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1227Estud. mensaje period. 27(4) 2021: 1227-1241
#EsteVirusloParamosUnidos: Comunicación política de guerra en Twitter. Creación de
comunidades homogéneas en la crisis de Covid-19
Anna Tous-Rovirosa1 y Daria Dergacheva2
Recibido: 29 de abril de 2021 / Aceptado: 28 de septiembre de 2021
Resumen. Se analiza la comunicación política en Twitter del Gobierno de España en el pico de la pandemia de Covid-19. La campaña
en Twitter #EsteVirusloParamosUnidos fue monitorizada durante las fechas con los peores resultados en términos de fallecimientos (31
de marzo-4 de abril). La muestra incluye 398.523 tweets que se recogen en cuatro bases de datos. Mediante el análisis de redes sociales
se identican los principales actores y las interacciones entre los usuarios. Observamos una elevada coincidencia entre la tipología de
portavoces de las ruedas de prensa institucionales y los principales actores del hashtag, dándose prioridad a la administración y a las
fuerzas armadas españolas. Se observa también que los principales líderes de opinión se relacionaron con su esfera natural. Mediante
el análisis computacional se concluye que en este hashtag se dio en un ambiente de guerra, que la palabra “gobierno” se mencionaba
más que términos médicos, así como la presencia de términos militares.
Palabras clave: Análisis de redes sociales; Twitter; comunicación política; covid-19; coronavirus; #estevirusloparamosunidos; cam-
paña institucional; pandemia
[en] #EsteVirusloParamosUnidos: War-like political communication on Twitter. Creating homogeneous
communities in the Covid-19 crisis
Abstract. This article analyzes the political communication on Twitter of the Government of Spain at the height of the Covid-19
pandemic. The #EsteVirusloParamosUnidos campaign on Twitter is monitored during the dates, with the worst results in terms of
fatalities (March 31st– April 4th, 2020). In total, the sample included 398,523 tweets in four data sets. Through Social Network
Analysis, the main actors and the main interactions between users were identied. The research shows a high coincidence between the
typology of the press conference spokespersons and the main actors on the analyzed hashtag, prioritizing the Spanish administration
and the armed forces. There was also a high relationship of the main opinion leaders with their “natural spectrum.” We conclude that
in this hashtag, there was a “war-like” atmosphere. Via the computer-based text analysis, we identify that the word ‘government’ was
mentioned more than medical words, and some military-like terms were present.
Keywords: SNA; Twitter; political communication; Covid-19; coronavirus; #estevirusloparamosunidos; institutional campaign; pan-
demics; Spain
Summary. 1. Introduction. News and facts. Description of the situation 2. Press Conferences: Multiplicity of spokespersons 3. Literature
review 4. Research Questions and Objectives 5. Method 6. Network Data Collection 7. Results 8. Discussion and Conclusions
Cómo citar: Tous-Rovirosa, A., & Dergacheva, D. (2021). #EsteVirusloParamosUnidos: Comunicación política de guerra en Twitter.
Creación de comunidades homogéneas en la crisis de Covid-19. Estudios sobre el Mensaje Periodístico 27 (4), 1227-1241. https://
dx.doi.org/10.5209/esmp.75758
1 Universitat Autònoma de Barcelona (España)
E-mail: anna.tous@uab.cat
2 Universitat Autònoma de Barcelona (España)
E-mail: daria.dergacheva@autonoma.cat
Estudios sobre el Mensaje Periodístico
ISSN-e: 1988-2696
https://dx.doi.org/10.5209/esmp.75758
1. Introduction. News and facts. Description of the
situation
The state of alarm in Spain began on March 14th,
2020, with the publication in the BOE (Boletín O-
cial del Estado/State Ofcial Newsletter) of Royal
Decree 463/2020. At the time of writing this arti-
cle, Pedro Sánchez’s executive had managed to ex-
tend the state of alarm for the fth time until June
7th, 2020. In the period analyzed (March 31st-April
4th), deaths from coronavirus totaled 5,216 in just ve
days, with April 2nd being the worst day on record
with 950 deaths according to RTVE (2020) and based
on the Spain’s Ministry of Health database. It has
been one of the worst periods on record in Spain. Ac-
cording to the INE (Instituto Nacional de Estadísti-
ca/ National Statistics Institute), there were 23,778
deaths caused by Covid during March 2020, with an
additional 8,743 deaths with causes unidentied but
suspected of being caused by Covid for the same pe-
riod. There were a further 38,325 deaths caused by
Covid in April 2020, again with 17.134 not identied
but suspected of being caused by Covid for the same
period (INE).
