The determinants and dynamics of Twitter-based interactions among ...

The determinants and dynamics of Twitter-based interactions among candidates Michaël Boireau Matteo Gagliolo Emilie van Haute Laura Sudulich (Universite libre de Bruxelles)

Abstract The case of the May 2014 Belgian elections offers a unique opportunity to investigate campaign behaviors. We explore interactions between candidates on Twitter in the run up to the elections of three different legislative bodies: the federal, regional and European assemblies. We define interactions based on the flow of retweets and conversations (@) initiated by candidates. In so doing we capture dynamic interactions or networks, as opposed to a more static definition based on followers. These multilayer elections enable us to map the positions of candidates, explore whether Twitter based networks go beyond the language divide (French/Flemish) and if ideological boundaries characterize candidates ‘interactions. Interactions are analysed using Ucinet. Finally, we explore the extent to which visibility within the network of candidates is fully explained by candidates’ offline profiles.

Paper prepared for the Workshop ‘Digital Media, Power, and Democracy in Election Campaigns’, Washington, DC (July 2-3, 2015)

1. Introduction In the mushrooming of campaign studies, a large number of contributions has tackled the integration of information and communication technologies in the electoral practices of parties (Gibson and Rommele 2009; Rommele 2003), voters, and candidates (for a review see Gibson 2012; Gibson et al 2014). We speak to this literature, by contributing to the latter topic, focusing specifically on candidates’ behavior on the Twittersphere. Twitter has attracted various strands of research, from forecasting, to public mood mapping (Murthy 2013; for an extensive review on Twitter and politics see: Jungherr 2014). Electoral studies have explored the Twitter activity of individual candidates, and have looked at both its determinants (Suh et al 2010; Conover et al 2011) and its effects (Spierings and Jacobs 2014). Other studies have analyzed the content of communication among citizens and between the public and political elites by conducting content and discourse analyses of the tweets (Small 2011; Hosch-Dayican et al 2013; Grimmer and Stewart 2013). However, Twitter is above all a social networking tool, connecting networks of actors in asymmetric relationships: an agent A can follow an agent B, without this relationship being necessarily reciprocal. Therefore, analyzing networks of individuals and unveiling the structure of relationships between actors is the most straightforward use of Twitter-based data. Precisely, the large amount of data produced by this medium, available and quantifiable, is particularly well suited to map and explain interactions among networks of individuals. We chart and test theoretically grounded hypotheses on the interactions among candidates on Twitter in the run up to elections. Specifically, we explore the extent to which campaign interactions among candidates are defined by ideological proximity or whether they happen across a wide range of ideological positions. Moreover, by mapping action and reaction within the population of wired candidates we assess prominence and influence. In so doing, we contribute to the literature on campaign studies by exploring communication between a large number of candidates running for three different elections. We disentangle nuances of cross party communication by means of network analysis and assess their substantial meaning. The 2014 Belgian elections, involving a variety of ideologically diverging parties, two parallel party systems and three simultaneous electoral context offer an ideal case to explore candidates’ communication and campaign dynamics. We find that political parties remain a major reference during electoral campaigns and best explain interactions among candidates, who mostly interact with their co-partisans. However,

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we find differences across interactions’ types: retweets show a homogeneous pattern pointing at party cohesion and self-promotion as the norm – candidates RT fellow candidates from the same party; Replies and conversations on the other hand, involve cross-party addresses, showing less party segregation. Yet, these interactions mainly occur within the same ideological area, candidates ‘proximity on the left-right axis define the range of interactions: the debate mainly occurs among candidates from closely competing parties. The paper is organized as follows. In the next section we outline our theoretical expectations and review the relevant literature. We then describe the electoral context/s. In section 4 we describe data and methods. Section 5 reports descriptive statistics while section 6 show the results from our analytical approach. We conclude with a brief discussion of the implication of our findings.

