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6 August 2021

Bridging the Gap: How Gender Influences Spanish Politicians’ Activity on Twitter

and
Department of Communication, University of Pompeu Fabra, 08002 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Journal. Media2021, 2(3), 469-483;https://doi.org/10.3390/journalmedia2030028
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Abstract

Women have historically been underrepresented in politics. However, in the last few decades, more and more women have been elected to both upper and lower houses, particularly in Spain. Social media has become one >of the main channels for women to gain visibility, but the issue of unequal distribution of power and influence between men and women remains. This paper sheds light on gender differences among politicians on Twitter by analyzing the social media activity and influence of 277 of the 350 Members of the Spanish Congress of Deputies from March to June 2020. Our research shows there are still major gender differences regarding audience figures and amplification and that both male and female politicians still largely retweet more men than women. In addition, we found significant differences between parties and across the political spectrum, although these are less prominent (albeit not neutralized) in parties with a female leader. This is in keeping with studies that have found broad similarities between male and female politicians’ communicative practices, but a persistently large gap to be bridged in terms of their online influence. Female leaders are proposed as a means to bridge this gap.

1. Introduction

1.1. Gender Differences in Political Power and Influence, and Underrepresentation and Empowerment in Politics

Politics, like many other areas of human activity, has traditionally been male-dominated territory. Female representation in democratic parties, congresses, senates and powerful political offices has only increased in the last few decades (Elder 2020; Bridgewater and Nagel 2020). However, research has shown that the situation is still far from balanced. Although women have populated parties, local governments and parliaments, various studies have revealed that sexism in the culture of political parties tends to favor male candidates on the ballot, to systematically disempower women (Verge and Troupel 2011; Verge and de la Fuente 2014) and to hamper women’s access to powerful political offices (Lovenduski 2005; Verge 2010). According to Verge and Wiesehomeier (2019), such discrimination runs across all parties, and parity is still a long way off, even in the most representative democracies.
Spain is a particular case of a country in which women have been historically underrepresented in politics (Fernández and Eugenia 2008). In 1977, two years after dictator Francisco Franco’s death, the constituent legislature had 21 female Members (5.8% of the total). This number barely increased in the first legislature in 1979, in which a mere 24 out of 350 Members were women, none of whom held any significant office in government. By the terms of 1989 and 1993, the proportion of female Members in the Spanish Congress had reached a meagre 10%. However, in 1996, almost a hundred women (23.9%) were elected in the first People’s Party government. In 2007 a new Equality Law came into force, requiring political parties to ensure minimum gender representation of 40% in candidates running for office (Verge 2010). This helped to balance the male-dominated political culture (see Table 1) visible in Spanish politics since the transition to democracy (Valiente 2008; Verge 2012). Nevertheless, Verge and Wiesehomeier (2019) argue that discrimination did not suddenly disappear with the 2007 quota. Although quotas tend to balance gender representation, other barriers to women in the political sphere, such as having to conform to male norms (Verge and de la Fuente 2014, p. 71), cause many women to relinquish certain offices (Verge 2015). Notwithstanding the ongoing inequality in Spanish politics, the number of elected women has increased in the last decade, and Spain now ranks sixteenth in the world in terms of women’s representation in parliament (Verge and Wiesehomeier 2019).
Table 1. Percentage of women in Spanish Congress in 2019 per party *.
The aim of this research is to analyze gender differences in Twitter use among Spanish members of parliament. To do so, we gathered all tweets from 277 of the 350 Members of the Spanish Congress from March to June 2020. We measured four variables related to their overall Twitter use: number of tweets, mean number of followers (audience), number of retweets (amplification), and efficacy. In addition, we measured the number of times that Members were retweeted by fellow party members (internal amplification), which can be linked to the internal communication strategies of the parties analyzed.

