % of the electoral quota, 7 they are automatically elected, provided that the party has a seat available (Jacobs and Leyenaar 2011). 8 Afterwards the remaining seats are distributed according to the list order. The number of preference votes thus has priority over the list order, and as a result the system has been characterized as semi-open or flexible, a characterization that makes the Netherlands a typical case when it comes to ballot structure (Colomer 2011, p. 10). In 2010, the Dutch preference threshold was at votes. Of the 30 candidates that crossed this threshold, only two would not have been elected based purely on their place on the party list (calculations based on The electoral quota equals 1/150 or 0.67 % of all votes cast. 8 Candidates who crossed the preference threshold move to the front of the line, but if a party has no seats, these candidates do not conquer a seat. The candidate with the highest number of preference votes gets served first, as long as they have passed the threshold. For all other candidates the list position is decisive. 9 Given that the Dutch electoral system is very proportional and de facto only has one district, the number of electable positions on the list is fairly high. As a result, most popular candidates would get elected based on their position on the list anyway.

7 Polit Behav (2014) 36: Dutch electoral campaigns largely focus on the first candidate on a party s list. Some other candidates set up a personal campaign, but they only receive very limited resources from the party to do so, as parties devote most of their staff and campaign money to the few top candidates. The campaigns have generally been run by the parties and their main focus is to get their message into the media. Grassroots campaigning only plays a limited role in the Netherlands and the individual candidates typically only have a low campaign budget (Andeweg and Irwin 2005, p ). Social media, and internet campaigning in general, serve as an alternative instrument next to the traditional large-scale media-centered campaigns. Just like in other West-European countries, the Dutch internet penetration rate is fairly high (89.5 % as of 31 December 2011; When the elections were held in June 2010, three social media sites dominated the Netherlands: Hyves, Facebook, and Twitter. Hyves, the largest of the three, was the Dutch equivalent of Facebook and broadly functions in the same way. In the Netherlands, Hyves had roughly 8 million users, Facebook had *4.5 million users, while Twitter had some 2.5 million users (Oosterveer 2011). 10 Data and Methods No less than 18 parties fielded candidates in the Dutch parliamentary elections on 9 June Ten of these received at least 0.67 % of the votes and thereby at least one seat. 11 We use a dataset that includes all 493 of the candidates of these 10 parties, and performed OLS (ordinary least squares) regression analyses to test for the impact of social media on the number of preference votes received. Our dependent variable is simply the absolute number of preference votes a candidate received. Our main independent variable, social media, was operationalized by using two different social media platforms, Twitter and Hyves. Facebook of which Hyves is the Dutch equivalent was active in the Netherlands, but in June 2010 it was still significantly smaller than Hyves, which is why we opted for Hyves instead of Facebook (Oosterveer 2011). We registered the number of Hyves and Twitter followers each of the candidates had, and these independent variables allowed us to assess the first hypothesis. To examine our second hypothesis, which expected a statistical interaction effect between the number of followers and social media use, we used the number of tweets each candidate sent out (from 27 April to 8 June 2010) as provided by Emma Communicatie and registered the number of updates they placed on their Hyves profile from 26 May to 2 June As mentioned earlier, when studying the impact of social media it is extremely important to avoid the danger of spuriousness by including adequate controls. We gathered data on all the core variables in the literature on campaigns and preference 10 In 2010, the Netherlands had some 16.8 million inhabitants. 11 The following parties received at least one seat: VVD, PVDA, Partij voor de Vrijheid, CDA, SP, D66, GroenLinks, ChristenUnie, SGP, Partij voor de Dieren (