2. Polarization and Normalization in Online Electoral Campaigns Interactions among candidates during electoral campaigns can be looked at from an aggregate or individual perspective. We look at both sides of the coin, by raising two sub-questions: At the aggregate level, which features structure the interactions among a large number of candidates from various parties? At the individual level, which factors determine the position of individuals in these interactions? With regard to the first question, we are interested in mapping whether specific patterns can be found in the interactions among candidates; specifically we expect them to be clustered around certain characteristics, above all, ideological proximity. Individuals, by natural inclination, tend to relate with other likeminded individuals. The online world makes no exception to this tendency toward homophily (McPherson et al. 2001), which has been observed also for political orientation (Himelboim et al 2013, Gruzd et al 2014): social media users tend to follow other users that have similar political orientations. The algorithms used by social networking platforms to recommend other accounts to follow can further exacerbate this tendency to homophily and segregation (Bucher 2012). A large corpus of studies has documented homophily within political networks on Twitter (for a review see: Ackland and Shorish 2014). Candidates’ online interactions should therefore make no exception. At the same time, Twitter is designed to inform, share and debate. Furthermore, electoral campaigns represent the ideal place where to engage voters, confront other parties and candidates. Therefore, candidates may adopt a broadcasting as well as a narrowcasting

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approach in their online communication strategy. When it comes to interactions with competitors – from the same party or from other parties – they can shape them in a more or less polarized fashion (Gruzd and Roy 2014). We explore whether candidates’ interactions are guided by ideological considerations by testing two competing hypotheses. On the one hand, one could argue that candidates belong to political parties and that, as members of the party community, they share a certain number of ideas and values that are reinforced via party socialization processes. Therefore, we should reasonably expect that candidates would primarily interact with other candidates from the same party or ideological family, and that social interactions progressively diminish the further away the other candidate is located on the ideological scale (H1a: political polarization hypothesis). On the other hand, we are looking at social interactions during electoral campaigns. Compared to other periods in political cycles, campaigns are expected to be the time when parties and candidates get out of their ideological niche, exchange ideas across the political spectrum, and engage in proper debates with other parties and candidates. Therefore, we also test the competing hypothesis that candidates interact with other candidates and engage in communication across party and ideological legacy (H1b: political debate hypothesis). In doing so, we build up on the arguments of Conover et al (2010), Yardi and Boyd (2010), and Gruzd and Roy (2014). Further to this, we address the determinants of individual candidates' positions in these networks of interactions. Twitter differs from other social networking tools on one central aspect: it is asymmetric by default. Relationships ought not to be – and are often not reciprocal. We know that not all individuals in a social network are equal. Some occupy more prominent positions than others. Similarly, not all candidates in an election are equal. We explore whether the position of individual candidates in their online interactions mirrors real word campaign dynamics or significantly diverge from them. Since Margolis and Resnisk’s (2000) seminal study, a myriad of contributions have posed the question of whether the internet reflects the structure of power of society. Particularly, in relation to electoral campaign this would mean finding traditionally strong candidates in position of prominence in the Twittersphere. The opposite argument – technology offering visibility to the less powerful actors – has gained certain credit and provided the basis for cyber-enthusiasm. We address the issue by exploring the position of individual candidates in the network with an eye on the indicators that would point to ‘politics as usual’ (H2: normalization hypothesis) such as prominence of frontrunners and incumbents. If these signs are not consistently detected, some

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reasonable doubts could be cast on the normalization hypothesis, which we maintain as the most likely scenario. The alternative scenario could be explained in terms of technological exceptionalism: prominence in the online sphere represents an exception and is explained in terms of ‘digital attributes’ more than in terms of real-world characteristics.

3. The electoral context/s

Belgium, and especially the last elections, constitutes an ideal case to test our hypotheses for a number of reasons. First, Belgian parties operate in separate electoral arenas based on the linguistic divide (French-speaking vs. Dutch-speaking). Therefore, one could consider that two separate party systems operate on its territory, only overlapping in Brussels (Deschouwer 2009). This enables us to test our hypotheses on two parallel systems, effectively producing comparative outputs. Second, it allows testing the polarization hypothesis on a parliamentary system characterized by high level of fragmentation and centrality of parties as political actors (Deschouwer, 2002). The Belgian party system reflects a crosscutting cleavage structure that generated a highly fragmented party system (or two fragmented party systems) (Delwit, 2012). In the second half of the 19th century, the denominational cleavage gave birth to the Christian Democrats and the Liberals, whereas the socio-economic cleavage gave rise to the Socialists. After WWII, the centre-periphery cleavage led to the emergence of the ethno-regionalist parties, but also the split of the three traditional party families along the linguistic divide (Deschouwer, 2009). In the 1980s, the development of new politics favoured the rise of the Greens and the extreme right. Today, each party family (Christian Democrats, Socialists, Liberals, Greens and Extreme Right) has its sister party on the other side of the linguistic border, but the strength and the electoral fate of either sibling vary across the linguistic divide, and they compete in separate electoral arenas (with the exception of Brussels and part of its suburbs). After the 2014 elections, no less than 13 parties were represented in the federal parliament. The distribution of these parties on the left-right axis is presented in Table 1. They range quite widely, which constitutes an ideal opportunity to test how the logics of polarization reflect in the interactions between candidates during the campaign.