INVESTIGACIONES Y DOCUMENTOS
1228 Tous-Rovirosa, A., & Dergacheva, D. Estud. mensaje period. 27(4) 2021: 1227-1241
Recent studies have shown the preeminence of
traditional media (Casero-Ripollés, 2020), publicly
owned (EBU, 2020) in media consumption as a re-
sult of the coronavirus pandemic. However, it is also
highlighted that “the results suggest the existence of
complementarity between traditional and digital me-
dia” (Casero-Ripollés, 2020: 11) in the hybrid com-
munication model already described by Chadwick
(2013), as has been shown by Masip et al. (2020).
According to this research, digital media appears as
the rst option to be informed (38.9%), followed by
TV news (33.9%) and social media (11.4%). Infor-
mation in digital media has been considered so im-
portant that on April 18th, the European Commission
adopted a recommendation on the use of digital me-
dia as an important instrument for containment meas-
ures (European Commission, 2020). Several studies
have been focused on fake-news and disinformation
in this pandemic (Andreu-Sánchez & Martín-Pas-
qual, 2020; Pérez et al. 2020; Salaverría et al., 2020).
2. Press Conferences: Multiplicity of spokespersons
From mere observation, we can state that the Span-
ish government’s strategy was to appear on TV daily
from the time the state of alarm was decreed. Such
a strategy could have been a simple matter of keep-
ing the population informed. The president appeared
eight times, from March 12th until April 4th; the tech-
nical committee appeared 22 times (March 16-30th);
while several ministers appeared during the same
period, some 26 press conferences from March 15th
until April 5th. In fact, all ministers have appeared in
press conferences, except Irene Montero, due to her
being quarantined. Costa-Sánchez and López-García
(2020) highlight that there have been several prob-
lems in the government’s political communication.
They focus on a lack of transparency, and they also
criticize the spokesperson formula adopted by the
government, as it is contrary to the crisis commu-
nication recommendations. A multiplied spokesper-
son can be positive (as they take the initiative and
are proactive in the messages), but “it multiplies the
risk of contradiction of the message and covers the
entire media space, saturates it, opening for the pub-
lic a scenario of attention and permanent tension.” In
sum, according to these authors, the institutional ac-
tors “face the challenge of transparency, coherence,
understanding with different stakeholders, leader-
ship and disclosure (Costa-Sánchez & López-García,
2020: 10-11).
A week after Santiago’s cessation because of his
controversial statement that the Civil Guard was
working to reduce criticism against the government
(the Interior Minister, Fernando Grande Marlaska,
quickly stated that they were not acting against free-
dom of expression), the Spanish government decided
to replace the press conferences that had taken place
with several spokespersons, some of them military.
They came out every morning to report on the evo-
lution of the health crisis (El Condencial, 2020b).
The press conferences of the Technical Management
Committee had ve spokespersons. Some of them
had to be replaced because of the disease, such as
González and Ceña. A Ministry of Health represent-
ative, usually the director of the Center for Coordi-
nation of Health Alerts and Emergencies, Fernando
Simón; an armed forces representative (Chief of the
Defense General Staff, Miguel Villarroya, or Chief of
Staff, Carlos Pérez), one of the Civil Guard (the dep-
uty operational director, Laurentino Ceña and Gen-
eral José Manuel Santiago); a representative from
the National Police (the deputy operational director
of the National Police, José Ángel González and the
commissioner, José García Molina, amongst others;
and a transport spokeswoman (the ministry’s secre-
tary-general, María José Rallo).
The communication strategy of the Spanish
government in this crisis has been widely seen as
a failure. Principally, this is due to the multiplicity
of stakeholders, which can lead to misunderstand-
ings and lack of coordination (Camacho, 2020; Cos-
ta-López, 2020; López-García, 2020), but also an ex-
cess of appearances by the president, Pedro Sánchez,
and the fact that most of the spokespersons are not
communication professionals.
With the change of strategy, a high presence of the
president is chosen, trying to reinforce his role as a
leader (Camacho, 2020). According to López-García
(2020), the use of military personnel as spokesper-
sons could could signal a strategic appeal to these
stakeholders as they have a higher approval rating
within the general public’.