8 222 Polit Behav (2014) 36: voting. Regarding the first cluster of control variables, the ballot position effects, we included a candidate s position on the list. We also included variables that have a special meaning in the Dutch context: whether the candidate was list-puller, 12 and whether the candidate occupied the (next-to) lowest position on the list, because Dutch parties often assign local celebrities or party legends to these positions. In addition, dummy variables for each party and interaction terms of these dummies with the list-puller positions were included as well. These variables control for party-specific effects, including the number of listed candidates and the popularity of the specific list-puller. Based on the ballot position effects, one can also expect that a substantial number of voters cast preference votes on the first woman on the list or the first candidate of a certain non-white ethnic group (Thijssen and Jacobs 2004; Wauters, Weekers and Maddens 2010). 13 To capture these effects we included four dummies: one for being a woman; one for being the first woman on the list; one for having a non-white ethnicity; one for being the first person on the list with a non-white ethnicity. We also included a set of variables for each of the three variables that can be identified in the campaign literature, namely the overall experience of the candidate, the media attention for candidates, and campaigning activities, all of which increase candidates salience and name recognition (Thijssen and Jacobs 2004; Gibson and McAllister 2012). It should be noted that there is some overlap between these categories. Firstly, to capture experience, we included a variable on incumbency. Incumbency is operationalized as a dummy: (1) was member of parliament after the previous elections or was minister in the last government; (0) was not. Of the 493 candidates, 129 were incumbents. The variable that captures campaigning experience measured whether the candidates stood for election during the previous elections as well: (1) yes; (0) no. This was the case for 168 of the 493 candidates. A second set of variables focuses on factors related to media attention. We included two measurements of the media attention the candidate received in the national newspapers. 14 One variable measures the number of articles in which the candidate has been mentioned in the period of the official campaign (i.e. the last 6 weeks for the elections), which mainly taps into the degree to which someone is well known in combination with campaign effectiveness. It also reflects an important part of the success of a candidate s campaign activities. The other newspaper variable measures the same but takes in the year before the official campaign, representing a candidate s general name recognition. We also added another broader control variable that focuses directly on being well known, dividing the candidates into two groups: well known (1) and not well known (0). This variable adds a subjective coding to the abovementioned objective items and makes it possible to capture 12 List-pullers are the first candidates on the list. In total, 84% of all votes cast on the candidates in our data was cast for the 10 list-pullers. 13 Only two out of 10 parties had a woman as list-puller. 14 The following newspapers were included in our measurement: AD/Algemeen Dagblad; Algemeen Dagblad; Boerderij Vandaag; Dag; Dagblad De Pers; De Volkskrant; Het Financiële Dagblad; Het Parool; Metro (NL); Nederlands Dagblad; NRC.NEXT; NRC Handelsblad; Reformatorisch Dagblad; Spits; Trouw.

9 Polit Behav (2014) 36: Table 2 Social media presence Social media (n = 493) Hyves No Hyves Twitter 111 (22.5 %) 57 (11.6 %) No Twitter 116 (23.5 %) 209 (42.4 %) people who are generally well known. After all, some well-known people do not appear in newspapers. We coded candidates as well known (74 out of 493) if they were generally known politicians (president of parliament, chair of parliamentary research committee, spokesperson on topical debates, party leaders, party presidents, executive member of a local government of a major city, mayor); presidents of a highly visible NGO, labor union, or employer s organization; TV presenters or anchorpersons; and opinion leaders and prominent activists. 15 Third, in order to capture other campaigning activities, having a personal website was included as this is the main means for candidates themselves to broadly reach out to voters. This is operationalized as a dummy, whereby the 174 candidates with a personal website score (1) and the remaining ones (0). As for our data sources, we made use of the archives of the Dutch electoral management body ( and supplemented these with Twitter data provided by Emma Communicatie, information provided by the Dutch political parties, and newspaper data in the Dutch newspaper archives. The Hyves and personal website data we collected ourselves. To examine the impact of social media, we use OLS regression analysis. Descriptives: are Politicians Using Social Media? Before we embark on our explanatory analysis, it is obviously important to ask the basic question first: did the candidates use social media? As Table 2 shows, a majority of the candidates, 57.6 %, had either a Hyves or a Twitter account, and 22.5 % of them actually had both. What about the citizens? The number of citizens that follows a candidate is fairly low, but there are notable exceptions. On average, a candidate with a Twitter account had 4,924 followers; while a candidate with a Hyves account on average had 1,981 friends (followers). As the high standard deviations indicate, the differences between the candidates are sometimes quite substantial. Nevertheless it is safe to say that most candidates have a relatively low number of followers, as is also illustrated by Figs. 1 and 2, which show the distributions of the followers. By themselves, these follower numbers are not 15 The intercoder reliability is 97.3 %, a value that can be considered very high, which is not surprising given the availability of explicit and fairly objective coding criteria. The disagreements mainly arose regarding opinion leaders and Presidents of highly visible NGOs. The final decisions were taken by consensus.