1.2. Communicating for Influence and Visibility

One way that women can increase their visibility in society is to garner media coverage. Representation in the media allows women to normalize their role in politics while also allowing for an impact on the political agenda (Kreiss 2016) as well as to articulate policy positions (Sobieraj et al. 2020). However, the media have traditionally under- or misrepresented women (Wasburn and Wasburn 2011; Sánchez Calero et al. 2013; Lünenborg and Maier 2015; Larson 2001; Fernández García 2013; Guerrero-Solé 2018; Dunaway et al. 2013). Currently, social networks are at the heart of all political communications strategies (Usher et al. 2018). Politicians the world over utilize social media, not only during electoral campaigns, but also for everyday communications (Graham et al. 2016). Politicians’ use of social media is a strategic form of publicity (Kreiss 2016; Cervi and Roca 2017; Casero-Ripollés et al. 2020; Guerrero-Solé and Lluís 2017; Guerrero-Solé and López-González 2019). As a consequence, politicians’ influence is no longer estimated exclusively on the basis of their coverage in traditional media, but also on their popularity on social networks, where follower numbers, shares, retweets and likes are the measure of their success. Politicians’ activity on social networks is also considered to be a driver for media attention (Rauchfleisch and Metag 2020; Graham et al. 2016). Social media activity is therefore a priority for female politicians, particularly given that research has shown they receive less media attention (Miller and Peake 2013; Baitinger 2015; Tromble and Koole 2020) and more negative coverage (Armstrong and Gao 2011; Ross et al. 2013; Larson 2001) than their male counterparts. McGregor and Mourão (2016) hold that women are more central to the conversation about them and about their opponents than men; this indicates that their connections in social networks are stronger. Various studies suggest that women having more visibility on social networks and communicating directly with citizens (Loiseau and Nowacka 2015; Vergeer 2015) can help to redress this.

1.3. The Role of Gender and Party on Twitter

Twitter has become one of the main tools that politicians use to complement their traditional communication strategies (Jungherr and Schoen 2013; Vergeer et al. 2013; Jungherr 2014). But what role does gender play in female politicians’ activity and influence on Twitter? Gender research into social media focuses mainly on two areas: harassment of women on social networks and the differing topics that men and women talk about. With regard to the former, the results to date are inconclusive and culture-specific. Some researchers have concluded that female politicians face more negativity on social media than traditional media (Conroy et al. 2015) and are more likely to be the target of hate speech (Wilhelm and Joeckel 2018) or uncivil tweets questioning their positions as politicians (Southern and Harmer 2019). On the other hand, Tromble and Koole (2020) found that in the UK, US and the Netherlands, gendered insults are infrequent. In relation to the second area, past research has found that female politicians tend to talk more about issues that predominantly affect women (Pearson and Dancey 2011). Moreover, although there are only minor gender differences in communication styles in some cases (Hrbková and Macková 2020), in general gender and party have an effect on what women tweet about (Hemphill et al. 2020; Johnstonbaugh 2020; Evans and Clark 2016).
In addition to harassment and styles of communication, research has also been carried out on the following: the gendered distribution of relational power in network discussions (McGregor and Mourão 2016); different patterns of liking practices; support of issues and civic engagement (Brandtzaeg 2017); self-presentation on social networks (Cook 2016); gender stereotypes of politicians online (Beltran et al. 2020; Wagner et al. 2017).
However, few studies have focused on gender and party differences in politicians’ number of tweets, size of audience, amplification and efficacy. We believe that this analysis can offer significant insight into the extent to which Twitter evens out any such hypothetical differences between men and women. Consequently, our first research question is as follows:
  • RQ1: Are there gender differences among Spanish Members with regard to number of tweets, audience, amplification and efficacy on Twitter?
As we have already mentioned, gender is not the only variable that might explain differences between politicians. Party membership can also be a predictor of politicians’ activity and influence in online environments (Johnstonbaugh 2020).
Therefore, the second research question is
  • RQ2: Are there party differences among Spanish Members with regard to number of tweets, audience, amplification and efficacy on Twitter? Are there differences between left- and right-wing parties?
In Spain, left-wing parties have strived to achieve gender equality (Uribe Otalora 2013). Therefore, male–female internal amplification can be a measure of how much attention fellow Members pay their female and male colleagues and whether they are equally likely to retweet them. Thus, the third research question is
  • RQ3: Is the amplification rate among female and male Spanish Members balanced?

2. Sample and Method

To answer the aforementioned research questions, we gathered all tweets, replies and retweets that Spanish Members posted on Twitter from 14 March to 19 June 2020. This period coincides with the COVID-19 state of alarm in Spain. The sample included 277 out of the 350 Members of the fourteenth legislature, of whom 44% were women and 56% men, from the parties shown in Table 2. They collectively posted 249,874 tweets and retweets in the three months, with an individual minimum of 2 and maximum of 7767 posts.
Table 2. Breakdown of Spanish Members by party.

2.1. Independent Variables

We coded for the following independent variables:
  • Gender: gender of the Member (male = 151, female = 126).
  • Political party: political party of the Member (see Table 1).
  • Political leaning: political leaning (left or right) of the Member’s party (left = 135, right = 121, independent = 21).

