1. Introduction
In Spain, the expanding role of social media—and Twitter in particular—in political activism and campaigning has been linked to the rise of the Los Indignados anti-austerity movement that erupted in 2011 and the emergence of new political parties that have fractured the country’s bipartisan democratic history (Ramos-Serrano, Fernández-Gómez, and Piñeda 2018, 127). This period also witnessed a de-centring of traditional media (television, print newspapers) and electoral programmes as primary sources of information for the electorate, as voters increasingly began to rely on social media. A recent study by Kennedy and Prat places Twitter among the top fifteen news sources in Spain (Kennedy and Prat 2019, 49). Voters who desire a more interactive and real-time discussion of political and electoral issues turn to Twitter—a “medium of immediacy” (Johnson 2012, 57)—to see how parties respond continuously to the flow of current events, engage their opponents, and speak directly to their audiences on a daily basis. Not only does the shift from television, newspaper, and electoral programmes to tweets represent a shift in medium, it also entails a change in parties’ rhetorical and communications strategies and the electorate’s reception of campaign messaging that urges scholarly consideration. As Janet Johnson argues, in exchange for widespread visibility and greater campaign reach, politicians must learn to adapt their rhetoric to the digital stage, especially on platforms like Twitter that require concise presentation of ideas (Johnson 2012, 55). Given this relatively new form of political campaigning and political engagement, the questions of how Spanish parties address their constituents within Twitter-mediated elections and what methodological approaches are best suited to analyze parties’ campaign messaging online merit attention.
In response to these questions and in order to understand the relationships and differences between each party’s rhetoric and style of tweets, their real-time responses to current events, and ideological stance on key electoral issues, this article employs various Natural Language Processing (NLP) techniques—namely word and hashtag frequency and sentiment analyses—to analyze tweets posted by the five most popular Spanish political parties in 2019—the Partido Socialista Obrero Español (Spanish Socialist Workers’ Party, shorthand PSOE), Partido Popular (People’s Party, shorthand PP), Ciudadanos (Citizens, shorthand Cs), Podemos (meaning “We Can”), and Vox—before, during, and in the immediate aftermath of the April 28, 2019, General Elections.
Previous studies of Spanish political Twitter discourse indicate that parties’ adaptive strategies for digital communications have varied and tend to find that parties do not always take advantage of the interactive and communicative capacities of Twitter (see Zamora-Medina and Zurutuza-Muñoz 2014; López-García 2016; and Ramos-Serrano, Fernández-Gómez, and Piñeda 2018). However, these studies have stopped short of examining the stylistic and rhetorical strategies that parties use to communicate their positions digitally through the stream of abbreviated messages on Twitter. In their study of European political party campaigns, Crabtree et al. (2018) note, “[w]hereas campaign content and campaign focus address what parties say and who they say it about, campaign sentiment addresses how they say it” (Crabtree et al. 2018, 1). A focus on rhetoric and style enables this study to address the inter-related nature of political language and affective sentiment and the ways in which they are used together to inform and persuade potential voters. As such, our analytical framework aims to examine how political rhetoric online functions as a discursive tactic “to produce action or change in the world” (Bitzer 1968, 4). This study builds on existing scholarship by proposing a combination of quantitative and qualitative methods to accurately parse and analyze political Twitter speech, heeding the call to investigate “how they say it” by evaluating the words parties use, the presence of positive and negative sentiment in their tweets, the use of platform affordances such as hashtags, and close readings to situate parties’ rhetorical strategies within their sociopolitical contexts.
To that end, this article begins with quantitative assessments—word frequency, co-occurrence (of qualified nouns), and sentiment analyses—of tweets published between March 1 and May 15, 2019, to gain a broad-level understanding of each party’s preoccupation with key electoral issues and rhetorical approaches to addressing said issues on Twitter. Grounded in the results of these distant readings of each party’s tweets, this analysis then narrows its focus by targeting and parsing tweets that contain frequently occurring terms within a specific topic category—in this case, gender. Gender provides an ideal frame for quantifying stylistic and rhetorical differences between parties given that the collection period for this study coincides with significant political discussion about gender-related issues in Spain.
An example of such was the renewed media attention in spring 2019 to a court case revolving around the gang rape of an 18-year-old woman during the San Fermín celebrations in Pamplona, Navarre, in 2016. The two legal categories which address sex without consent in Spain are “sexual abuse” and “sexual aggression,” the latter necessitating violence or intimidation, the parameters of which are extremely narrow and subject to interpretation. The perpetrators had been previously convicted of the lesser crime of sexual abuse instead of sexual aggression in 2018. This sentence exposed the failings of the Spanish legal system in addressing such cases and galvanized and enraged feminist groups. During the collection period of this study, the Spanish Supreme Court was deliberating a revised sentencing of these men. The perpetrators were finally reconvicted of “continuous sexual assault” in June 2019. This period also spans the #8M feminist strikes in Spain on March 8, 2019, in which gender and feminism were much-discussed topics in the cultural milieu, and points of conflict over which parties hashed out ideological and discursive conflicts.
During the #8M strike, an estimated 5 million Spanish women did not go to work or do any paid or unpaid labour to protest the wage gap. The strikers protested economic disparities, but also gender discrimination and sexualized violence, among other intersecting experiences of gendered oppression. Accordingly, our methodology also understands feminism and women’s rights to be intersectional issues that cannot be separated from other key political topics during the election cycle, including employment, immigration, and health care. In fact, gendered social relations structure the way these other economic and social issues play out in a material way, impacting the lives of all Spaniards. Focusing on gender enables this study to examine both how political parties frame questions of gender and feminism as well as how they situate gender in relation to other political concerns.
By combining quantitative and qualitative approaches, as opposed to one or the other, to analyzing the topical focus and affective tendencies of each party’s tweets, this study unearths a nuanced relationship between a party’s discursive strategies on Twitter and their position on and ways of engaging with key electoral issues and inter-party disputes that is grounded in both a distant and close reading of relevant tweets. Rhetorical differences between parties are most pronounced in topical contexts, such as gender-related discussions, where politicians resort to a variety of discursive mechanisms, such as word choice, sentiment, and hashtags, to distinguish their viewpoints from those of their political opponents. Using the Spanish General Elections of April 2019 as a case study, this article provides a model of how to leverage each of these methods to maximize one’s critical understanding of political Twitter and an in-depth study of the Twitter style and rhetorical strategies of Spain’s established and emergent parties when engaging with key and often contentious electoral issues.
1.1. Background
In the last five years, Spaniards have witnessed the fracturing of the historical political dominance of the PSOE and the PP, the two parties that held mostly uncontested control over the country since the 1977 elections that followed the end of the Franco regime. The ousting of the PP’s Mariano Rajoy and the brief administration of the PSOE’s Pedro Sánchez occurred in parallel with the rise of new political parties—namely the centre-right neoliberal Ciudadanos (founded in 2006), the far-right nationalist Vox (founded in 2013), and the left-wing populist Podemos (founded 2014). Between December 20, 2015, and November 10, 2019, Spaniards took to the polls to vote in General Elections no less than four times—a staggering number given the maximum four-year term permitted for Spanish Congress and Senate. The period between 2015 and 2019 was marked by political instability due to a series of failed attempts at coalition governments, allegations of corruption, and a no-confidence vote that culminated in the resignation of Prime Minister Rajoy in June 2018 and his succession by Sánchez. After merely eight months in office, Sánchez called a snap general election for April 2019, which resulted in his party losing the majority and creating a political deadlock with the left that sent Spain back to the polls on November 10, 2019, mere days before the completion of this study. The November 2019 elections are not discussed in this article, but the methods put forth herein could be easily applied to Twitter data from this period.