10 224 Polit Behav (2014) 36: Fig. 1 Distribution of Hyves followers Fig. 2 Distribution of Twitter followers

11 Polit Behav (2014) 36: Table 3 Average followers (Hyves/Twitter only data) Social media Average followers SD Maximum Twitter 4,924 13, ,374 Hyves 1,981 15, ,288 enough to cross the Dutch preference threshold. 16 Yet all of this does not necessarily mean that social media are irrelevant. For instance, one could argue, as Gibson and McAllister (2011, p. 230) do, that a two-step effect may well be operating: social media followers are just the first step, and in a second step, these followers will act as ambassadors, using information received from social media to influence their friends and peers (Table 3). Explanatory Results One way of assessing the impact of social media is to look at the impact they have on the number of preference votes a candidate receives. Table 4 presents the results of our OLS regression analyses. The effect of the list-puller, the first candidate on a list, clearly dwarfs all other effects: it is robust and remains consistent at a very high level. This should come as no surprise, as the Dutch electoral system does not allow casting a party vote. The first place on the list is reserved for the party leader and [v]oters who have a preference for a party but not for any particular candidate usually cast their vote on the first candidate on the list (Andeweg 2005, p. 494). As a result, the list-pullers receive an extremely large number of preference votes compared to the other candidates on the list. However, once the list-puller effect is filtered out, some other effects stand out. The other control variables, with the exception of incumbency and having a personal website, all perform as expected. 17 But what about the social media variables? The direct effect of having a Twitter account is significant (p \ 0.05), but fairly limited: every 1,000 followers yield some 190 extra votes (Model 2). Given that the average Twittering candidate has 4,924 followers, this means that she or he would receive 936 extra votes compared to candidates without a Twitter account, or approximately one-sixteenth of the 15,694 preference votes needed to cross the 16 The fairly low numbers are consistent with findings in other countries (e.g. Gibson and McAllister 2011; Hoff 2010). 17 The incumbency variable is mostly used in American research and is said to have a substantial positive effect. However, in a proportional electoral system with multi-member districts, the incumbents are routinely placed higher on the list. Hence most of the incumbency effect is probably absorbed by the latter variable. A second cluster of variables that most likely absorbs a substantial part of the effect consists of being well known and the number of newspaper articles a candidate featured in the year before the campaign. Moreover, a party-list proportional representation system produces a lot of lesser-known incumbents as they have no local constituency, but are still elected. Hence, it should not come as a surprise that the effect of the incumbency variables is very limited or absent. The same holds for having a personal website: many candidates (174), including many of the lower-ranked ones, had a website, and we control for list placing and a host of other variables such as media exposure.

12 226 Polit Behav (2014) 36: Table 4 Social media and preference votes (OLS regression) Model 1: controls Model 2: direct effect Hyves and Twitter Model 3a: interaction effect Hyves*use Model 3b: interaction effect Twitter*use Coef. Stand. coef. Coef. Stand. coef. Coef. Stand. coef. Coef. Stand. coef. (Constant) 1,034 1, ,527 Hyves followers (H1) (per 1,000) 1, Twitter followers (H1) 190* (per 1,000) Interact. Hyves (H2) 1,151*** Hyves use (H2) -310* Interact. Twitter (H2) 11*** Twitter use (H2) -28* # Journalists (H3) Incumbency -1, , , Well-known 2,528* , ,342** , # Newspaper art. (1) 12*** *** (year before campaign) # Newspaper art. (2) (campaign period) 160*** *** *** Personal website Campaign experience Position list -66** ** ** End of list List-puller a 1,2E6*** ,1 E6 *** ,3 E6*** c First woman 42,189*** ,461*** ,403*** c Woman ,613*