According to the CIS, in 2015, the Spanish gen-
eral public’s approval rating of the Civil Guard was
(6,02), the National Police (5,95), and armed forces
(5,51). This was doubled that of their assessment of
the government (2,77), trade unions (2,61), and po-
litical parties (2,23) (CIS, 2015). López-García high-
lights that this assessment remains on a similar foot-
ing nowadays, so the “government sought to hide in
the military, police and Civil Guard” (López-García,
2020: 14).
Some scholars (Castelo-Szulman, 2020; López-
García, 2020) and media have criticized the usage
of war metaphors (“against the coronavirus”) by
the Spanish government spokespersons, particular-
ly Pedro Sánchez. Susan Sontag already criticized
war metaphors against cancer in 1978 (Illness as a
Metaphor) and against AIDS in 1988 (AIDS and its
metaphors). The armed forces in Spain were used in
all kinds of tasks related to the Covid-19 crisis. So,
the ght against coronavirus was not just a metaphor.
As López-García (2020: 9) states, “The war-like lan-
guage and the presence of uniforms on the news and
in press conferences clearly showed intention from
the government: this is a war.” Through this research,
we will try to nd out if this also happened in the
analyzed hashtag.
The government uses the Spanish Army’s Military
Emergency Unit to perform various functions related
1229Tous-Rovirosa, A., & Dergacheva, D. Estud. mensaje period. 27(4) 2021: 1227-1241
to the virus in what is called “Operation Balmis,” in
tribute to Francisco Javier de Balmis, the Spanish
military doctor that brought the smallpox vaccine to
America and the Philippines. López-García (2020: 7)
emphasizes that media coverage of the UME (Span-
ish acronym for Military Emergencies Unit) was quite
positive. In fact, one of the few counter-reactions was
the opposition from the Catalan Government, which
at rst refused UME’s help to undertake actions that,
according to them, could be perfectly realized by the
Catalan administration (García, 2020).
3. Literature review
In political communication, Social Media have been
widely considered as instruments that facilitate ci-
vilians to participate and become active political
subjects and empowered citizens (Casero-Ripollés,
2017). The basis of this statement was clear: accord-
ing to the utopian point of view at the beginnings of
the research on the Internet (Meredith, 2013), the
Internet and Social Media would facilitate citizen’s
participation, and there were interesting problems to
be studied, such as vulnerable populations (low ed-
ucation and non-frequency of digital network use)
and their difculty of equal access to these bene-
ts (Casero-Ripollés, 2017). According to Cam-
pos-Dominguez (2017), the analysis of Twitter as a
means of disseminating political messages is extraor-
dinarily useful to get an overview of the discursive
strategies of any political party.
There are multiple ways to study politics and so-
cial networks: how social networks are integrated
into political campaigns (Householder & LaMarre,
2014), as well as the study by Kensi and Jomini
(2006) on the relationship between Internet access,
interest in political campaigns and national politics,
and Twitter (Ausserhofer & Maireder, 2013); and
also political participation on Twitter (Bode & Dal-
rymple, 2014). Research into Spanish elections and
social media has been undertaken by Baviera (2018)
and Guerrero-Solé (2018), amongst others. Guo et al.
(2020) have recently analyzed the 2016 US elections
and the effects of Twitter’s echo chamber. Ausser-
hofer and Maireder’s research concluded that the net-
work formed by Austria’s most political Twitter users
was dominated by a political professional elite but
was open to outside participation. The topic analysis
reveals the emergence of niche authorities and the pe-
riodic divergence of the political discourse on Twitter
with that of mass media (Ausserhofer & Maireder,
2013: 291). Social Media as an implement used by
political communication has been analyzed in the
context of fragmentation and segmentation (Gibson,
2015: 184). In a broad sense, media space has evolved
from a ‘one size ts all’ logic to a segmented appeal
(Gibson, 2015: 184), or, in Negroponte’s words, from
“prime-time” to my time (Negroponte, 1994).
Another interesting issue is activism. As Castells
(2013) pointed out, in relation to politics, Social
Media is used to mobilize the bases, leading to dif-
ferent movements between politics, media, and the
electorate. Social media are seen as a way to engage
citizens and ght against political disaffection in con-
temporary democracies because those who are polit-
ically engaged on social media platforms tend to be
more active (Jensen & Anstead, 2013: 162). Activism
and new political parties have been widely studied
(Gibson, 2015; Linares, 2013; Pérez-Altable, 2015;
García-Carretero & Díaz-Noci, 2018; Casero-Rip-
ollés, 2017; Valera-Ordaz & López-García, 2019).