In Spain, the rise of social networks as a major platform for political mobilization coincided with the anti-austerity movements of 2011, a precedent to Occupy Wall Street (Castañeda 2012), following the global financial recession of 2008. During the General Elections of 2011, political leaders including Rajoy began using Twitter as a campaign tool for the first time in Spanish political history (Jivkova-Semova, Requeijo-Rey, and Padilla-Castillo 2017). Since then, the online presence of Spanish politicians, political parties, and political mobilizers has increased exponentially, as have digital readership and engagement by Spanish citizens. Figures 1 and 2 illustrate, respectively, each party’s increasing number of followers by year and the accumulated number of tweets, including retweets, posted each year by each party. Notably, the two most active party accounts on Twitter (in terms of published tweets) belong to two of the most recently formed parties, Podemos and Ciudadanos.
2. Data collection and methodology
Between March 1 and May 15, 2019, we collected 10,038 tweets posted by the Twitter accounts of the five major Spanish political parties: @PSOE, @Populares, @CiudadanosCs, @ahorapodemos, and @vox_es (counts are listed by party in Table 1). Twitter’s Application Programming Interface (API) offers both streaming and search services. The former allows for real-time monitoring of tweets based on predefined search criteria (e.g., account names, hashtags, keywords, or geographical areas). For this particular study, the streaming service was used to target and monitor discussions related to the 2019 General Elections. The latter search service facilitated the gathering of profile and timeline information from the five accounts. Collected data was then saved in a NoSQL database to streamline queries and future analysis, consisting primarily of NLP techniques for word and hashtag frequency analysis, the creation of co-occurrence networks of nouns and their qualifiers, and sentiment analysis. This database was also used to withdraw tweets for selected close readings. This study uses the Stanford CoreNLP tool given that it includes a robust Spanish language model, with an accuracy rate of 97% per token (words and punctuation symbols) and at least 55% per sentence (i.e., if only one token in a sentence is incorrectly classified, the whole sentence is marked as incorrectly tagged) (Manning 2011). NLP enables the extraction of the full sentences embedded in tweets as well as the words in each sentence. Single words and words within sentences are be tagged based on their parts-of-speech (POS) classification (nouns, qualitative nouns, verbs, and adjectives), while taking typographical and rhetorical devices such as punctuation into consideration. These classified terms can then be processed and visualized for further quantitative and qualitative examination.
Party | Twitter Account(s) | Number of Tweets | % of Total |
---|---|---|---|
Partido Popular (PP) | @ppopular @populares |
2831 (1180) | 28.20% |
Citizens (Ciudadanos) | @CiudadanosCs | 1912 (1034) | 19.05% |
Partido Socialista Obrero Español (PSOE) | @PSOE | 1904 (798) | 18.97% |
Vox | @vox_es | 1690 (1403) | 16.84% |
Podemos | @ahorapodemos | 1701 (801) | 16.94% |
Obs.: The figure in parentheses denotes the total number of original tweets originating from each party’s account, excluding retweets. On March 8, 2019, International Women’s Day, the PP changed its official Twitter handle from @ppopular to @populares. This study considers the data from both accounts as a single data point.
As this study aims to first provide a quantitative analysis of the rhetorical strategies used in tweets published by political parties, NPL techniques provide a number of methods to do so, including Latent Dirichlet Allocation for topic modelling, absolute and relative word frequency for general content analysis, and sentiment analysis for numerical measurements of positive and negative messaging within a given tweet.
A closer look at the parts of speech used most frequently by each political party in their tweets enabled us to identify central party concerns (nouns were especially indicative of these), recognize stylistic and rhetorical strategies (word choice and use of hashtags), and explore relationships between the speech, ideology, and campaign platforms of the leading political parties. After conducting an initial word frequency analysis, we created co-occurrence networks to examine how parties qualified nouns related to gender (mujer, mujeres, and feminismo, meaning woman, women, and feminism). Co-occurrence networks rely on matrices that organize selected words in relation to the context of their sentence. For this project, a set of pre- and manually selected target terms related to gender were used with the goal of understanding how different parties address the issue of women’s rights and how they view feminist movements.
In addition to parsing tweets for frequently used terms as a means of identifying key electoral issues, we conducted sentiment analysis to gauge the tone (positive, negative, or neutral) of the messages shared by each party. Sentiment analysis tools offer a means of processing relatively large amounts of text to reveal affective and/or emotive tendencies. Typically, this approach relies on the polarity of a positive/neutral/negative classification achieved through the application of lexicon-based approaches. For example, generally positive words, such as nice, good, and fabulous, are assigned higher sentiment scores, while commonly negative words, such as terrible, bad, and corrupt are given lower sentiment scores. Negation words, such as not, but, and however, are also taken into consideration when sentences are evaluated for sentiment. For this study, we chose Google’s Natural Language API, as it supports sentiment analysis in Spanish. As is typical for most sentiment analysis tools, Google’s API normalizes the sentiment score of a sentence within the range of –1.0 (negative) to +1.0 (positive).
The first step of the sentiment analysis was an assessment of all tweets posted from each party account. We then ran a more nuanced sentiment analysis of tweets containing key gender-related terms—specifically, mujer (woman), mujeres (women), feminismo (feminism), and feminista (feminist)—to better understand the relationship between differences in party sentiment and the rhetorical treatment of gender equality and feminism. Despite notable advances in sentiment analysis research in recent years, the complexity and nuances of human language make the task of sentiment analysis nonetheless challenging. With that said, sentiment analysis is useful when engaged as one of several analytical tools with which to assess political speech. To illustrate stylistic and rhetorical differences between parties, this study begins with word frequency analysis to identify topics of interest, constructs co-occurrence networks to examine the use of selected terms in context, and then turns to sentiment analysis to look at the emotive dimensions of party tweets. Each of these processes is accompanied by close readings that situate the quantitative results in context and elaborate on subtle differences in distant readings that point to significant variations in party rhetoric. The final portion of this article uses a word frequency analysis to identify frequently used hashtags to examine the use of these hashtags as discursive frames.
2.1. Ethics
While the content of this study involves cultural conversations around sexualized violence, we chose to focus exclusively on the tweets of official party accounts, with no reference to the tweets of individual users. Many users were engaging with several of the hashtags through self-disclosure of experiences of sexualized violence and use of this information in a study would necessitate careful and prolonged ethical consideration.
While the generally accepted best practice in ethical research involves verifying if tweets remain public at the time of publication, there is ongoing discussion in digitally engaged fields about how to navigate the complexities of online data harvesting. Jackson, Bailey, and Welles suggest that writing about individual users’ tweets, particularly “marginalized people who are not in the public eye … risks exposing them to unwanted and unanticipated attention” (Jackson, Bailey, and Welles 2020, xi). In their 2020 book, Jackson et al. detail the additional precautions they employed in the data collection process to address the tension between representing Twitter conversations and discourses accurately and honouring users’ right to privacy and ownership of their own data. For instance, they exclusively selected tweets that were not only public, but addressed to a larger community, excluding replies on individual user’s threads. To respect privacy, they also disqualified tweets from closed accounts, as well as tweets which had been subsequently deleted. Other scholars, such as Earhart, underline the importance of not abstracting data from its humanity, as it is “always a part of a community or individual” (Earhart 2018, 369). She asserts that without attention to issues of consent, ownership/control of data, and without situating oneself as a researcher, we can easily exploit communities with which we engage (Earhart 2018).
To ethically include individual user’s data, especially that which could be raw or personal, this study would have been designed differently. The goal of this work was to investigate political parties’ emerging strategies of digital communication, and we believe that within the scope of the project, we could not dedicate the deserved amount of proper attention and care to individual user’s data, and thus it is excluded from the study.
3. Word use: Identifying topics of interest
Words and hashtags are immediately searchable on Twitter. Using the Twitter search bar highlights the discursive differences indicated by word choice as users can enter substantially different streams of discourse by searching similar albeit different terms like nación (nation) and país (country). For this reason, word choice in political Twitter communication forms part of a deliberate rhetorical strategy with links to ideological stance, and these terminological differences are observed in parties’ Twitter discussions of major electoral issues, such as nationalism, feminism, and/or immigration, to name a few examples.