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Table Error! No sequence specified.. Average score of voters on the Left-Right axis, Belgium 2014 (Flanders + Wallonia) Party family Extreme Right Regionalists Liberals Christian Democrats Socialists Extreme Left Greens

Party acronym - L-R score Party acronym - L-R score Flanders (0-10) Wallonia (0-10) VB 7.38 N-VA 6.61 FDF 5.76 Open VLD 5.91 MR 6.79 CD&V 5.94 CDH 5.73 SP.A PvdA+ Groen

4.87 4.72 4.66

PS PTB-GO Ecolo

5.72 5.25 4.18

Source: Partirep voter survey 2014 – www.partirep.eu

Third, the 2014 elections were a triple synchronized election, where voters had to elect representatives for the European parliament, the federal parliament (national level), and the three regional parliaments (Brussels, Flanders and Wallonia)1. No less than 8,088 candidates were running for these three levels across the country, in the two separate party systems (Table 2). This provides us with a large population of agents and three simultaneous electoral context taking place at once. Table 2. Number of candidates by level of election – Belgium, 2014 elections Level Europe Federal Region – Brussels Region – Flanders – Flemish speaking Region – Wallonia – French speaking Community – German-speaking Total

Number of candidates 336 2,841 1,175 1,802 1,781 153 8,088

Finally, Belgium uses proportional representation and a semi-open list system where voters can opt to vote for the list as a whole, which indicates that they validate the order on the list as proposed by the party (party vote), or they cast a preference vote for one or several candidates (preference vote - as many as there are candidates on the list). Candidates have different incentives and most likely behave differently during the campaign depending on their position

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Some voters also had to elect representatives for the German-speaking community parliament. The 153 German-speaking candidates (Table 2) displayed very low activity levels on Twitter during the campaign (0 replies, 1 retweet – see Table 3). Therefore, these candidates were excluded from the analyses. The parliaments of the other two Communities (Flemish-speaking and French-speaking) are indirectly composed by representatives of the regional parliaments.

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on the list and chances of success. This characteristic of the electoral mechanism creates a context where the system of preferences stimulates competition and the list order defines a hierarchy: we explore what of these two opposite stimuli prevails in determining visibility in the Twitter-sphere.

4. Data and Methods Our dataset is based on data collected via the Twitter Streaming API2, which allows to capture and filter Twitter traffic in real-time, based on its account of origin, or on keywords included in the text. The software was implemented in Python, using the open source library Tweepy3. The filter we used consisted of a list of 1999 accounts – to which we henceforth refer as our followees - that were identified as belonging to candidates or other relevant campaign actors (politicians not running for election, parties, journalists, etc.). The statuses were stored in their original JSON format, including all metadata (e.g. timestamp, URLs of links and pictures, and the unique identifier of each user involved, as author or mention). We restricted the analysis to tweets written in either Dutch, French, or English, sent during the three weeks preceding election day (May 25th)4, by one of our followees, and mentioning at least another one. In short, the filtered data only contains instances of direct communication among two or more of our candidates. These filters allow focusing on social interactions among candidates and media during the campaign. This left us with 1,614 valid candidates Twitter accounts5. We look at two types of interactions. Generally speaking, a mention consists of the "@" symbol followed by the username of the target user, and it can be either included manually by the author of the status, or automatically by the Twitter interface in case of a reply or a retweet. We can consider the status containing the mention to be a form of directed communication from the author to the mentioned user(s), as the target user is automatically notified. However, mentions can take two very different forms of communication that should be interpreted differently: replies and retweets.

2

https://dev.Twitter.com/streaming/overview

3

http://www.tweepy.org

4

This time window covers the period going from the 4th to the 24th of May 2014 (one day before the Election Day). 5

A handful of candidates have duplicated accounts.