Social media in politics are being used to connect
with young people (Utz, 2009), and to increase links
with the electorate, in a broad sense (Ward & Gib-
son, 2008; Túñez & Sixto-García, 2011), as well as to
convey feelings (Coromina et al., 2018). According
to some research, such as Kruikemeier’s (2014, 136),
the use of Twitter in political campaigns is consid-
ered a positive input for political parties.
More recent investigations show that political
communication on Twitter has developed an autoref-
erential character (García-Ortega & Zugasti, 2018),
as well as a lack of factual interaction with the elec-
torate. Cifuentes and Pino’s (2018) results also led
to the conclusion that political parties’ use of Twitter
was in relation to the creation of an “in-group” in Co-
lombia’s Democratic Center, via fostering cohesion
in the political party and with its electorate, mean-
while trying to attack their political rivals, placed in
the “out-group” sphere.
A variety of research has been undertaken, and
in several areas, that we must quote here because
of their proximity to the topic. To start, the use of
Twitter during the Covid-19 pandemic in Spain has
also been analyzed from a mathematical perspec-
tive (Gutiérrez et al., 2021). The authors analyze the
Twitter behavior of citizens and administrations dur-
ing states of alarm.
The issue has also been analyzed with sentiment
analysis techniques in several European countries
(Kruspe et al., 2020) and via a topic modeling ap-
proach in order to explain which were the main top-
ics discussed on Twitter during the pandemic (Agüe-
ro-Torales et al., 2021). Finally, the authors of this
paper have also analyzed war-like language in the
hashtag #estevirusloparamosunidos (Mustajoki et al.,
2020).
Social networks do have a process of constant
negotiation “to impose a certain story” (Coromina,
2017). It is good to bear in mind that in a crisis such as
this, the administration may have not only this need
to impose a particular story in the hashtag analyzed
but also an additional narrative. Furthermore, social
platforms have been studied as spaces where afni-
ties and similar points of view between users led to
lter bubbles (Pariser, 2011) or echo chambers where
users are only exposed to agreeable opinions, without
forgetting the importance of a platform’s algorithms
to ensure the permanence of the users on the page
(Baeza-Yates, Peiró, 2019). “As a result, we may see
increasing social fragmentation and ideological po-
1230 Tous-Rovirosa, A., & Dergacheva, D. Estud. mensaje period. 27(4) 2021: 1227-1241
larization in our society” (Liao and Fu, 2014: 2745).
The echo chamber effect has been studied by Colle-
oni et al., 2014; Freelon et al., 2015; Himbelboim et
al., 2013; Guo et al., 2020; Liao and Fu, 2014; and
Johansson, 2018, amongst others. Echo chambers
in Twitter also have their detractors, as indicated in
the title “Twitter Is Not the Echo Chamber We Think
It Is” (Shore et al., 2018). But the more generalized
perspective is that Twitter as a Social Media plat-
form reproduces the effect, which tends to increase
polarization, as “people tend to discuss issues only
with other like-minded people” (Du, Gregory, 2017).
There are several research scholars studying the echo
chamber effect on Twitter, either in terms of racism
(Criss et al., 2021), the Covid vaccination (Cossard et
al., 2020), or the Greek Referendum and Europe as a
public space (Michailidou, 2017), to name but a few.
Therefore, we consider that the echo chamber
effect and polarization should be related to the us-
age of Twitter by politicians and political institu-
tions, because according to several research studies,
they usually employed Twitter for campaigning, for
self-promotion and to spread information rather than
to engage in conversations, even though Grant et al.
(2010:579) showed that those who did interact with
other users appeared ‘to gain more political bene-
t from the platform than others’ (Ausserhofer and
Maireder, 2013: 292). In this sense, Guo, Rohde, and
Wu’s (2020: 234) study concluded that “certain opin-
ion leaders were responsible for creating homogene-
ous communities on Twitter.”
Towers et al. (2015) studied Twitter’s repercus-
sion in the US’s Ebola health crisis; Vijaykumar et al.