3.1. Overall word use
We structured our data to determine which terms appeared most frequently in party tweets and selected the top twenty nouns used by each party based on the assumption that nouns are better indicators of party issues and concerns than verbs and adjectives. We excluded proper nouns with the exception of España (Spain), the names of the five party leaders (Pedro Sánchez, Pablo Casado, Pablo Iglesias, Santiago Abascal, Albert Rivera), and the names of Spain’s autonomous regions and cities, and provincial capitals. Table 2 summarizes the top twenty most frequently used nouns by each party in absolute values, expressed in raw numerical count. The table also displays the relative frequency of each word in relation to the total number of words posted by each party, expressed as a percentage of total words. Examining the top words used by each party not only reveals their recurring concerns, giving us insight into each party’s electoral priorities, but even when party concerns demonstrate some overlap, a party’s word often reveals an implicit ideological stance on an issue. This demonstrates how parsing the language of tweets provides readers with insight into the relationship between a party’s ideology and word choice in Twitter campaign discourse.
Podemos | PSOE | PP | Ciudadanos | Vox | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Word | # | % | Word | # | % | Word | # | % | Word | # | % | Word | # | % |
gente | 271 | 3.7 | España | 463 | 5.5 | España | 529 | 4.4 | España | 484 | 5.6 | España | 283 | 4.9 |
país | 197 | 2.7 | derecha | 313 | 3.7 | gobierno | 361 | 3.0 | gobierno | 248 | 2.8 | acto | 176 | 3.0 |
gobierno | 180 | 2.5 | gobierno | 237 | 2.8 | presidente | 202 | 1.7 | igualdad | 204 | 2.3 | lista | 101 | 1.7 |
derecho | 138 | 1.9 | mujer | 202 | 2.4 | voto | 198 | 1.6 | libertad | 188 | 2.2 | congreso | 100 | 1.7 |
España | 138 | 1.9 | futuro | 196 | 2.3 | día | 195 | 1.6 | país | 173 | 2 | voto | 97 | 1.7 |
banco | 90 | 1.2 | país | 195 | 2.3 | empleo | 175 | 1.4 | español | 145 | 1.7 | Barcelona | 95 | 1.6 |
medio | 90 | 1.2 | año | 175 | 2.1 | impuesto | 160 | 1.3 | proyecto | 136 | 1.6 | español | 93 | 1.6 |
año | 88 | 1.2 | derecho | 144 | 1.7 | español | 153 | 1.3 | día | 122 | 1.4 | Madrid | 91 | 1.6 |
cambio | 86 | 1.2 | igualdad | 112 | 1.3 | eleccion | 150 | 1.2 | futuro | 118 | 1.4 | persona | 89 | 1.5 |
democracia | 80 | 1.1 | voto | 103 | 1.2 | año | 146 | 1.2 | año | 115 | 1.3 | mujer | 86 | 1.5 |
poder | 78 | 1.1 | proyecto | 94 | 1.1 | entrevista | 133 | 1.1 | mujer | 113 | 1.3 | historia | 81 | 1.4 |
cosa | 78 | 1.1 | acto | 89 | 1.1 | libertad | 132 | 1.1 | cambio | 103 | 1.2 | libertad | 80 | 1.4 |
voto | 76 | 1.0 | política | 84 | 1.0 | futuro | 131 | 1.1 | presidente | 101 | 1.2 | candidato | 76 | 1.3 |
día | 76 | 1.0 | mes | 83 | 1.0 | mujer | 112 | 0.9 | Madrid | 98 | 1.1 | miedo | 72 | 1.2 |
historia | 76 | 1.0 | mayoría | 83 | 1.0 | campaña | 106 | 0.9 | familia | 96 | 1.1 | día | 71 | 1.2 |
mujer | 75 | 1.0 | justicia | 80 | 0.9 | izquierda | 101 | 0.8 | Cataluña | 88 | 1.0 | campaña | 66 | 1.1 |
campaña | 73 | 1.0 | eleccion | 78 | 0.9 | Madrid | 98 | 0.8 | comunidad | 87 | 1.0 | presidente | 64 | 1.1 |
eleccion | 71 | 1.0 | medida | 71 | 0.8 | país | 96 | 0.8 | estado | 85 | 1.0 | eleccion | 61 | 1.0 |
verdad | 71 | 1.0 | presidente | 70 | 0.8 | Cataluña | 94 | 0.8 | política | 72 | 0.8 | medio | 60 | 1.0 |
vida | 69 | 1.0 | sociedad | 70 | 0.8 | candidato | 91 | 0.8 | voto | 71 | 0.8 | entrevista | 58 | 1.0 |
Obs.: listed words count for their singular and plural forms, when applicable.
Repeat occurrences of nouns across party accounts paint a picture of general electoral concerns, highlighting matters that all parties address in their tweets. For instance, España (Spain) appears within the top noun count for all five parties (see Table 2). This is to be expected given that these are national elections. Other topics (nouns) discussed by all parties include mujer (woman)—also unsurprising given that these elections take place shortly after the Women’s Strike on March 8—and voto (vote), although how each party engages with these matters varies. However, beyond identifying common electoral concerns, this study finds that a comparison of noun recurrence between parties also reveals differences in each party’s electoral priorities and, at times, suggests their stance on an issue.
In the case of Catalan neo-liberal party Ciudadanos, there is a high occurrence of terms associated with Catalonia (Cataluña, n = 88, 1.0%) and freedom (libertad, n = 188, 2.2%), reflecting the party’s neoliberal stance and opposition to Catalan separatism, in favour of a pro-Spanish, pro-European, and post-nationalist stance. In that of populist and self-proclaimed feminist party Podemos, the terms people (gente, n = 271, 3.7%), woman (mujer, n = 75, 1.0%), and feminism (feminismo, n = 47, 0.7%) recur often, highlighting its interest in discussing and appealing to “the people,” women, and feminists. As a point of contrast, the fiscally conservative right-of-centre Partido Popular discusses taxes (impuestos, n = 160, 1.3%) more often than any other party, as well as government (gobierno, n = 361, 3%) and the presidency (presidente, n = 202, 1.7%) more than any other topic besides Spain (España, n = 529, 4.4%). PSOE’s second-most-tweeted term after Spain (España, n = 463, 5.5%) is right (derecha, n = 313, 3.7%), followed by government (gobierno, n = 237, 2.8%) and woman (mujer, n = 202, 2.4%). This suggests that the party’s main electoral priorities include opposing the rise of arguably the biggest threat to its power, what it refers to as the right-wing trio (trío de derechas)—comprised of PP, Ciudadanos, and Vox—and women’s empowerment, rights, and freedoms. Finally, Vox extensively tweets the terms rally (acto, n = 176, 3%), a reference to its many campaign rallies, and list (lista, n = 101, 1.7%), a reference to the electoral lists of provincial congressional candidates. A relative newcomer to the political scene in Spain, Vox frequently uses nouns that demonstrate the party’s interest in self-promotion, generating a sense of community through numerous rallies, and introducing its provincial candidates to its voter base online. This contrasts with the incumbent leftist PSOE’s more defensive and oppositional stance towards the right, as it focuses on maintaining and defending its power and popularity, rather than trying to obtain them like Vox.
3.2. Word use and gender
While the recurrence of a noun—or electoral issue—suggests the degree to which a party is preoccupied with that topic, frequency of word use is not necessarily indicative of a party’s support for that issue. Our gender case study illustrates this point. For instance, neoliberal party Ciudadanos tweeted the term feminismo (feminism, n = 52, 0.6%) approximately as often as feminist parties Podemos (n = 47, 0.65%) and PSOE (n = 44, 0.52%). However, few are likely to argue that Ciudadanos is equally or more feminist than Podemos. If anything, Ciudadanos is criticized by feminists, journalists, and left-leaning political pundits for its neoliberal pseudofeminist discourse that envisions a feminismo liberal (liberal feminism), which advocates for women having the freedom and right to make decisions over their own bodies without proposing policy changes that will actually empower women, in the same vein that it argues for neoliberal, free-market, and capitalist economic policies. In the words of feminist activist, journalist, and author Cristina Fallarás, “El feminismo tiene que ser anticapitalista por definición porque el capitalismo es patriarchal” (“feminism must be anti-capitalist by definition because capitalism is patriarchal,” translation mine, quoted in Trobat 2019). So, although feminism is a recurrent theme in Ciudadanos’s tweets—highlighting their consistent concern and engagement with the issue—their position on feminism is not captured in a basic word noun count or word frequency analysis.