13 Polit Behav (2014) 36: Table 4 continued Model 1: controls Model 2: direct effect Hyves and Twitter Model 3a: interaction effect Hyves*use Model 3b: interaction effect Twitter*use Coef. Stand. coef. Coef. Stand. coef. Coef. Stand. coef. Coef. Stand. coef. First ethnic non-white cand. 9,736*** ,277*** , Ethnic non-white cand. 1, , , Adjusted R 2b (0.656) (0.663) (0.772) (0.772) N (Hyves only) 168 (Twitter only) Sources self-collected data; Emma Communicatie * p \ 0.05; ** p \ 0.01; *** p \ We also included 10 party dummies and interactions between the party dummies and the list-puller variable as a set of technical control variables. These are not included here as they only filter out the party effects. Coefficients of these controls can be founding Table 6 in appendix. Because of the added instrumental interaction terms of party with list-puller (see below), the coefficient for list-puller basically measured the effect of being the list-puller of the party for freedom (PVV). Nevertheless, there is a strong list-puller effect found for each and every party a b As most of the variance is due to the list-pullers, for which we included dummy variables (see a ), the R squares are extremely high and mainly reflect the explanatory power of those variables. The figure between brackets is the adjusted R 2 for the same models excluding the list-pullers (the B coefficients presented here remain exactly the same) Due to extreme multicollinearity, model 3b did not include list-puller or first woman. In other words, the effects of these variables have been captured completely by the other control variables, because only people with Hyves or Twitter accounts are used in these models c

14 228 Polit Behav (2014) 36: preference threshold. 18 Not bad, but fairly limited all the same. The Hyves effect is larger every 1,000 followers seem to yield a bonus of 1,343 votes but is not statistically significant. 19 In a second analysis we examined whether there is an interaction effect between candidates use of social media accounts and the influence the number of followers has on the number of preference votes they receive. As including the candidates who do not have an account would bias the findings, we only selected the people with a social media account. 20 Because these people are different for Twitter and Hyves, we conducted two separate analyses (Models 3a and b) and found both interaction effects to be significant. 21 The analyses refine our previous findings. Merely having a Twitter or Hyves account does not do the trick: the effect of having an account without updating it is not significant. But the significance of the interaction terms shows that when candidates use social media more, the effect of the number of followers on the number of preferential votes increases (and becomes significant). Each tweet during the campaign period 22 adds 11 extra votes per 1,000 followers. Given that the candidates on average sent out 112 tweets, this means 1,232 extra votes per 1,000 followers. Similar results are found for Hyves users, though the effect seems somewhat larger. However, given our previous findings we should be careful interpreting these results. 23 All in all, it seems that, regarding Hypothesis 1, the 18 It needs to be stressed though that using the average offers the best case scenario. Many candidates had far lower numbers of followers, as the average is biased towards the upper end (cf. Fig. 2). 19 An explanation for this relationship not being statistically significant may be that the number of Hyves followers has a very high skewness. Indeed, 69 % of all Hyves followers are accounted for by just two candidates; for Twitter this percentage is only 26 %. 20 This mirrors the logic of election research where citizens younger than 18 are not included as they cannot vote. Indeed, including candidates who do not have a social media account would artificially inflate the number of candidates with low scores on social media usage as having no account automatically means a zero score on usage. Our dataset still includes candidates with zeroes on one or the two usage variables. Such candidates have an account, but do not use it at all. Since direct effects of social media are found, including artificial zeroes on the usage variables potentially biases the results in favor of our hypotheses, which is a second reason to exclude the candidates who do not have an account from the interaction models. 21 To test the robustness of these models we also performed the analysis for Twitter and Hyves together (integrated Models 3a and b), with all the candidates that used at least one of the two platforms. That model shows results that are similar to the ones reported here: both interaction effects are positive and statistically significant (at least p \ 0.01). The coefficient for Twitter remains 11, for Hyves it decreases a bit to 784 (compared to 1,151 here). These additional analyses support our conclusions. In addition, we reran the interaction models on the full sample of 493 candidates as well, both for Twitter and Hyves separately and for both combined. All four interaction coefficients in the three models are statistically significant (p \ 0.01). For both Twitter and Hyves we find interaction effects similar to the one reported here. The B-coefficient for Twitter is 9 or 10 (here 11) and for Hyves 964 and 1,268 (here 1,151). Again these robustness checks strengthen our conclusions. 22 The campaign started on 27 April and lasted until 8 June As 49 % of the candidates who have a Hyves account also have a Twitter account, some of the impact of Twitter use may well be included in the Hyves effect. Another reason to be cautious is the fact that only 47 candidates updated their profile, so the data are very skewed. Moreover, it can be questioned whether the interaction effect is as gradual as presented here. For the usage of Twitter we were able to partly test this by running the model with a dummified ordinal variable for the number of tweets send, and interaction terms of each of those dummies with the number of followers. This model is not presented