(2018) analyzed information about Zika in Twitter,
also in the US, and they considered that the spread
of information in Twitter had been positive: “As this
study illustrates, journalists and news media organ-
izations play a signicant role in disseminating and
amplifying Emerging Infectious Disease Outbreak
(EIDO) information, with much of the Twitter con-
tent related to public health updates, actions, and
advice” (Vijaykumar, 2018: 555). Some researchers
highlight that Twitter does have a preeminence on in-
formation distribution related to health (Bakal & Ka-
vuluru, 2017), but, along with some other problems
observed in other social media, there is a dangerous
mixture of information, easy and quick distribution,
and the lack of verication (Albalawi et al., 2019;
Perez et al., 2020: 4).
Political communication has been following sev-
eral novelties related to the Internet and new social
media and using them to improve communication
with the general public (Túñez & Sixto, 2011; Cam-
pos-Dominguez, 2017). In fact, some authors claim
the leadership of political class in the usage of new
technologies (Guerrero-Solé and Mas-Manchón,
2017). According to López-García (2017), Twitter
undoubtedly constitutes one of the social networks
best adapted to the nature of political communica-
tion, which is the interrelation between three actors
(politicians, media, and citizens) in the same space.
Nowadays, it is not at all rare that a public ad-
ministration creates a hashtag on Twitter related to a
campaign created and developed to sensitize the gen-
eral public and raise awareness of the difculties and
recommendations to overcome the health crisis. The
institutional campaign #EsteVirusloParamosUnidos
started with a press campaign: the most important
legacy media in Spain (El País, El Mundo, ABC, La
Razón, La Vanguardia, El Periódico, amongst others)
appeared on March 15th, 2020, with the same cover
and the hashtag of the campaign. Some legacy media
from other countries in South America imitated the
press campaign, such as Puerto Rico (El Conden-
cial, 2020a), Argentina, México, and Perú (Lubianco,
2020).
The campaign has been released by the Spanish
Ministry of Health Sanitary Spanish Ministry and
has been created by the Spanish advertising agency
Kitchen. It was dealt with urgently, under Article 16,
Royal Decree Law 7/2020 (March 12th). According to
El Condencial Digital (2020) and Dircomdencial
(27/3/2020), the cost of the media buying campaign
was 4.5 million euros and was the most important
campaign in Spain during March 2020. Amongst
other institutional campaigns, it was also the subject
of questions to the government by the opposition
(PP/ Partido Popular) in relation to the awarding of
institutional advertising campaigns. (EuropaPress,
2020). The campaign #estevirusloparamosunidos has
a digital aspect, including a website (Ministerio de
Sanidad, 2020), Facebook (Ministerio de Cultura y
Deporte, 2020), Telegram, banners, a YouTube vid-
eo, and the hashtag on Twitter, as well as a tradition-
al one comprising media, cradle, and posters. As of
May 22nd, 2020, Telegram and Twitter have the best
results in terms of quantity of users engaging with the
campaign.
Table 1. Number of views and likes
of the ofcial social network channels of campaign
#EsteVirusLoParamosUnidos
Social Network Visits/ Subscriptors* Likes
Facebook 14.000 268
Youtube video 1,516,021 2.5K
Telegram 3.414.000 Does not apply
Twitter over 3.000.000 of tweets Does not apply
*Spanish Ministry Ofcial Telegram channel
4. Research Questions and Objectives
These are the research questions that we will try to
answer in this paper by analyzing the hashtag #es-
tevirusloparamosunidos during the aforementioned
period.
RQ1: Who were the opinion leaders in the cover-
age of the pandemic?
RQ2: What is the typology of the main actors on
Twitter?
1231Tous-Rovirosa, A., & Dergacheva, D. Estud. mensaje period. 27(4) 2021: 1227-1241
RQ3: Who are the main Twitter users disseminat-
ing the information in the ego networks of the most
important actors in this campaign on Twitter?
Using SNA techniques, the main objective of this
research is to analyze and visualize which network is
woven inside the hashtag #estevirusloparamosunidos
circulating on Twitter around the Covid-19 pandem-
ic. The specic objectives are the following:
1. To identify the main actors. Find out which
are the users that have relevance or authority
2. To study which are the main interactions in
the network.
3. To observe whether there are any coinci-
dences between the spokesperson(s) at press
conferences and the spokesperson(s) at the
space created on Twitter.
5. Method
This study examined the Twitter hashtag #estevirus-
loparamosunidos during the peak of the coronavirus
pandemic in Spain. The analyzes were based on four
data sets of publicly available tweets with the hashtag
collected during the dates March 31st – April 4th, 2020.