Given the above, party word choice when addressing gender bears ideological weight. For example, one of the most frequently tweeted nouns by PP is women (mujeres, n = 112, 1.5%) despite them not once mentioning feministas as either a noun or an adjective. This raises questions about the types of gender politics reflected in the PP’s stance and rhetoric. Given the feminist march on March 8, 2019, and general feminist movements getting extensive exposure during this election, it seems that the PP wants to appeal to voters interested in some aspects of gender equality platforms but is reluctant to identify with feminism as a movement. Similarly, while it is no surprise that Vox (n = 31, 0.5%) and PP (n = 12, 0.1%) tweeted the term feminista the least of all parties, the fact that the anti-feminist Vox tweets about feminism much more frequently than PP does not indicate Vox’s position on the issue. It is only upon taking a closer look at Vox’s individual tweets containing feminismo that one can deem the party anti-feminist. The tweet below demonstrates how Vox villainizes feminism and depicts it as a social and political problem with which Vox intends to do away:
En VOX no vamos a participar en la #HuelgaFeminista8M. No creemos en las leyes de género, ni en las cuotas, ni en el feminismo supremacista que lo único que busca son privilegios, no para las mujeres, sino para una minoría de aprovechados y lobbies.
(VOX will not participate in the #FeministStrike8M. We do not believe in gender laws, nor in quotas, nor in the supremacist feminism that only seeks privileges, not even for women, but for a minority of opportunists and lobbies.) (VOX [@vox_es] 2019a)
To see if these discursive differences in the contextual use of gender-related words can be made on the macro level through quantitative analysis, we conducted a co-occurrence analysis to identify the qualifiers surrounding mujer/es (women) and feminismo (feminism).
3.3. Qualified nouns
To develop broad-level assessments of parties’ positions on the recurring issues identified in the word-frequency analysis, this study delves further into the gender case study by using co-occurrence networks to compare parties’ use of qualified nouns. While the word frequency analysis quantifies the extent to which a particular noun/issue/topic is discussed by a party, the study of qualified nouns provides an indication of how and in relation to what those issues are discussed. From the results of the word frequency analysis, we selected the terms mujer/mujeres and feminismo to further nuance our gender case study. The node graphs in this section (Figures 3, 4) represent the results of this analysis: first, the qualifiers that each party associates with the term in question and, second, the relative usage or recurrence of each qualifier in tweets. The latter is represented by the relative width of the lines, or edges, connecting the central term to its qualifiers, with thicker and thinner edges representing higher and lower frequencies of use, respectively. Qualifiers need to have occurred a minimum of ten times to be selected to the graph.
As Figure 3 reveals, the PSOE most often qualifies mujer and mujeres (woman and women) as joven (young), española (Spanish), and asesinada (murdered). Podemos, meanwhile, qualifies mujer/es in terms of a group or gathering (concentración de), a discursive tendency in line with the party’s focus on strike- and demonstration-related frames for discussing gender-related issues (see further discussion of this tendency in Hashtags section). In contrast, the PP qualifies mujer/es as trabajadora (working) and embarazada (pregnant), reflecting the party’s focus on workplace and family policies when addressing gender. Echoing the party’s focus on liberalism, our analysis of Ciudadanos’s tweets containing mujer/es yields the qualifiers valiente (brave) and autónoma (autonomous/independent). In contrast, Vox qualifies mujer/es as española (Spanish) and maltratada (abused or mistreated). While these qualifications shed some light on how each party views and represents women and their issues and engages with issues such as gender violence, perhaps most telling is that all five write about women in relation to men, hence the prominence of hombre y (man and) as a prominent qualifier for mujer/es. In fact, except for Podemos, all parties qualify women in relation to men more so than anything else, indicating that they consistently imagine women within a male-female binary—rather than as independent from men—and, perhaps, that they wish to assure voters that the party sees a place for men within their discussions of women’s rights and feminism.
Moving from mujer/es to feminismo—a term that sparked substantial debate among parties—again reveals differences across parties, shown in Figure 4. To begin, the avowed anti-feminist far-right Vox engages in fear tactics through the construction of a feminismo supremacista (supremacist feminism), radical (radical), and imperante (dominant). Vox’s use of cursos de (courses/classes in) might indicate a perceived threat of mandatory feminist education in schools. Compared to the incensed qualifications of feminismo in Vox’s speech, more muted qualifiers appear in the PP’s section of the graph, all with relatively sparse use frequency. The PP has few tweets that contain feminismo, but those that do tend to deflect critique of the party’s policies in favour of levelling criticism at their left-leaning detractors on the grounds that these entities promote an exclusive and/or negative form of feminism. Tweets containing these qualifiers yield messages in which the PP’s politicians protest the perceived negativity of the party’s critics, rebuffing the lección sobre (lesson on/lecture about) feminism offered by those who criticized the PP’s stance, on the grounds that the PP already espouses a feminismo positivo (positive feminism) that positions women as equal to, rather than against, men.
No hay que confundir la reivindicación del #8M y aquello que quiere utilizar la izquierda en la manifestación, que es la idea de que el feminismo solo puede ser de izquierdas. A la mujer no hay que imponerle un dogma para ser feminista.
(We shouldn’t confuse the revival of 8M with the vision that the left wants to bring to the march, which is the idea that feminism is the sole property of leftists. A woman doesn’t have to submit to a dogma to be a feminist.) (Partido Popular [@Populares] 2019a)
In Cs’s case, the recurrence of liberal as a qualifier for feminismo reflects their ideology. The notion of a feminismo liberal is central to Ciudadanos’s platform. After using the expression feminismo liberal sporadically in 2018, Ciudadanos published their Manifiesto Feminismo Liberal (Liberal Feminism Manifesto)—an outline of the party’s policy priorities—on March 3, 2019, days before the 8M demonstrations (Ciudadanos 2019). The use of liberal as modifier serves to distinguish this platform from a more radical feminism like that of Podemos, while also differentiating Ciudadanos from the non-engagement of the PP and the vitriolic anti-feminism of Vox. Beyond the qualifiers closely associated with their feminismo liberal platform, Ciudadanos qualifies feminismo with the terms inclusivo (inclusive) and moderno (modern), engaging an implicit critique of previous movements as overly radical and exclusive of those with more subdued objectives within the area of gender equality.
However, feminismo liberal is also the most frequently tweeted qualifier-noun pairing of the PSOE. According to this node chart, then, the PSOE and Ciudadanos most often qualify feminism in the same way, as liberal. This similarity, however, is not a good indicator of each party’s stance on feminism, given that, while Cs promotes a liberal feminism, PSOE does not. Determining the context in which a qualified noun is tweeted demands a close reading of tweets containing the term feminismo liberal. In fact, PSOE’s tweets containing feminismo liberal reveal that the qualified noun is only mentioned in tweets questioning the notion of liberal feminism:
Queremos un país con mujeres libres, seguras y vivas. La derecha habla de feminismo liberal y violencia intrafamiliar. Las palabras en democracia representan compromisos y el PSOE será el valladar de los derechos y libertades de las mujeres.