2.2. Dependent Variables

The dependent variables were defined as follows:
  • Amount: number of tweets and replies that each Member posted in the period analyzed (min = 0; max = 3045; mean = 259; SD = 341).
  • Amplification: number of times each Member was retweeted during the period (min = 0; max = 1,427,478; mean = 38,412; SD = 128,870).
  • Audience: mean number of each Member’s followers during the period (min = 137; max = 1,351,574; mean = 38,270; SD = 136,211).
  • Efficacy: defined as amplification divided by amount and audience (min = 0; max = 209.55; mean = 6.86; SD = 14.06).
  • Internal amplification: proportion of retweets by fellow Members from the same party.

3. Results

To answer research question one, we first calculated the mean values of the dependent variables: amount, amplification, audience and efficacy. Table 3 shows the mean values by gender of these variables. We performed ANOVA tests to evaluate the statistical significance of the differences between genders.
Table 3. Mean values of amount of tweets, number of followers and efficacy of Spanish Members on Twitter by gender.
To answer research question two, we calculated the mean values of the dependent variables for each of the seven main parties in the Spanish Congress of Deputies. First, we analyzed the differences in tweet amount, amplification, efficacy and audience (Table 4). As above, we performed ANOVA tests for statistical differences. The results are also shown in Figure 1.
Table 4. Mean amount, amplification, efficacy and audience of Spanish Members by gender and party.
Figure 1. Mean amount, amplification, efficacy and audience of Spanish Members by gender and party.
The second part of RQ2 aimed to ascertain differences between left- and right-wing parties in Spain. For this purpose, we labelled UP and PSOE Members as ‘left-wing’ and Cs, PP and Vox Members as ‘right-wing’. We calculated the mean scores of the dependent variables: amount, amplification, audience and efficacy (Table 5).
Table 5. Mean amount, amplification, audience and efficacy by political spectrum (left/right).
Whereas the first two questions were related to the general Twitter activity of the Spanish Members, and amplification was defined as how many times they were retweeted overall, RQ3 explored how often Members retweeted posts published by fellow party Members (internal amplification). Table 6 below shows the gender breakdown of intra-party retweets.
Table 6. Mean internal amplification of women and men, standard deviation, and significance by gender of the retweeter.
As Table 7 shows, male politicians retweet other male politicians twice as much as they retweet female politicians. Furthermore, female politicians also retweet male politicians more frequently, although the difference is slightly smaller.
Table 7. Internal amplification (IA) among Spanish Members by party.
We performed a detailed analysis of the internal amplification strategies of men and women by party and gender (Table 8) and found that, in all cases, both men and women retweeted more tweets from men than from women. We performed a t-Test for paired samples and found that in the ruling party PSOE, the right-wing PP, and the far-right party Vox, the gender differences were highly significant.
Table 8. Internal amplification (IA) of women and men by party and gender.