15 Polit Behav (2014) 36: direct effect of the number of followers strongly depends on the usage of the social media platform, which means that the direct effect is conditional and cannot be simply expected; it is not enough to just have an account. Hypothesis 2, on the other hand, is corroborated: those candidates who actively use their accounts reap the harvest of social media, while non-active users enjoy far smaller benefits. 24 These findings also trigger new questions, such as, how can it be that 1,000 followers yield a bonus of more than 1,000 preference votes? Here the multiplicative effect seems crucial. It may well be that (some of) the followers are opinion leaders who use the social media information to convince their friends and family, or that they simply show, retweet, or share messages making candidates more known among their followers and friends. Or, as some of a candidate s followers are grassroots activists, they can use the candidate s messages in their campaign efforts (Jungherr 2012). Hence, a two-step, multiplicative effect is more likely than a simple one-on-one effect (Gibson and McAllister 2011, p. 230). In the Dutch context, where grassroots campaigning is largely absent, especially the opinion leader/sharer is likely to have played a role. Given the nature of our data (cf. Methods ), we cannot distinguish between the one-on-one and multiplicative effect in our analyses. Our analyses reflect the sum of both effects, which may explain why the number of extra votes exceeds the number of followers. 25 Conclusion Given their low cost, it is perhaps not surprising that Dutch politicians use social media quite often during the election campaign. A decent majority of them either Footnote 23 continued here because it is far more complex and yields roughly similar results: according to our data the effect is relatively stable and it is not the case that the major difference is found between candidates who update their account and candidates who do not. We distinguished groups based on the average number of tweets per day: 0-1; 1-2; 2-3; 3 5; 5 10; [10. The model on all 493 candidates showed a more or less linear model, or with some creativity two thresholds between three groups could be derived from the model: (1) candidates with no tweets up to 1; (2) candidates tweeting 1 to 5 times a day; and (3) candidates who tweeted at least 5 times a day. A model on the 168 Twitter users only showed the most difference between candidates not tweeting, candidates tweeting up to once a day, and candidates tweeting more regularly. However, the standardized coefficients of that model also suggested a gradual effect. For Hyves, we could not carry out such additional analyses. The nature of the platform is less directed to publishing small updates and only 47 candidates placed one or more short updates, in our data set: 21 placed one update, 6 two, and the rest between 3 and 32. The groups would become too small to perform the interaction analyses. All in all, these additional tests suggest that the advantage of having more followers in combination with posting more updates is cumulative and not just a difference between candidates who Tweet and those who do not. 24 Based on these coefficients, one can assess the overall impact of social media on the election outcome. It turns out the effect was very limited: social media made the difference for just two candidates who would not have been elected otherwise, namely Sabine Uitslag (15,933 preference votes) and Pia Dijkstra (15,705 preference votes). 25 Bond et al. (2012) show that such multiplicative effects can amount to significant numbers. In their study on political mobilization in the U.S. they show that the spread of messages -effect was small for each individual voter, but given large numbers of potential voters, the estimated overall effect adds up quickly.