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In a retweet, the author diffuses the tweet of another user to her/his own followers, automatically notifying the author of the original tweet, and the other users it mentioned. It occurs if an individual chooses to echo someone else's tweet through the retweet function. 2014-05-04 T05:24:22 openvldvrouwen (Party):

RT @ALDEParty: ALDE candidate

@GuyVerhofstadt (Candidate, Liberal openVLD, European level) the 'clear winner' of first #EUdebate2014 http://t.co/SEzDsNRQUZ #ivoteliberal #EP2014 http:/…

A reply is made when someone comments someone else's tweet via the reply function of the interface. It is directed to the author of the original tweet, but it includes by default a mention for each user mentioned in the original tweet. It is then the equivalent of a conversation and can involve several people. 2014-05-05 T21:33:07 CapiauxAlain (Candidate, Right-wing populist PP, Regional level): @Isabelle_Durant (Candidate, Greens Ecolo, European level) Your Europe works so well that you’re responsible for a FN at 25, and UKIP at 30 #riseofpopulism 2014-05-05 T21:51:18

Isabelle_Durant @CapiauxAlain: What about co-responsibility?

National politics would have nothing to do with it? Austerity = Choice of National heads of state, elected. (authors’ translation)

To highlight these differences, we systematically present results for two networks: the network of replies and the network of retweets, and we describe them separately. While focusing on candidates who effectively interacted with each other during the campaign (3 weeks), these amount to 799 in the replies network ( 3,798 edges), and 558 in the retweets network (2,237 edges), with no duplicates included6. Table 3 provides the basic characteristics of these interactions. They are predominantly initiated by male, Dutch-speaking candidates to the federal or regional level, from (center)-right parties.

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The networks were aggregated with the package ‘tnet’ for R (Opsahl 2012).

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Table 3. Frequencies of replies and retweets by Party, Level, Language and Gender

Party (Flanders: Dutch-speaking Belgium)

Party (Wallonia: French-speaking Belgium)

Level

Language community Gender

Extreme Right – FL (VB) Populist Liberal – FL (LDD) Regionalist – FL (N-VA) Liberal – FL (OpenVLD) Christian Democrat –FL (CD&V) Socialist – FL (sp.a) Green – FL (Groen) Extreme Left – FL (PVDA+) Populist Liberal – FR (PP) Liberal – FR (MR) Regionalist – FR (FDF) Christian Democrat – FR (cdH) Socialist – FR (PS) Green – FR (Ecolo) Extreme Left – FR (PTBGO) Green – FR (VEGA) Brussels Parliament Federal Parliament European Parliament Flemish Parliament Walloon Parliament German speaking com. Dutch speakers French speakers German speakers Female Male

# Replies N % 183 8.2

# Retweets N % 568 15.0

3

0.1

0.0

372 295

16.6 426 13.2 570

11.2 15.0

438

19.6 521

13.7

160 210

7.1 9.4

177 389

4.7 10.2

79

3.5

228

6.0

154 42 15

6.9 1.9 0.7

10 104 12

0.3 2.7 0.3

23

1.0

53

1.4

82 130

3.7 5.8

178 184

4.7 4.8

51

2.3

373

9.8

0 182 673 192 985 205 0 1740 497 0 520 1717

0 8.1 30.1 8.6 44.0 9.2 0.0 77.8 22.2 0.0 23.3 76.7

5 270 1328 632 1400 167 1 2879 918 1 1152 2646

0.1 7.1 35.0 16.6 36.9 4.4 0.0 75.8 24.2 0.0 30.3 69.7

0.0

We use the replies and retweets to build aggregate networks of candidates. The resulting aggregated networks are directed and weighted (Wasserman and Faust 1994). Concretely, it means that each observed link brings two pieces of information: first its direction from one node to another and second the number of interactions (the weight) taking place in that direction during the observational period. The networks have been built as follows: each node corresponds to one of our followees, and a directed link of weight n is drawn from user A to user B if user A sent n tweets containing a mention of user B during the time window considered. Note that, in this way, in the replies network, a single tweet can contribute to

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more than one link, one for each mention of a user. The replies network is built considering only tweets sent in reply to other tweets, including both tweets where A replies to B, and tweets where A replies to a different user, but mentions B in the reply. The retweet network is obtained by considering only tweets in which user A retweets a status of user B. Finally, as mentioned in the previous section, Belgian politics is heavily divided between language communities, up to the point that it is often considered that parties operate in separate electoral arenas based on the linguistic divide (French-speaking vs. Dutch-speaking). The mapping of the Twitter interactions among candidates provides a crystal clear picture of this language divide (Figure 1)7. Language is the candidates’ attribute that defines the most their social interactions, both in the retweet and in the replies network. It confirms that two separate, hermetic systems co-exist in parallel. Language differences are reinforced by the rules of the game (electoral system), so much so that candidates have no incentives to interact across the linguistic divide during the campaign.