(We want a country whose women are free, safe, and alive. The right talks about liberal feminism and inter-family violence. Words, in a democracy, represent commitments, and the PSOE will be the defender of the rights and liberties of women.) (PSOE [@PSOE] 2019b)
In this light, the PSOE’s other frequently used qualifiers—nuevo modelo de feminismo (new model of feminism), and feminismo en estado puro (feminismo in its pure state)—seem to be a defense of endorsing feminism unconditionally. While Podemos also engages with Ciudadanos’s platform via the use of liberal as a qualifier, their qualifiers illustrate the party’s call for a “plural” feminism that is intersectional and diverse, one that fills the streets (calles), echoing their use of concentración de to qualify mujeres. Notably, Podemos’s call for policy changes that would include a mandatory asignatura de feminismo (course on feminism) being taught in Spanish public schools drew the ire of Vox (see the above discussion of Vox’s use of cursos de). Podemos calls for a feminist revolution, the creation of feminist policies and a strongly worded platform that advocates systemic overhaul, mobilization, and change, whereas Vox attempts to discredit feminist movements, parties, and organizations by referring to them as supremacista (supremacist) and imperante (dominant).
The quantitative analysis and visualization of qualified nouns alone, whilst signalling how key terms like feminism are most often qualified in party tweets, fall short—like the noun frequency analysis (Table 2)—of consistently indicating a party’s stance on a given issue. Nevertheless, while a standalone analysis of qualified nouns by party may not provide an accurate depiction of a party’s position on a topic, in conjunction with a close reading of relevant tweets, it provides insight into the principal ways that each party describes and imagines key electoral issues and the rhetorical style of a party’s engagement with these. By comparing how key terms are qualified, one can identify differences between how parties tweet about gender, particularly between progressive and conservative parties.
Comparing how parties qualify gender-related nouns provides a valuable basis for examining stylistic and ideological treatment of gender. Furthermore, the overlaps and conflicts in the qualified nouns analysis point to areas of potential examination through selected close readings, such as the conflict over feminismo liberal between PSOE and Ciudadanos. To develop another lens, we conducted sentiment analysis to gain a deeper and more accurate quantitative reading of how parties discuss gender on Twitter by determining if there is a quantifiable relationship between the sentiment of tweets and gender-related discussions generated by party.
4. Sentiment analysis: Assessing the affective dimensions of tweets
Political strategists and campaign managers have long realized that emotion is key to mobilizing voters and garnering their support. Considering this, this study analyzes all collected tweets to obtain a macro-level analysis of party variations in sentiment. This analysis also examines specifically gender-related tweets to assess the relationship between party stance on key campaign issues and sentiment to better understand the affective nature of a party’s rhetoric and campaign tactics on Twitter as it relates to gender and feminism. Using Google’s sentiment analysis tool, sentiment was evaluated on the level of each tweet, regardless of the length of the tweet.
4.1. Comparing overall sentiment
By analyzing the sentiment of all tweets posted by each party, this section illustrates how sentiment analysis can reveal relationships between the sentiment of party tweets and how much power a political party holds—be that in number of votes and parliamentary seats or in years of party power and activity. In their 2018 study of the strategic use of emotive language in European campaign manifestos, Crabtree et al. found strong support for the hypotheses that 1) incumbent parties use more positive sentiment in their manifestos and 2) ideologically moderate parties use more positive sentiment than radical or ideologically extreme parties (Crabtree et al. 2018). The first hypothesis is supported by this study, where both incumbent parties and parties with the highest level of public support (measured here in their percentage of the popular vote) during the April 2019 elections are the same parties that generated most positivity—or least negativity—in their tweets: PSOE (incumbent and leading party with 28.7% of votes) and PP (second to PSOE with 16.7% of votes). Conversely, parties with the lowest percentage of votes displayed the most negativity in their tweets overall: Cs (15.9%), Podemos/Unidas Podemos (14.3%), and Vox (10.3%). Figure 5 shows overall sentiment scores by party.
A second reading of the data is grounded in terms of the left-right political spectrum, whereby the parties that are, despite their fundamental differences, closer to the centre of the ideological spectrum—PSOE (left/left of centre), PP (right/right of centre), Ciudadanos (right/right of centre)—generate more positive tweets, whereas parties at more extreme ends of the spectrum—Podemos (far left) and Vox (far right)—generate more negative tweets. A third reading is based on the longevity of each party, whereby the longest standing parties in Spain—in this case, PSOE (founded in 1879), PP (founded in 1989), and Ciudadanos (founded in 2006)—display more positive tweets than those published by Podemos (founded in 2014) and Vox (founded in 2013). In any event, the two parties that have exclusively governed Spain at the national level in the post-Franco era are the same parties that express more positive sentiment in their tweets, suggesting they have a higher degree of satisfaction with Spain’s current political system, whereas less mature parties display more negative sentiment in tweets, suggesting their lower degree of overall satisfaction with the system. A close reading of the following tweet from the PP offers a more qualitative illustration of these rhetorical tendencies.
Tras el #28A vamos a volver a gobernar para todos. Somos un Partido Popular renovado, orgulloso de lo mucho que hemos hecho por España y ambicioso con el futuro que tenemos que conquistar. Somos el #ValorSeguro y no os vamos a defraudar
(After #28A we will again govern for all. We are a renewed Partido Popular, proud of all we have done for Spain and ambitious about the future we have to conquer. We are the #SureBet and won’t let you down.) (Partido Popular [@Populares] 2019b)
In making their promises to the electorate, the incumbent parties PP and PSOE strive to portray their political records as works-in-progress that should be interpreted in a generally favourable light. However, these differences in positioning do not translate to stark differences in sentiment analysis scores between the parties: opposition parties Podemos and Vox have only slightly lower median sentiment scores (0, indicating overall neutral sentiment) compared to the incumbent parties and Ciudadanos (0.1). To better leverage the capacity of sentiment analysis to draw broad-scale conclusions about party rhetoric, it is helpful to compare how party sentiment in discussions of a particular topic—gender—compares with the sentiment scores of their tweets overall. This comparative approach offers a more nuanced reading of party differences and allows us to identify areas for further selected close readings.
4.2. Sentiment analysis using gender-related terms
For a more nuanced understanding of each party’s stance regarding topics related to gender, we performed a sentiment analysis of tweets containing the frequently occurring nouns mujeres, feminismo, and feminista identified in the word frequency analysis. This analysis was done by looking at each party’s tweets that included the gender-related target terms feminism and feminist. We created a corpus of tweets containing the terms in question, and then used Google’s Natural Language API to compute the individual score of each sentence from the filtered tweets. This analysis enables us to form conclusions about the differences in parties’ use of relatively neutral gender-related terms, like mujeres, versus contentious terms that attracted political controversy during the leadup to the elections, like feminismo (see Figure 6).
Our comparative study of mujer/es and feminismo/feminista indicates that the five parties tended to treat these terms distinctly. Notably, with the exception of the PSOE, the parties’ gender-related tweets tended to display more variable sentiment (as measured by the Interquartile Range, or IQR), indicating less consistency in their treatment of gender. Among more conservative parties (PP, Ciudadanos, Vox) tweets containing feminismo/feminista displayed more negative sentiment than both the parties’ tweets overall and those containing mujer/es. This is especially interesting in the case of Ciudadanos, whose feminismo liberal platform drew substantial criticism from more progressive parties. In fact, the median sentiment score for tweets that included the terms mujer and mujeres was lower than the overall median sentiment score for all parties except Ciudadanos (whose median scores were the same across all three sentiment analyses). Figure 6 visualizes the sentiment of tweets that include feminismo and feministas from all five parties using box plots.
Although the PSOE, Podemos, and Ciudadanos share a median positive sentiment score of 0.1 in the feminismo/feminista sentiment analysis, there are pronounced differences in the sentiment patterns of these parties. The generally positive sentiment of Podemos’ tweets containing feminist terms is unsurprising in that Podemos positions itself as a feminist party. Although Podemos is the only party whose sentiment surrounding the terms feminismo/feminista is more positive than that of tweets containing mujer/es and the party’s overall sentiment, Podemos also displays a fair amount of negativity in these tweets. Upon closer examination, it becomes clear that Podemos adopts a strongly negative tone when criticizing the policies of other parties:
¿Cómo un partido que critica las subvenciones para la lucha contra la violencia machista puede llamarse feminista? Este es el #FeminismoLiberal de Cs : a favor de los vientres de alquiler y en contra del lenguaje inclusivo. Mucho tienen que aprender del movimiento feminista.