16 230 Polit Behav (2014) 36: has a Hyves or a Twitter account. As most candidates only had a limited number of followers, though, one could rightly ask what the impact of social media on preference voting is. Our OLS-regression analyses showed that by far the largest chunk of preference votes is explained by the position on the list. Especially listpullers perform extraordinary well. Yet our analyses also showed that social media do have a significant (albeit rather limited) added value in terms of extra preference votes. Next to this general finding, our study found that using social media is crucial. All in all, the answer to our research question is that social media Twitter in particular have a modest effect on the number of preference votes a candidate receives when they are used. To what extent can these findings be generalized? Let us first start with the internal validity of the analysis. The risk of an omitted variable always exists in statistical research, which may lead to potential biases in the coefficients. For instance, not including grassroots activities might slightly bias the effects; but most of those effects are captured by our control variables that tap into the effect of campaign activities. 26 For this study, the most notable omitted variable would be the evaluation of a given candidate. This might be problematic because voters may well follow the politician they like and vote for the politicians they like. It is hard to control for this possible spurious relationship in candidate data (as opposed to voter data), but our analysis of Twitter s interaction effect helps a great deal. The positive direct effect of Twitter even if the candidate hardly used the medium suggests that these voters were indeed already convinced who to vote for before they started following the candidate. However, the result that the positive effect of having followers increases by a more intense use of Twitter suggests that politicians can actually convince citizens to vote for them by using social media. Regarding the external validity, this study includes all 493 major party candidates, but is nevertheless a one-country study. The chosen country, the Netherlands, is a rather representative case of a list PR system when it comes to social media and preferential voting. Surely, the Netherlands has one of the most proportional electoral systems, but regarding its ballot structure, the semi-open list system, it is a typical case (cf. supra). Most established democracies, and many European ones in particular, use such a list-proportional electoral system. As such, it is likely that our findings can be generalized to other list-pr established democracies. To sum up, it is too early to call our findings ironclad, but they are nevertheless suggestive. Without a doubt, the coefficients will be different in other countries, but it is not unlikely that the general effects will be found elsewhere as well. Follow-up research studying the effects of social media could include different elections from different countries and many more candidates in a multilevel setting in order to test the validity of our results and get a better grip on the interaction between the electoral system type and social media effects. If social media become 26 Moreover, as grassroots campaigning is virtually nonexistent in the Netherlands, the country is an excellent case to study the impact of social media in the absence of grassroots campaigning.

17 Polit Behav (2014) 36: omnipresent in the future, this might lead to simply being present and active having a weaker effect. On the other hand, the type of usage might then become much more important to differentiate oneself from other candidates, which brings us to the new questions raised by this study. As touched upon earlier, it has been stressed by some that it is the message conveyed through social media that is important (Crawford 2009; Wilson 2009); Based on other studies one might put more emphasis on the maintenance of a relationship with the voters (Grimmer, Messing and Westwood 2012). This seems the most important avenue for future research, which should thus focus on what candidates send out, study the content of the messages, and examine what exactly it is that convinces followers. A consultant-driven approach to social media whereby lackeys place updates that are merely generic and informational are unlikely to ignite a fire in the hearts of their followers (Wilson 2009). A second content element that may matter is the interactivity. Do candidates who listen and respond to voters messages do a better job? Barack Obama famously ignored his followers in that his campaign did not reply to them (Crawford 2009, p. 530), but it may well be that others would not get away with this so easily. As our results strongly suggest that voters appreciate attention in the form of being kept up to date, one might expect that interactivity is even more appreciated. A second cluster of suggestions for future research focuses on who receives the message and what they do with it. Our analyses showed that social media probably have a two-step effect. Future research could thus look at how opinion leaders and grassroots activists use social media information to convince people to vote for a given candidate (cf. Jungherr 2012). A second group of people that may have a high impact are journalists. Quite often journalists are among the most active consumers of social media. Especially Twitter fits journalists needs because of its format of short messages and their quotability. Hence, it may well be that social media are actually a new door into an old house, or a means of getting into the old mass media, which in turn may increase the media coverage and number of votes a candidate receives. Acknowledgments We explicitly want to thank Reinout de Vries and Jonneke Stans from EMMA Communicatie for their generous help and for providing us with data on Twitter usage. We also want to thank the participants of the 2012 ECPR Joint Session workshop Parties and Campaigning in the Digital Era, and Laura Sudulich, Rachel Gibson and Andrea Römmele in particular for their useful comments. Appendix See Tables 5 and 6.