Figure Error! No sequence specified.. Replies network by language

Note : The Retweets network displays a very similar pattern Color guide for language Dutch French

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All network figures were realised with ‘NetDraw’ (Borgatti 2002).

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Therefore, next to presenting separate analyses for the network of replies and the network of retweets, we also produce separate analyses for the two language communities. This enables us to test our hypotheses on two systems, effectively producing comparative outputs. . 5. Analyses a) Polarization On the basis of our first hypothesis, we investigate if the replies and retweets networks are built around one main attribute: party affiliation, both in French-speaking Belgium and in Flanders. We compare with gender, constituency and type of elections.

In order to verify this, we performed two separate sets of tests. First, we used the E-I index to evaluate homophily in the networks on these attributes (Krackhardt and Stern 1988). The E-I index takes its values between -1 and 1, where -1 means that nodes do connect only with those sharing the same features on the attribute of interest, and 1 means that nodes only associate with those having different values on it. For every E-I index obtained, a permutation test was performed and indicated whether the value was statistically significant. The permutation test generates randomly hundreds of networks with the same number of nodes and attributes, and then checks whether the association between existence of a link and similar attribute is statistically significant in the observed network (p < 0.05).

The results are partially consistent with our expectations (Table 4). Party affiliation produces a very high degree of homophily only in the retweet network, while it is almost inexistent in the replies networks. This confirms that these two networks capture different interactions. It means that candidates almost exclusively use the retweets to echo a fellow party affiliate’s ideas. Conversely, they use the reply function to interact much more broadly with candidates from various parties (see below).

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Table 4. Test for Homophily (E-I index) of the two subset networks (Dutch- and Frenchspeaking candidates)

Party Level of election Constituency Gender

Dutch-speaking network Replies Retweets -0.136 * -0.906 * 0.118 0.131 0.060 0.048 -0.217 * -0.163 *

French-speaking network Replies Retweets -0.012 * -0.903 * 0.036 0.092 0.141 0.158 -0.149 -0.087

Note : E-I index ranges from -1 (perfect homophily) to 1 (perfect heterophily)*: p < 0.05

To illustrate this, Figure 2 and 3 presents the retweets and replies networks for Flanders. The pictures for French-speaking Belgium are very similar and available upon request.

Figure 2 - Replies Network of Dutch-speaking Candidates

Error! No sequence specified.

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Figure 3. Retweets Networks of Dutch-speaking Candidates

CD&V OpenVld sp.a Groen! PVDA+ Dutch-speaking N-VA Lijst Dedecker Vlaams Belang

Figure 3 clearly shows that party affiliation produces almost perfect homophily in the retweets network. However, the replies networks depict a very different scenario (Figure 2) where the trend towards homophily is significant yet close to zero. The other notable element emerging from the permutation test is that gender also slightly influences replies and retweets (Table 4), with interactions occurring more frequently between candidates of the same gender. However, the gender attribute dictates social interactions among candidates to a much lesser extent than party affiliation (in the case of the retweets network only). Lastly, the level of election and the constituency where the candidate runs actually showed positive E-I indices (Table 4), i.e. heterophily. Besides, these two variables were not

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statistically significant after the tests. Candidates do not seem to interact based on these attributes. From what seen in the figures above, the retweets networks support the idea of political polarization, while the replies network shows less party-defined interactions among candidates. However, this suggest only that the reply network is not party structured, but ideological proximity may still be the most defining trait, if not defined by party belonging.