(How can a party call itself feminist and criticize policy changes to combat gender-based violence? This is the #LiberalFeminism of Cs: in favour of wombs for rent [referring to surrogate gestation] and against inclusive language. They have a lot to learn from the feminist movement.) (Podemos [@ahorapodemos] 2019a)
Similarly, tweets classified as negative for the PSOE often outlined the PSOE’s critique of other parties’ stances on gender issues. For example, as noted in the qualified nouns discussion, the PSOE repeatedly criticized Ciudadanos for adding modifiers (apellidos) to feminism, which specifically targets the feminismo liberal platform that Ciudadanos sought to advance.
Feminismo es #igualdad, no tiene apellidos: o se es feminista, o cómplice del machismo.
(Feminism is #equality, it doesn’t have modifiers: in other words, it’s feminism, or it’s complicit in chauvinism.) (PSOE [@PSOE] 2019a)
Vox’s sentiment around feminism and feminists is by far the most negative of the five parties, with the Partido Popular a near second. Upon closer inspection, however, the target of this negative sentiment was revealed to be substantially different between the parties. Although the overall sentiment of Vox’s tweets leaned negative, their tweets containing the terms feminismo/feminista were especially so, with a median sentiment score of -0.2. This is especially significant given that Vox’s sentiment in tweets containing the terms mujer/mujeres and feminista/feminismo is markedly more negative than the party’s overall sentiment (for all tweets). A close reading of Vox’s tweets that address the 8M feminist demonstrations and the notion of a feminismo supremacista further illustrates how this negative sentiment plays into an underlying assertion of Vox’s platform.
Las mujeres de la España real no necesitan un colectivo que les diga lo que son, mujeres fuertes, trabajadoras e independientes. Ni un euro público más para los lobbies del feminismo supremacista que propagan odio y división.
(The women of the true Spain do not need a collective to tell them who they are, strong, hardworking, independent women. Not one euro more for the supremacist feminist lobbies that promote hate and divisiveness.) (VOX [@vox_es] 2019b)
Here, by using sentiment analysis to guide close readings of the tweets, we identify points of discursive and ideological conflict between parties, particularly over the meaning of feminism and the role of the political party in advancing a specific interpretation of gender equality.
5. Hashtag analysis: Evaluating parties’ framing of topics
Unlike simple noun use, hashtagging creates opportunities for discursive participation online and, to borrow from Benedict Anderson (1983), imagined communities of users who perceive themselves as part of a particular group or movement such as Los Indignados–#15M (Anderson 1983). Hashtags enable political discussion on Twitter to serve a social and dialogic purpose, linking users together based on a similar topic of interest. Although hashtags originated as a means of identifying and searching for discussion topics, Ash Evans (2016) argues that “the use of this affordance has shifted to become its own interactional communication” (Evans 2016), allowing users to create a dialogue and take a clear stance within the space of a single tweet. Similarly, Pond and Lewis (2019) identify hashtags as tools for the social production of meaning, as “genre defining discourses” through which Twitter users engage action frames (Pond and Lewis 2019, 217). The hashtag, along with the text of the tweet and any linked media, are components of a system of symbolic exchange through which Twitter users encode and interpret meaning.
In the context of this study, this means that while some hashtags function as labels that aggregate tweets based on a topic, like #España, others transmit campaign positions, like #MásPPMásIgualdad (#MorePPMoreEquality), or #CsConLasFamilias (#CsStandsWithFamilies) or prescribe an audience viewpoint, like #LaEspañaQueQuieresEsFeminista (#TheSpainYouWantIsFeminist). Analyzing topical hashtag use across parties reveals how they frame and engage with a given topic. By comparing the topical focus of the most-used hashtags of each party and further nuancing this analysis with a case study of gender-related hashtags, this section examines how each party strives to frame the discourses they engage on Twitter through strategic hashtag use.
Parties’ engagement with gender-related hashtags situates them in relation to larger feminist movements and their goals and interventions.
5.1. Feminist and activist hashtag politics
Significant scholarship has been produced on feminist and activist hashtag use. For example, work exists on how the use of hashtags by feminist movements fosters online conversations on specific issues, such as misogyny in Korea (Kim 2017), or on hashtags as a form of feminist “shouting back” (Turley and Fisher 2018). Scholars have also cited media hashtagging as a response to rape culture, and as a method to “call out” and demand accountability from perpetrators “when mainstream news media, police, and school authorities do not” (Rentschler 2014, 67).
It has also been argued that hashtagging has brought ordinary people into the political arena and aided in feminist consciousness-raising on a large scale. Mendes, Ringrose, and Keller study how the hashtags #BeenRapedNeverReported and #MeToo do the work of creating feminist solidarity by exposing the structural nature of oppressive experiences, and concurrently make survivors feel heard (Mendes, Ringrose, and Keller 2019). This is also due to the conversational nature of the platform, which enables a larger “collective storytelling” (Jackson, Bailey, and Foucault Welles 2020). Larrondo (Larrondo, Morales-i-Gras, and Orbegozo-Terradillos 2019) similarly writes about this phenomenon in Spain, contending that hashtag activism “would appear to be an intermediate step in a longer process of creating a higher consciousness regarding gender equality issues in Spain” (207).
However, online feminist networks that utilize hashtagging are not without critique. They have been largely problematized for replicating pre-existing racial hierarchies (Feldman and De Kosnik 2019). This results in a dominant digital politic that ignores the contributions of women of colour in activist movements and centres the experiences of white women (Mueller et al. 2021).
The feminist Twitter protest tactic of self-disclosure to gain visibility has also been discussed in terms of its potential harms. While it has certainly had deep social impacts, this visibility can simultaneously put survivors at risk. As Clark-Parsons writes, “publicly performing the identity of a survivor in a cultural context where sexual violence victims are shamed and doubted” (Clark-Parsons 2019, 10) can make participants more vulnerable to online harassment, doxxing, and personal attacks. Parties’ framing of political discourse is in conversation, reaction, and response to activist and feminist hashtagging practices, particularly in terms of gender issues.
5.2. Overall hashtag use
The results of our preliminary hashtag analysis are summarized in Tables 3 and 4. A superficial reading of these hashtags supports the broad policy leanings associated with each party. However, a look at the context in which each party’s most frequently tweeted hashtags were produced reveals more complex entanglements of party self-representation and use of hashtags to carefully frame political discourse.