4. Discussion

Research has shown that women have historically been discriminated against in politics. Unequal distribution of political positions and responsibilities coupled with women’s underrepresentation in parliaments have driven the need for gender quotas (Verge 2010; Verge and de la Fuente 2014). This has resulted in significantly more women in parties and governments than in the past. However, parity is still a long way off, particularly due to the underlying androcentric political culture in some countries. Spain has been no exception when it comes to a gender imbalance in politics, and women have achieved increased visibility and power only in the last decade. The media have often spearheaded this shift, and today social media is one way that enables women to increase their presence, power and visibility. However, the issue of equality remains.
Our research analyzed the extent to which male and female Members of the Spanish Congress are equally influential in terms of content amount, amplification, audience and efficacy on Twitter, one of the most widely used social networks for political communications in Spain. The results show that there are few overall gender differences when it comes to number of tweets. We found that male and female Members are equally active on Twitter, which is in tune with the reported increase in women’s visibility on social networks (Loiseau and Nowacka 2015; Vergeer 2015). Our results also echo previous studies that have found minor differences in candidate online campaigning coverage (Tromble and Koole 2020) and reveal Spanish female politicians’ effort to be as active and influential on social networks as men. However, we found major disparities in the amplification of tweets (men are retweeted twice as many times as women) and audience (men have more than double the audiences of women). Nevertheless, most of these differences were not statistically significant due to the skewed distribution of variables (see Table A1 in Appendix A for the scores of variables for each Member).
When we broke down the analysis by party, the only considerable gender difference in amount of tweets was in the female-led Catalan party JxCat (women tweeted three times more than men) and the populist far-right party Vox (men tweeted twice as much as women). With regard to the other variables analyzed, we found that gender differences in amplification were notable, in particular in UP and ERC. In all parties except the right-wing PP, women were less amplified on the network than men. These results are in tune with previous research on the interaction of party and gender stereotypes on politicians’ effectiveness when they use Twitter (Holman et al. 2011). There were also stark differences in audience in the female-led party Cs. Finally, we found statistically significant differences between men and women in efficacy in ERC and UP. While the UP male Members’ efficacy was significantly greater than the women’s, in ERC, women had almost three times the efficacy of their male counterparts. The case of UP is significant because it defines itself as a feminist party and has clearly feminist policies. However, as the overall results show, women remain a minority in the male-dominated political sphere.
Statistically significant differences emerged when we grouped parties by ideological leaning. The right-wing parties Cs, PP and Vox were far more active than left-wing parties UP and PSOE. The same was true of amplification and efficacy, although the differences were lesser (p < 0.05). Amplification in right-wing parties was three times greater than in left-wing parties; efficacy was twice as high, and mean audience was almost half. In short, the right-wing parties, currently in the opposition, were far more active, had a greater impact on the network, and were much more efficient than the ruling left-wing parties. These results suggest that party and ideological leaning are better predictors of differences than gender in content amount, amplification and efficacy.
However, the most relevant and interesting results of this research are for internal amplification according to political party. We found that in all seven parties analyzed, internal amplification of men was substantially larger (broadly double) than of their female counterparts. Moreover, in five parties this difference was statistically significant. Earlier research found a sexist and discriminatory culture in most parties that favors male candidates on ballots, systematically disempowers women (Verge and Troupel 2011; Verge and de la Fuente 2014) and hampers women’s access to relevant political positions (Lovenduski 2005; Verge 2010). It is interesting to note that the two parties in which gender differences in internal amplification were not statistically significant (JxCat and Cs) were both led by a woman. It is therefore possible to conclude that having a female leader, i.e., allowing women to access relevant political positions, may balance out differences in internal amplification.
The results are similar when we look at internal amplification by gender. Women internally amplify more men than women, although the differences are only statistically significant in the ruling party PSOE and the far-right party Vox. Men also retweet more male than female fellow party members. Again, all of the differences observed are significant except for JxCat and Cs, the two parties in the Spanish Congress of Deputies with female leaders. We can therefore conclude that women are broadly discriminated against in the internal communications strategies of political parties in Spain on Twitter, especially in the case of women who are discriminated against by male party colleagues. This discrimination is not related to the party’s position on the political spectrum and is only neutralized in female-led parties. These results confirm previous findings that show that Twitter is far from being a public sphere in which gender inequalities are eliminated (Hu and Kearney 2020).
Finally, it is worth mentioning that this research was performed with a sample of tweets collected during the first COVID-19 state of alarm in Spain. There is evidence that contexts with heightened states of national security threat—and the COVID-19 outbreak may be considered such a case—can activate preferences for male politicians (Holman et al. 2011). Consequently, new research is needed in the future to support and generalize the conclusions of our work.