To disentangle this suggestive pattern to a greater extent we performed a second set of tests. We used a Multidimensional Scaling Solution (MDS - Mair et al. 2015) in order to highlight potential polarization at play. In a two-dimensional left-right scale, polarization is captured by the distance between two individuals: the greater the distance between the position of two individuals on the scale, the larger the degree of polarization between them. Our MDS solution is based on the same principle, applied to a three-dimensional space. MDS is a technique designed for investigating similarities in the data through a geometric graphical representation. It is based on a distance matrix that translates dissimilarities between all pairs of observations. Its diagonal is composed of zeros, since the distance between an observation and itself is of course null. This matrix furthermore assumes symmetry across the diagonal to allow the spatial representation. Here we based the distance matrix on the geodesic distance between all pairs of nodes observed in the reply and retweet networks (i.e. the interactions between candidates). The geodesic distance of each interaction is the number of edges that separate any pair of nodes. Since we examine directed networks, the geodesic distance is not necessarily the same for the path that runs from node “a” to node “b” and its reverse. Indeed, a Twitter user can address another user's account without getting any answer. The identical logic applies for retweets: one can retweet someone else's messages without having one’s own messages retweeted in return. Therefore, it could be that a path leads from node “a” to node “b” but that no path exists the other way around. On that basis, the retweets and replies networks were first dichotomized because aggregated networks – as are those analyzed here – have weighted edges where the values are the number of interactions that took place on a determined time window. Dichotomy and equal value edges were necessary here to translate the idea of distance. Secondly, the related distance matrices were calculated and symmetrized by taking the average distance between two nodes. When there was no path from one node to another, the maximal distance noticed in the

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network added to one unit became the given value. The stress or discrepancy between the spatial representation and these distances proved to be lower when opting for a 3D representation. As a result, the two following solutions8 are 3D plots calculated for, respectively, the distances in the replies network and those in the retweets network (Figures 4 and 5). The dots refer to the Dutch speaking candidates, and the colors to their political parties (same colors as in Figure ). Figure 4. 3D spatial representation of the candidates based on the distances in the Dutch speaking retweets network

Figure Error! No sequence specified.. 3D spatial representation of the candidates based on the distances in the Dutch speaking replies network distances

8

The 3D plots were realised with ‘Scatterplot3d’ (Ligges and Mächler 2003).

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Figure 5. 3D spatial representation of the candidates based on the distances in the Dutch speaking replies network

The color clustering effect is much more prominent in the retweets network (Figure 4) than in the replies network (Figure 5). In the former, the core is made of clearly visible clusters of candidates running for the same party, and who stand very close to each other. As the distance grows and candidates get plotted to the periphery, they seemingly keep being part of the same “pole”. This confirms what we observed with our first tests of homophily. On the contrary, the plot of the candidates in the replies network (Figure 4) is much more difficult to interpret, and patterns are uneasy to distinguish. To get a measurement of this, we run a cluster analysis (k-means) based on every candidate’s coordinates in the spatial solutions. K-means clustering finds as many groups of observations as it is stipulated beforehand. We set the number of clusters to be found to the number of parties (#8). We then test for significance the cluster assigned to every candidate with the corresponding party in a contingency table. While the coefficients are significant in both cases (χ²= 78.92 p < 0.01 in the Replies distances and χ²=538.87 p < 0.001* in the Retweets), the dependence between parties and clusters is much more perceptible in the retweets network than in the replies network. Overall, our various tests provide mixed evidence for our hypotheses. Candidates tend to interact primarily with candidates from their own party in the retweet networks, which confirms the political polarization hypothesis (H1a). The reply networks do not show a

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similarly enough significant pattern suggesting that the dialog is somewhat going beyond mere party belonging. While homophily is almost perfect in the retweets networks in both language communities (i.e. above -0.9), their replies networks are only marginally homophile. These results suggest that Twitter is has a twofold function: it is a support and socialization tool via the retweet function, and something else – including a debating/arguing tool via the reply function.

b) Normalization In this section, we turn to an individual level analysis to unveil what determines the position of individual candidates in these interactions. More specifically, we explore whether campaign dynamics in the Twittersphere diverge in any possible sense from overall campaign patterns, by looking at visibility in the network. Networks are composed of individual candidates, that do not occupy equal positions in politics and, it’s reasonable to suspect that existing inequalities in the offline political sphere will be reflected in their position in the online network of social interactions. More specifically, we explore the position of individual candidates in the network with an eye on the indicators that would point to ‘politics as usual’ (H2: normalization hypothesis). Closeness is a measure of centrality based on the average distance of a node vis-à-vis all other nodes in the network. The shorter its average distance is, the higher a node will score on its closeness. There are several ways to calculate it. Here, the method retained takes the sum of the reciprocal of the shortest path, in number of edges, from one node to every single other. If there is no path running between two nodes, then it adds zero to the sum since the reciprocal of an infinite distance is by convention null (Opsahl et al., 2010). As far as the direction of the edges matters, incoming ties are here considered for the calculation of closeness which then translates how much a candidate lies at the centre of attention by the others. Those Twitter accounts getting more attention – no matter whether in terms of replies or retweets - should logically score higher as per their closeness. Therefore we take closeness here as a proxy for the importance of the candidates in the replies and retweets networks.