18 232 Polit Behav (2014) 36: Table 5 Descriptives Variable Additional information N Min. Max. Mean SD Hyves followers (10009) Hyves use Twitter followers (10009) Date of retrieval: 8/6/ Number of Hyves updates (26/5/2010 to 2/6/2010) Date of retrieval: 11/4/2011* Twitter use Number of tweets (27/4/2010 to 8/6/ ) # Journalists Date of retrieval: 8/6/ Incumbency MP or cabinet member ( ) Well-known Expert assessment (1 = Yes)** # Newspaper articles (1) # Newspaper articles (2) Personal website Campaign experience Articles including name candidate (27/ 04/2009 to 26/04/2010) Articles including name candidate (27/ 4/2010 to 8/6/2010) Personal website (1 = Yes) Presence on list 2006 national elections (1 = Yes) Position list Position of candidate on list End of list (1 = Yes) List-puller First candidate on list (1 = Yes) First woman Highest ranked woman (1 = Yes) Woman (1 = Woman) Ethnic nonwhite (1 = Yes) First ethnic nonwhite Valid N (listwise) Highest ranked ethnic non-white candidate (1 = Yes) Note 1 Since the number of Twitter followers is measured after the campaign, some candidates gained prominence after the elections (e.g. because they became Junior Minister). As a result they most likely have more followers than they had during the campaign. Based on analyses of media coverage in the months between the election and the measurement, we expect that this has only led to a substantial increase of followers for eight candidates. More importantly, because they became well known after the elections, this measurement error can only be expected to lead to weaker relationships in other words: conservative estimates. As the number of followers is overestimated, but the number of preferential votes is not, the regression coefficients are biased downwards. If anything our results thus underestimate the real coefficients 493

19 Polit Behav (2014) 36: Table 6 Coefficients of instrumental variables that go with the models in Table 4 Model 1: controls Model 2: direct effect Hyves and Twitter Model 3a: interaction effect Hyves Coef. Coef. Coef. Coef. Model 3b: interaction effect Twitter PVDA 3,481** 3,234** 1,677 a CDA 2,376* 2,095 3,655* 931 SP 1,278 1,219 1, PvdD ,116 GroenLinks SGP CU ,589 D66 2,095 1,682 1, VVD 4,122*** 4,041*** 4,37*** 514 PVDA*listpuller 117,190*** 239,596*** 168,992*** 1,391,630*** CDA*listpuller -452,458*** -590,634*** -510,657*** 827,372*** SP*listpuller -467,264*** -364,122*** -535,451*** 765,452*** PvdD*listpuller -1,182,578*** -1,092,213*** -1,240,730*** 72,177*** Gl*listpuller -1,007,983*** -919,368*** -1,238,936*** 212,371*** SGP*listpuller -1,090,244*** -987,988*** -1,113,765*** 136,828*** CU*listpuller -744,596*** -686,247*** -776,865*** 459,147*** D66*listpuller -785,372*** -703,342*** -876,548*** 427,666*** VVD*listpuller 258,468*** 357,791*** 300,427*** 1,523,109*** * p \ 0.05; ** p \ 0.01; *** p \ Notes The reference group for the party dummies is PVV (Party for Freedom); a due to extreme multicollinearity, model 3b did not include PVDA, in other words the effects of this variables have been completely captured by the other variables, because only people with Hyves or Twitter accounts are used in these models References Andeweg, R. B. (2005). The Netherlands: the sanctity of proportionality. In M. Gallagher & P. Mitchell (Eds.), The Politics of electoral systems (pp ). Oxford: Oxford University Press. Andeweg, R. B., & Irwin, G. (2005). Governance and politics of the Netherlands (2nd ed.). Houndmills: Palgrave MacMillan. Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., et al. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415),

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