The total sample included 398,523 tweets in four data
sets. Through Netlytic software and ORA software,
we created a Social Network Analysis and comput-
er-based text analysis of the tweets from March 31st
– April 4th, 2020. This computer-based text analysis
records the word frequencies during these dates in
the campaign #estevirusloparamosunidos.
On Twitter, Netlytic is widely used to study hash-
tag campaigns, political activism, and political com-
munication. Some of the latest examples include an
analysis of feminist identity by Lommel et al. (2019)
through research on the international hashtag cam-
paign #DayWithoutAWoman; Romeiro et al. (2021)
analyzing a protest campaign by the opposition in
Brazil during Covid-19 with a hashtag #Somos70por-
cento; and Figueiredo (2021) studying #dia26euvou
in Brasil which deconstructed the left-wing protest
movement narrative. ORA is software included in
Sage’s handbook on Social Network Analysis (Scott
& Carrington, 2014). It is commonly used by stud-
ies in political communication and social networking
sites (e.g., among the latest – Mercea et al.: 2020;
Pavan: 2020).
6. Network Data collection
The data was collected from Twitter from March 21st,
2020 – April 20th, 2020. Twitter’s followers’ network
is a directed graph where nonreciprocal relations are
permitted (Morales et al., 2014). All publicly availa-
ble tweets with the hashtag #estevirusloparamosuni-
dos were collected. Overall, 1,232,759 tweets were
collected over the period of 31 days. We took a sam-
ple of tweets for the week when the pandemic was
peaking. As the worst day of the pandemic in Spain,
in terms of deaths from Covid-19, was April 2nd, we
chose the sample of tweets from the week of March
31st – April 4th. The sample included 398,523 tweets.
They were analyzed via social network analysis rst
as name networks for each day of the peak. The name
network analysis studies the messages’ content and
connecting nodes (in our case – Twitter users) be-
tween each other in case they reply, repost, or men-
tion another user’s tweet (Gruzd: 2009). A Twitter
actor, or name, network shows us who interacts with
whom in relation to a hashtag or search term (Gra-
ham and Ackland: 2017).
The resulting Name Networks were exported to
ORA software in GraphML format, a network visual-
ization application (Gruzd, Haythornthwaite: 2013).
We then created a dynamic network that showed
how the main actors’ posting behavior and impor-
tance for the network (centrality measures) had
changed during this period. This dynamic network
had 21,397 nodes, meaning that the total number of
Twitter posters connected to each other in a network
during this week was 21.397. As there were a great
deal more tweets, it means that some actors had post-
ed far more than once or twice. In fact, some of them
had posted hundreds of tweets.
For the purpose of analysis, the four input me-
ta-networks were divided into three periods: Begin-
ning (March 31st), Middle (April 1st-2nd), End (April
3rd-4th).
Some nodes were excluded as they represented
a part of another hashtag, for instance, SEGUIMOS
LUCHANDO JUNT@S
1232 Tous-Rovirosa, A., & Dergacheva, D. Estud. mensaje period. 27(4) 2021: 1227-1241
7. Results
Table 2. Twitter accounts of the opinion leaders that appeared most frequently in multidimensional network
built on the hashtag #estevirusloparamosunidos (March 31th – April 4th 2020)
# Beginning Value # Middle Value # End Value
1 sanidadgob 0,110 1 antobald98 0,065 1 sanidadgob 0,148
2 sanchezcastejon 0,106 2 sanidadgob 0,054 2 mercadona 0,054
3 mitmagob 0,088 3 sanduhadah 0,024 3 ejercitoaire 0,046
4 guardiacivil 0,047 4 tc_disisleri 0,024 4 salvadorilla 0,040
5 policia 0,044 5 tcsavunma 0,024 5 elprogramadear 0,037
6 ejercitotierra 0,035 6 tcbestepe 0,024 6 guardiacivil 0,034
7 ejercitoaire 0,034 7 mercadona 0,023 7 defensagob 0,028
8umegob 0,030 8camillebertrand 0,021 8sanchezcastejon 0,027
9sevillafc 0,024 9brandspain 0,020 9mitmagob 0,026
10 nato 0,022 10 dani78175141 0,018 10 feriademadrid 0,025
11 diba 0,022 11 jaarcossanchez 0,016 11 policia 0,024
12 laliga 0,021 12 mossos 0,16 12 valenciacf 0,023
13 proteccioncivil 0,020 13 barcelona_gub 0,16 13 24h_tve 0,023
14 maecgob 0,018 14 embespturquia 0,16 14 rccelta 0,022
15 mjmonteroc 0,017 15 mevlutcavusoglu 0,16 15 aspas10 0,021
16 yosoy8a 0,016 16 eldiarioes 0,16 16 populares 0,020
Source: Own elaboration
RQ1 asked who the opinion leaders were in the
coverage of the pandemic.