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In order to test this hypothesis, we combine ‘real world’ attributes of the candidates with a measure of Twitter specific experience/proficiency: the number of tweets ever sent from their account at the beginning of the campaign (t0). While we dispose of several other measures accounting for twitter activity we opt for this particular indicator because it is not immediately related to our dependent variables – which are computed on the basis of the activities taking place in the three weeks of campaign. Moreover, in order to keep a parsimonious model – given the relatively low N- we abstain from loading it with extra indicators. This variable alone should detect the effects of experience/proficiency in the Twittersphere, if this element does play a role. The indicators of candidates’ placement in the real world are: political experience (coded as 1 if the candidate was a public representative in any of the 3 levels at the time and 0 otherwise), position on the list operationalized by setting apart list pullers from the rest. We control also for the type of office one was running for (EP, Regional, Federal) labelled election type, party affiliation (coefficients from party dummies are suppressed from the output, but available upon request) and whether the candidate was effective or suppleant. Finally we account for age and gender. Given the zero inflated nature of the dependent variables me make use of a Two-Part Model (TPM), running first a probit setting apart zeros from positive values and, conditional on the probability of a non zero outcome, a follow up linear regression explaining variation among positive values. In other words, this enable us to explain the differences between those who are completely isolated and those that display some levels of connections as well as explaining variation in the extent to which a candidate is connected to others. The results of these models are presented in Table 5.

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Table 5. Determinants of closeness in the Reply Networks (Model 1-4) and in the Tetweets Networks (5-8) (4)

glm

(3) NL replies probit

0.554** (0.256) 0.013 (0.012)

0.103 (0.145) 0.013* (0.007)

Candidate Effective

-0.171

List Puller Previous Experience

Gender Age

Twitter Statuses Count t0

(1) FR replies probit

(2)

(6)

glm

(5) FR retweets probit

(8)

glm

(7) NL retweets probit

0.484*** (0.172) -0.022** (0.009)

0.071 (0.062) 0.002 (0.003)

0.450** (0.193) -0.011 (0.010)

0.009 (0.145) 0.003 (0.007)

0.169 (0.121) -0.008 (0.006)

0.027 (0.035) -0.001 (0.002)

-0.087

0.475***

0.073

0.239

-0.006

0.279**

0.134***

(0.288) 0.318 (0.271)

(0.156) 0.262** (0.128)

(0.181) 0.628*** (0.213)

(0.071) 0.206*** (0.058)

(0.218) 0.365 (0.233)

(0.144) 0.300** (0.145)

(0.130) 0.673*** (0.175)

(0.041) 0.201*** (0.037)

0.223

0.073

0.506**

0.060

0.468*

-0.150

0.579***

-0.027

(0.282)

(0.131)

(0.225)

(0.064)

(0.253)

(0.151)

(0.172)

(0.043)

0.000**

0.000

0.000**

0.000***

0.000

0.000**

0.000***

0.000***

glm

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

Election Type

0.481**

0.054

0.223*

0.005

0.035

0.081

0.071

0.065***

Constant

(0.207) -1.527** (0.678)

(0.085) -1.022 (7.701)

(0.126) 0.598 (0.409)

(0.039) 3.708*** (0.143)

(0.155) 0.472 (0.500)

(0.094) 2.485*** (0.325)

(0.091) -0.209 (0.305)

(0.025) 4.389*** (0.086)

300

217

137

576

379

Yes

Yes

Yes

Yes

Yes

Observations 153 106 381 Party Yes Yes Yes dummies * : p < 0.05 - ** : p < 0.01 - *** : p < 0.001

The models above show results for the Replies network for the Francophone sample (1-2) and for the Flemish sample (3-4) as well as outputs for the Retweets network in the two languages (FR- 5-6; NL 7-8). To simplify the interpretation of the TPMs, we compute the corresponding marginal effects, reported in Table 7, where columns 9-10 present marginal effects for the French-speaking networks of Replies and Retweets, and columns 11-12 for the Flemish ones.