Podemos | PSOE | PP | Ciudadanos | Vox |
---|---|---|---|---|
#LaHistoriaLaEscribes Tú (110) |
#28A* (462) |
#ValorSeguro (252) |
#28A* (249) |
#EspañaViva (153) |
#28A* (34) |
#LaEspañaQueQuieres (363) |
#28A* (141) |
#FeminismoLiberal (62) |
#PorEspaña (34) |
#ElDebateDecisivo (25) |
#HazQuePase (263) |
#CentradosEnTu Futuro (105) |
#ActualidadCs (53) |
#28A* (26) |
#L6Npabloiglesias (24) |
#VotaPSOE (170) |
#26M (55) |
#VamosCiudadanos (51) |
#vídeo (19) |
#HuelgaFeminista2019* (23) |
#PSOE (147) |
#España (46) |
#PPSOE (48) |
#Vox (14) |
#FridaysForFuture 22 |
#LaEspañaQueQuieres EsFeminista (85) |
#ElDebateDecisivo (43) |
#endirecto (32) |
#FakeNews (14) |
#ObjetivoIglesias (21) |
#8M (80) |
#VotaPP (42) |
#Madrid (20) |
#NoHablesEnMi Nombre (10) |
#15MClimático* (21) | #España (70) |
#ELDEBATEenRTVE (37) |
#EleccionesGenerales 28A (19) |
8M (12) |
#8M* (19) |
#EstamosMuyCerca (65) |
#CasadoPresidente (29) |
#26M* (19) |
#Directo (9) |
#LaRutadelCambio 18 |
#26M (51) |
#ProgramaPara España (21) |
#TeruelNaranja (19) |
#EleccionesGenerales28A (8) |
#EstaHistoriaLaEscribes Tú (17) |
#CMin (37) |
#NoHablamos Hacemos (18) |
#Aragón Naranja (18) |
#CataluñaPor España (7) |
#ELDEBATEenRTVE (17) |
#igualdad (37) |
#CasadoCon España (15) |
#EnMarchaPorLa Libertad (17) |
#EnEuropaPorEspaña (7) |
#Horizonte Verde (17) |
#SiempreHaciaDelante (30) |
#ObjetivoEuropa (15) |
#EncuentroCiudadano (17) |
#Madrid (6) |
#L6Neldebate (16) |
#PP (26) |
#GarantíaParaEspaña (15) |
#AlicanteNaranja (16) |
#LaEspañaViva (6) |
AlertaSpoiler (15) |
#ViolenciaDeGénero (26) |
#ElVotoQueNosUne (15) |
#HospitaletNaranja (16) |
#26M (6) |
Obs.: Use counts for each hashtag are listed in parentheses. Hashtags marked with an asterisk indicate that the counts for multiple very similar variations of a hashtag (for example, 8M, 8Marzo, and 8M2019) have been aggregated as a single count.
Podemos | PSOE | PP | Ciudadanos | Vox |
---|---|---|---|---|
#HuelgaFeminista (28) |
#LaEspañaQueQuieresEsFeminista (92) |
#NoHablamosHacemos (18) |
#FeminismoLiberal (62) |
#8M* (11) |
#8M* (21) |
#8M* (80) |
#PrisiónPermanente Revisable (13) |
#8M* (12) |
#NoHablesEnMi Nombre (10) |
#NadaNosPara8M (7) |
#Igualdad (37) |
#DíaDeLaMujer (13) |
#DíaDeLaMujer (5) |
#HuelgaFeminista (6) |
#DíaDeLaMujer (6) |
#ViolenciaDeGénero (28) |
#8M* (9) |
#Objetivo8M (4) |
#Familias (2) |
#YoTambiénSoy Feminista (3) |
#NiUnPasoAtrás (19) |
#LeyDeMaternidad (5) |
#Gestación Subrogada (3) |
#ConectaConLaVida (2) |
8MCampañaARV (3) |
#DíaDeLaMujer* (12) |
#MásPPMásFamilia (3) |
#Debate8MH25 (3)# |
InternationalWomensDay (2) |
#FeminismoLiberal (3) |
#TiempoDeMujeres* (11) |
#MásPPMásIgualdad (3) |
#CsConLasFamilias (2) |
#Femimarxismo (1) |
#YoVoy8M (2) |
#Feminismo (8) |
#Objetivo8M (3) |
#IgualdadCs (2) |
#LGBTI (1) |
#MujeresPoderosas (1) |
#Objetivo8M (6) |
#ViolenciaDeGénero (2) |
#MujeresRurales (1) |
#Género (1) |
#Cuéntalo (1) |
#ViolenciaMachista (5) |
#FelizDíaDeLaMadre (2) |
#Aresaca8M (1) |
#MujeresFuertes (1) |
Obs.: Use counts for each hashtag are listed in parentheses. Hashtags marked with an asterisk indicate that the counts for multiple very similar variations of a hashtag (for example, 8M, 8Marzo, and 8M2019) have been aggregated as a single count.
The left-of-centre incumbent PSOE’s use of #LaEspañaQueQuieres (#TheSpainYouWant) reinforces the generally positive sentiment that the party displayed in this study’s Sentiment Analysis. By linking this slogan with a demand for gender equality in #LaEspañaQueQuieresEsFeminista, the party pitches itself as not only a party with a proposed platform on gender equality, but a party that advocates for a feminist Spain and expects its voters to want the same. Notably, #LaEspañaQueQuieres and other PSOE tags like #HazQuePase (#MakeItHappen) and #VotaPSOE (#VotePSOE) directly address the audience with action- and future-oriented frames that invite the reader to be part of the PSOE’s political project.
The conservative incumbent PP focuses on frames that assure voters of the party’s competence and commitment to achieving its aims, with hashtags like #ValorSeguro (#SureBet), #NoHablamosHacemos (#WeDon’tTalkWeGetThingsDone), and #GarantíaDeFuturoPP (#FutureProofedPP). Compared with more progressive parties, few of the PP’s hashtags relate to specific topical concerns that are linked to specific policy areas, with one notable exception: #PrisiónPermanenteRevisable (#RevisablePermanentImprisonment), a reference to the PP’s contentious call to modify Spain’s penal code and expand the list of crimes that can carry a sentence of life imprisonment. Unsurprisingly, tweets by the conservative Partido Popular also regularly reference Spain (as is the case with #ProgramaParaEspaña—#ProgramForSpain). In other words, the PP’s use of hashtags reveals a platform that is nationalist and tough on crime.
Ciudadanos’s hashtag use reminds readers that the party wishes to present itself as a refreshing, ideologically moderate alternative to the two incumbent parties. The hashtag #PPSOE, a portmanteau of the acronyms for the two incumbent parties, offers an implicit critique of the two-party system and the corruption and political gridlock that have been central problems of Spanish politics in recent years. Podemos, on the other hand, seeks to distinguish itself from the dominant parties through its positions on social and environmental issues and a strong encouragement of voter participation. Examining the table, we see that Podemos’s tweets are often climate- and gender-focused with hashtags such as #FridaysForFuture, #8M, #HorizonteVerde (#GreenHorizon), #15MClimático (#15MForClimate), #HuelgaFeminista (#FeministStrike), #UnidasPodemos, and #UnPaísAnimalista (#AnimalFriendlyCountry) among its twenty most frequently tweeted hashtags. These ecologically minded and gender-inclusive hashtags, coupled with those with a focus on democracy and popular participation in history and politics, such as #LaHistoriaLaEscribesTú (#YouWriteThisHistory), paint Podemos as environmentally friendly, committed to gender equality and women’s empowerment, as well as democratic. As a point of comparison to this approach to staking a claim as an opposition party, Vox’s hashtag use is heavily inflected with nationalism. Six of Vox’s top twenty hashtags include the term España, consistent with the party’s nationalist platform. Vox displays a strong focus on nationalism through tags such as #EspañaViva (#LiveSpain) and #PorEspaña (#ForSpain), but also draws on anti-feminist rhetoric with #NoHablesEnMiNombre, which is discussed further in the next section.
5.3. Gender-related hashtags
In the leadup to the 2019 election, parties engaged in a public series of debates about what feminism is and is not, what it means to be a feminist party, and what the role of the party is in outlining modes of participation in feminist activism. Interpreted in this context, hashtags operate as discursive frames that offer insight into each party’s rhetorical treatment of gender. To examine variations in hashtag use, we manually identified gender-related hashtags and then interpreted those hashtags based on their function in positioning the party’s stance on gender. It was necessary to do this manually because parties often used hashtags related to gender that were neither existing refrains of online discussion about gender nor contained gender-related keywords. To identify these hashtags, we reviewed the tweets that contained them and determined if they operated as discursive frames in gender-related conversations.