Author Contributions

Conceptualization: F.G.-S. and C.P.-G.; Data curation: F.G.-S.; Investigation: F.G.-S.; Methodology: F.G.-S. and C.P.-G.; Supervision: C.P.-G.; Validation: F.G.-S. and C.P.-G.; Writing—original draft: F.G.-S.; Writing—review & editing: F.G.-S. and C.P.-G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the MCIU/AEI/FEDER, UE under Grant PGC2018-097352-A-I00.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Twitter data of the 277 Spanish Members in the sample.
Table A1. Twitter data of the 277 Spanish Members in the sample.
GenderPartyTwitter HandleFollowersActivity RetweetsRT Times
FCsinesarrimadas638,783476249168,040
FCsmariadelamiel45,465107116349,758
FCsmartamartirio10,2422672194716,115
FCsmcmartinez_cs10945212883259
FCssaragimnez47332841186958
MCsbaledmundo28,27980262374,017
MCsguillermodiazcs15,0152790139165,040
MCsmarcosdequinto62,08751571138,377
MCspaucambronerocs63332281193328,526
FERCbassamontse11,445168858286
FERCcaroltelechea26065932531
FERCinesgranollers987149812022065
FERCmartarosiq16,7753111844390
FERCnormapujol2373433365400
FERCpilarvallugera1044369322265
FERC_maria_dantas_5346471038065344
MERCcapdevilajoan8306152813981668
MERCgabrielrufian754,24913341145343,089
MERCjoanmargall4346142011513225
MERCjsalvadorduch71094323041538
MERCnuet23,466261221245654
MERCxavieritja35559147631302
FJxCATconceptermens939370314694
FJxCATlauraborras114,44870333988154,404
FJxCATmarionaid1105742648684
FJxCATmiriamnoguerasm95,3281587136362,417
MJxCATferran_bel76988755156943
MJxCATgenisboadella24984163261859
MJxCATjacs_jaumeacs144,823823293238,307
MJxCATsergimiquel63652931254221
FPPabeltran_ana812137420429,416
FPPaliciagarcia_av4603114171610,699
FPPanadebande12,2284131319659,218
FPPanapastorjulian106,286730417133,732
FPPanazurita734496455193584
FPPauxipd14621801800
FPPbealinuesa1553336160454
FPPbea_fanjul59,436735306580,147
FPPbelenhoyo11,0175934194462
FPPborrego_corte1679923914143
FPPcarmenriolobos6262372130293534
FPPcarolinaespanar319244944096
FPPcayetanaat212,899319163389,637
FPPcnlacoba1901223140547
FPPcucagamarra10,32859536214,384
FPPedurneuriarte21,9672132462,997
FPPllanosdeluna673202182494
FPPmargaprohens57433211264310,416
FPPmariaramallov433513017
FPPmartaglezvzqz78413221374
FPPmdelaoredondo28416015415
FPPmilamarcos203313041051951
FPPmoromjesus3796246322492118
FPPpalomagazquez2098481647683307
FPPpilarmarcosd4660250520768433
FPProsaromerocr957198827813,764
FPPsolcruzguzman21456834751310
FPPtejerinapp218032320
FPPteresajbecerril664532411920,190
FPPtristanamg2593414407109
FPPvalentinam32273171256975
MPPaalmodobar409313936424131
MPPaglezterol19,43243116035,821
MPPalbertocasero214712941173715
MPPandreslorite382283249510,697
MPPcarlosrojas_ppa5440132112333064
MPPcelsodelgadoou124014112628
MPPdiegogagob75564343413219
MPPdiegomovellan16216966391481
MPPeducarazo29676283532445
MPPeloysuarezl47864511492669
MPPgmariscalanaya60654394121075
MPPherrerobono483017136826
MPPhispanpablo8552714164
MPPjacallejascano629307136558
MPPjaimedeolano13,4562682231730,005
MPPjangelvillalon2032283207569
MPPjavierbasco3322362310
MPPjavier_merino2463397259638
MPPjiechaniz314657411827,000
MPPjosemiguel_glez37968644
MPPjspostigo65643040331
MPPjuan_pedreno17542225
MPPluisstamaria4174339273509
MPPmapaniagua4532189831300
MPPmariogarcessan4339172507477
MPPmcastellonpp1523346232243
MPPmiqueljerez1617656607651
MPPmontesinospablo40,42053735721,305
MPPoscarclavell29054832211
MPPoscargamazo170215281174679
MPPotazu3569610979371533
MPPpablocasado_423,738760292562,173
MPPpedronavarrol2299168613061492
MPPquin195423828266212
MPPsanchezcesar857515583748
MPPsebastianlede1569111461055119
MPPtcabcas1080635561689
MPPteogarciaegea61,518434214123,982
MPPvicentebetoret53996274782953
MPPvicentetiradopp1548647063601083
MPPvicpiriz197555278764437693