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Table 6. Combined Marginal Effects from Probit and GLM.

Gender Age Candidate Effective List Puller Previous Experience Twitter Statuses Count t0 Election Type Observations

(9) FR Replies

(10) NL Replies

(11) FR Retweets

(12) NL Retweets

2.806* (1.492) 0.159** (0.077) -1.346 (1.671) 3.364** (1.426) 1.403 (1.485) 0.001*** (0.000) 2.124** (0.983) 153

9.090*** (3.322) -0.179 (0.171) 9.049** (3.691) 16.603*** (3.488) 8.869** (3.853) 0.002*** (0.001) 3.013 (2.271) 381

1.614 (1.213) -0.018 (0.060) 0.779 (1.280) 3.433*** (1.321) 0.529 (1.381) 0.001*** (0.000) 0.704 (0.867) 217

4.913 (3.080) -0.206 (0.154) 12.298*** (3.345) 23.893*** (3.975) 11.225*** (4.130) 0.005*** (0.001) 4.595** (2.274) 576

Two common factors across the four models are playing a role: the highly significant effect of the baseline Twitter activity, whose small size is determined by the large values attained by this variable, and the candidates' position on the list. Those who tweeted more before the campaign and have therefore higher familiarity with the platform are more likely to be prominent, central actors on Twitter during the campaign itself. List pullers are more likely to display high levels of connectivity and the effects are particularly pronounced in the Retweets networks for both the Francophone and Dutch-speaking community where a change from position number one in the list to a lower ranking produces a -16 points change in the dependent variable (French-speaking candidates); this figure is even higher (-23) in the Flemish network. The level of election is a strong agent of high connectivity in the Dutchspeaking networks (both Retweets and Replies), with Federal and EP candidates showing higher connectivity than their counterparts at the regional level. Political experience only plays a significant role in the Flemish community, whereas in the French-speaking community there is no significant difference between incumbents and challengers. The Replies networks (both Francophone and Flemish) are highly gender biased men are much more likely to display high degrees of centrality in the Dutch network, but that applies, although to a smaller extent, in the French one as well. Women occupy clearly less central positions in online twitter debates, but their position do not differ from their male counterparts when it comes to Retweets. There may be some hierarchy at play here, where woman

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candidates play the loyal, promotional game in the Retweets network, but are not occupying the same position when it comes to Replies and debates.

All in all, real world attributes play a major role in determining how visible a candidate is in the Twitter-sphere. Nevertheless, an effect of familiarity with the medium is present. This leads us to conclude that it is mostly a case of normalization, but familiarity with Twitter is certainly an asset. List pullers are more prominent in both linguistic communities, whereas being an incumbent and an effective candidate is only an asset in the Flemish Twitter-sphere.

7. Discussion and conclusion In this paper, we have sought to unveil how the structure of candidates’ interactions reflects and conditions campaign communication. More specifically, we have looked at whether candidates’ interactions reflect the ideological spectrum in which they operate. In that respect, our results show that Twitter allows candidates to articulate their communication flow to fulfil two different functions. On the one hand, they use the retweet function to strengthen their bond with other candidates from their own party, creating an online socialization sphere that reinforces the circulation of their shared ideas and values, validating the polarization hypothesis. On the other hand, they use the reply function to debate and discuss topics with other candidates from other parties. They still tend to do this more with candidates from parties closer to theirs in the ideological spectrum. Yet, this definitely fits with the campaign hypothesis. Lastly, beyond the determinants of the structure of the online Twitter network, we investigated whether the online network reflects offline politics, and especially the status of individual candidates to find that this is mostly the case. Real world attributes matter, particularly position on the list. List pullers are significantly more prominent than the rest, however the role of political experience and demographic characteristics are not systematically good predictors. Twitter-specific proficiency seems to play a clear role in defining every network. Overall, we have shown that social interactions among candidates on Twitter are structured according to language group and party, and respond to certain characteristics of the individual candidates. We have tested our hypotheses on two parallel systems, which show a certain

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degree of consistency and attribute greater external validity to our findings. All in all, the snapshot from the Belgian elections of 2014, shows what found elsewhere with regard to the prevalence of homofily and normalization. However, a number of elements – the weak degree of homophily in the Reply networks and the significant role played by Twitter experience in explaining individual visibility – suggest that Twitter based communication may respond also to logics that do not necessary map offline characteristics.

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