Although left-leaning parties Podemos and PSOE both tended to identify with feminist labels more frequently than the conservative parties, they adopted different hashtagging tactics. The PSOE took a rigorous and consistent approach to branding their feminist platform using a set of party-specific hashtags. To continue their #LaEspañaQueQuieres slogan, the PSOE made frequent use of #LaEspañaQueQuieresEsFeminista (#TheSpainYouWantIsFeminist). The heavy use (n = 80) of the hashtag #8M speaks to how the PSOE sought to immerse itself in the demonstrations on March 8. They also drew on concept-focused tags such as #Igualdad (#Equality), #ViolenciaDeGénero (#GenderViolence), and #ViolenciaMachista (#ChauvinistViolence) to articulate specific concerns within their advocacy for gender equality. On occasion, the PSOE drew on existing feminist Twitter discussions through the use of hashtags, such as #NiUnPasoAtrás (#NotOneStepBackwards), used by activists during protests on January 15, 2019, against the Andalusian government’s alliance with the anti-feminist Vox. The threat of regression implied within #NiUnPasoAtras is a resonant form of thematic unity for the PSOE’s broader rhetorical strategy of future-oriented promises that portray the party’s accomplishments as a favourable work in progress and warn against the regressive policies of more conservative parties.
Like the PSOE, Podemos included a handful of hashtags belonging to existing feminist Twitter dialogues, such as #Cuéntalo (#TellIt/#TellYourStory), first tweeted in April 2018 by journalist Cristina Fallarás who called on women in the Twittersphere to tell the story of their first memory of sexual harassment and abuse. It is also notable that the PSOE and Podemos were the only parties to draw on existing feminist hashtag dialogues, indicating their interest in affiliating themselves with larger movements for gender equality as part of their feminist self-presentation. However, in comparison to the PSOE, Podemos used relatively few gender-related hashtags overall, and left many of their gender-focused tweets untagged. Beyond the frequent use of the tags #8M and #HuelgaFeminista (#FeministStrike) to draw attention to the feminist demonstrations on March 8, 2019, Podemos used a variety of event-linked hashtags to express their party’s support for the marches: #YoVoy8M (#I’mGoing8M) and #NadaNosPara8M (#NothingCanStopUs8M) reinforce the assessment of Podemos’s approach to feminism that emerged from the co-occurrence analysis—one that is based on a vision of feminism as an unstoppable wave that fills the streets.
While the more progressive parties tended to describe the 8M protests as a #HuelgaFeminista (#FeministStrike), conservative parties stuck to a muted playbook of celebrating women in family- and workforce-oriented frames. The conservative PP—who declined to participate in the 8M demonstrations on the grounds that the left-leaning parties had “politicized” the event— opted for variations of the tag #DíaDeLaMujer (#WomensDay) rather than invoke frames associated with feminist demonstrations. The centre-right and conservative parties—PP and Ciudadanos—tended towards hashtags that focused more heavily on loosely defined equality frames in the context of workplace and family policy, as the PP’s #MásPPMásFamilia (#MorePPMoreFamily) and #LeyDeMaternidad (#MaternityLaw) indicate. The PP’s use of #NoHablamosHacemos (#WeDon’tTalkWeGetThingsDone) is especially compelling, as it attempts to deflect the critique from left-leaning parties of the right’s language surrounding gender by pointing to the PP’s historical encouragement of women’s labour force participation. Similarly, Ciudadanos focuses their hashtag use on advancing a neoliberal platform for gender equality—#FeminismoLiberal. Vox’s hashtags lend more vivid context to the negativity observed in our earlier analysis of the party’s gender-related tweets. The use of the tag #NoHablesEnMiNombre (#Don’tSpeakInMyName), is a particularly telling tactic wherein Vox attempts to shift critique towards progressive parties and politicians and feminist activists for “speaking in the name of” (hablar en nombre de) all women. Notably, this hashtag borrows some of the features of popular feminist twitter campaigns through a testimonial-style format in which Vox supporters share their experiences and state their opposition to feminism. The notion of a feminismo supremacista that crowds out the real voices of Spanish women and asserts itself within politics and public life against the will of the people is consistent with Vox’s perception of an ideología de género that is corrupting Spanish institutions.
By parsing and visualizing the most frequently used hashtags by party, this study finds that Spanish parties use hashtags to frame their policies and critiques of other parties. By understanding these discursive frames, one can understand how parties align themselves with existing feminist dialogues on Twitter like #NiUnPasoAtrás, create contextual frames for their campaign promises as with #LaEspañaQueQuieresEsFeminista, and deflect criticism by attempting to change or destabilize the terms of feminist discourse through speech frames like #NoHablamosHacemos and #NoHablesEnMiNombre.
6. Conclusion
Each quantitative analysis undertaken in this study provides significant insight into the relationships between a party’s interest in and position on key political issues and its Twitter rhetoric. However, these methods are most effective when put in conversation with one another and accompanied by close readings of tweets that contextualize noun and hashtag recurrence. By using word frequency analysis to assess the amount of attention a party dedicates to key electoral issues as a starting point and then parsing tweets further to identify co-occurrence networks, analyze sentiment, and conduct close readings, this study demonstrates the potential of quantitative approaches for focused analyses of online speech. In tandem, this set of tools reveals the connections between party stance, rhetorical tendencies, and affect.
Although the initial word frequency analysis found that gender was a topic of interest for all parties, it was the co-occurrence and sentiment analyses that unearthed a more detailed portrait of party variations in discussions of gender. The qualifiers and hashtags analyses demonstrated that, while right-leaning parties formulated their treatment of gender in ways that avoided overt affiliation with feminism, their tactics differed considerably, as indicated by the differences between Ciudadanos’s desire for a moderate feminismo liberal, Vox’s polemic threats of a feminismo supremacista, and the PP’s near-total avoidance of the terms feminismo/feminista in favour of cautious statements about women’s economic activity. Left-leaning Podemos and PSOE seized on this rhetorical indecision as a point of critique and an opportunity to assert their support for an unapologetic feminism sin apellidos (without modifiers) and comprehensive platforms for gender equality. This study’s exploration of this conflict illustrates how distant methods—in this case, the use of co-occurrence networks to reveal overlap between the PSOE and Ciudadanos’s use of the qualifier liberal for feminismo—uncover contested and inter-party conflicts in voter-focused communication. Furthermore, the gender case study reveals that all parties use disputes over the meaning of key terms like feminismo as opportunities to assert themselves against their political opponents. While the PSOE sought to reinvent itself as a future-oriented feminist party with a favourable record on women’s rights, the PP decried the PSOE’s “politicization” of participation in the 8M feminist strikes and pointed, instead, to their own statistics for women’s workforce participation as evidence of a commitment to gender equality. Conversely, Podemos aligned itself with the feminist movement by supporting intersectional economic justice for women and dramatic policy changes to highlight its comparative progressivism in relation to the muted feminismo liberal of Ciudadanos and the avowed anti-feminism and gender ideology conspiracy theories of far-right party Vox.
Returning to this study’s original question, then, is there a quantifiable relationship between the style and rhetoric of a party’s Twitter speech, political platform, and political ideology? The findings of this analysis suggest that, yes, one can quantify the relationship between these elements, but it is most accurately measured when the quantitative analysis focuses on specific and often divisive topics such as gender equality, and with a multimodal approach that places analyses of word and hashtag frequency, co-occurrence networks, and sentiment in conversation with each other to reveal patterns in the rhetorical treatment of these topics. Given these findings, this study provides a model for similar studies of political Twitter in other geopolitical contexts and of other significant campaign issues, including immigration, regional autonomy, tax reform, and climate change.
Competing interests
The authors have no competing interests to declare.
Contributions
Authorial
Authorship is alphabetical after the drafting author and principal technical lead. Author contributions, described using the CASRAI CredIT typology, are as follows:
Vanessa Ceia: vc
Thyago Mota: tm
Rhian Lewis: rl
Authors are listed in descending order by significance of contribution. The corresponding author is Vanessa Ceia.
Conceptualization vc
Methodology vc, tm
Software tm
Project Administration vc
Formal Analysis vc, tm, rl
Investigation vc, tm, rl
Visualization tm
Data curation vc, rl
Writing – Original Draft Preparation vc, rl
Writing – Review and Editing vc
Editorial contributions
Section and Copy Editor
Shahina Parvin, Journal Incubator, University of Lethbridge, Canada
Layout Editor
Christa Avram, Journal Incubator, University of Lethbridge, Canada
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