FPSOEadrilastra81,460566465107,381
FPSOEafernb12,97292434425,213
FPSOEanaprietonieto8163375328958084
FPSOEangelesmarra959536301251
FPSOEariagonagp52222144
FPSOEbeamcarrillo2611413299821
FPSOEbeatrizcorredor13,2699678201172
FPSOEbegonasarre2332882574755
FPSOEbelenfcasero17757016311278
FPSOEbelitagl760854796289
FPSOEcaballerohelena57715371144363
FPSOEcarmenandres_3912323267346
FPSOEcarmencalvo_66,86332527217,196
FPSOEcelaaisabel35,9231443316,063
FPSOEelviraramon3471236521091612
FPSOEestherpadillar3271571454796
FPSOEestherpcamarero4015303170866
FPSOEevabravobarco7318533406
FPSOEevapatriciab6499251220
FPSOEfuensantalima266715881093957
FPSOEgraciacanales356318211462
FPSOEhernanzsofia4698397360452
FPSOElauraberja863240123810304283
FPSOElidiaguinart462610376344492
FPSOEluisacarcedo10,31087535028
FPSOEluzseijo71843571787110
FPSOEmaraluisavilch118029427716
FPSOEmarina_ortega_11405853664564
FPSOEmaritxu30181012114513
FPSOEmarotoreyes13,9335563887636
FPSOEmarrodanmaria8165434
FPSOEmerceperea5605202814984281
FPSOEmeritxell_batet49,52358324311,290
FPSOEmjmonteroc41,1201131311
FPSOEmontseminguez40196094751025
FPSOEmsolsj2866200716772099
FPSOEmvalerio_gu21,1997917642414
FPSOEnvillagrasa1284304188346
FPSOEolgaalonso6215555139874
FPSOEpatri_blanquer16443332541161
FPSOEpilicancela624313466738824
FPSOErafi_crespin282714176207
FPSOEsandrage76102823972183827
FPSOEsoniafetesoro2786532041
FPSOEssumelzo21,6874413981222
FPSOEsusana_ros63865284351685
FPSOEtamarayar184630929391
FPSOEteresaribera44,32943222911,675
FPSOEzaidacantera35,3181923130336,460
MPSOEabalosmeco70,83640318561,864
MPSOEalejandrosolerm34127982081939
MPSOEalfonsocendon272614708127486
MPSOEantidiofagundez254440
MPSOEapabellas1631180
MPSOEarandapaco3521155212183413
MPSOEarnauramirez71345143246093
MPSOEasanchog1371461442
MPSOEastro_duque522,9841594129,125
MPSOEcesarjramos91205981874246
MPSOEconjosemfranco7058173116625525
MPSOEdioufluc170843242590
MPSOEfelipe_sicilia10,47237230618,655
MPSOEfranciscopolo24,769709642657
MPSOEgermanrenau120018988354
MPSOEgomezdcelis94632301228309
MPSOEguillermomeijon27957655971415
MPSOEhectorgomezh53703322503720
MPSOEjavieranton1438330311116
MPSOEjaviercerqueir4252317125578
MPSOEjavizqui51407795945678
MPSOEjccampm61062671412381
MPSOEjcduran_5957362353192
MPSOEjfrserrano35369025411508
MPSOEjlaceves244328892696907
MPSOEjoseantoniojun405,09457341745,650
MPSOEjosluisramosro22351311300
MPSOEjruizcarbonell10,373262235187
MPSOEjuanb04623861461379
MPSOEjuanluissotoadd2409651437552
MPSOEj_zaragoza_48,56741811158,209
MPSOElcsahuquillo126255550
MPSOEluisplanas13,1763281596938
MPSOEmarclamua3097280228591
MPSOEmigonzalezcaba1542458337645
MPSOEmontimar6614815228450
MPSOEmorissiero127311589541513
MPSOEnasholop546278049911,024
MPSOEodonelorza201156,853111819228,813
MPSOEpabloaranguena24285195115,358
MPSOEpatxilopez195,80923416213,409
MPSOEpedrosaurag5536118100109
MPSOEpedro_casares706199653817,958
MPSOEperejoanpons412311816341097
MPSOEpmklose15,558183791313,581
MPSOEsalazarropaco554351837511,369
MPSOEsanchezcastejon1,351,574628332284,959
MPSOEsanticl4070159137888
MPSOEsarrimorell18532312223
MPSOEsergio_gp79787856272055
MPSOEsimancasrafael25,69565450044,649
MPSOEvalentingarciag4665802590447
MPSOEviondi65351890571103,622
FUPainavs14,6555163227825
FUPantonia_jover_126015649216
FUPgagupilar35692541023803
FUPgloriaelizo20,05674249821,838
FUPionebelarra69,25220414120,034
FUP isabel_franco_13,44740720414,110
FUPlauralopezd2343163105659
FUPluciadalda22084233662616
FUPmargpuig5328227871458
FUPmaria_podemos18917152707168
FUPmarisasaavedram16235012792104
FUPmartinavelardeg45286812642563
FUProser_maestro300813187515
FUPsofcastanon27,59067725623,745
FUPveranoelia48,083142938404
FUP vickyrosell83,69960727959,418
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