Table 2 presents opinion leaders that appeared most
frequently on Twitter in each period. To measure a
Twitter user’s inuence in a network, we used in-degree
centrality. The in-degree centrality denes how many
network ties were pointed towards a user (Borgatti &
Everett: 2018). Researchers previously dened opinion
leaders as having an in-degree centrality of at least 10%
(Valente and Pumpuang, 2007). This research uses the
2% threshold because of the large scale of the Twitter
networks analyzed, and as suggested by previous Twit-
ter research (Guo et al., 2018), this could ensure that
the study detects a broad range of users. A total of 38
unique opinion leaders were identied. The analysis of
in-degree centrality was carried out on the whole net-
work over all the actors who had in-degree over 2%. So,
some were added in periods 1 and 3, and some were de-
leted in period 2, for instance, because they didn’t pass
the threshold. All in all, there are those Twitter users
– opinion leaders, who passed a 0.02 threshold accord-
ing to the in-degree centrality.
To better understand the composition of the
network based on the hashtag #esteviruslopara-
mosunidos, we also visualized and analyzed the
Twitter network in Netlytic, using three periods
of collection. Because the networks are very
large, and constitute around 100,000 tweets for
each period, only the largest connected clusters
(components) were displayed, while isolated
nodes and small disconnected clusters were not
taken into account (Gruzd & Tsyganova, 2014).
Each of the five clusters of each period was thus
visualized, and based on the visual examination,
the main actors were identified. The visualization
was done day by day.
They represent the most important nodes in a con-
crete visible cluster that Netlytic identied. As can be
observed, the most repeated ones in Table 2, during
the three periods, are sanidadgob; sanchezcastejon;
mitmagob; guardiacivil; ejercitoaire, and mercadona.
It can be noted in the following graphs (See also Ta-
ble 3) that these were the main stakeholders during
the peak of the pandemic.
1233Tous-Rovirosa, A., & Dergacheva, D. Estud. mensaje period. 27(4) 2021: 1227-1241
Figure 1. March 31st. All clusters
Figure 2. April 1st-2nd. All clusters
Figure 3. April 2nd-4th. All Clusters
RQ2 was about the typology of the main actors of
the hashtag. We categorized the main actors (those
with the highest in-degree centrality of the network
over time). The following table presents the typolo-
gy. The main categorization is between politicians,
administration and institutions, and other social ac-
tors, which we are not mentioning further, such as
sportsmen, inuencers, businesses, and the like.
1234 Tous-Rovirosa, A., & Dergacheva, D. Estud. mensaje period. 27(4) 2021: 1227-1241
Table 3. Politicians, administration and institutions’ Twitter accounts that appeared most frequently
on Twitter during the period of data collection of multidimensional network built on the hashtag
#estevirusloparamosunidos (March 31th – April 4th 2020)
Spanish Government Armed Forces Spanish Administration International
Bodies
Other
Governments
Political
parties
Sanidadgob - Ministerio de Sa-
nidad (Ministry of Health)
Guardia Civil diba - Diputació de Barcelona
(Barcelona Provincial Coun-
cil)
nato - Ofcial Twitter account
of NATO - the North Atlantic
Treaty Organization
tc_disisleri - Ministry of Fore-
ign Affairs of Turkey
tcsavunma Republic of Turkey
Ministry of National Defence
tcbestepe –administration of
the President of Turkey
@populares – Partido Popular
Mitmagob - Ministerio de
Transportes, Movilidad y
Agenda Urbana, Gobierno de
España.
Ministry of Transport, Mobili-
ty and Urban Development
Policia- Police
sanchezcastejon Pedro Sán-
chez, Presidente del Gobierno
de #España.
President of the Spanish gover-
nment, Pedro Sanches
Ejercitotierra – the Spanish
army
Salvador Illa Roca - Ministro
de Ministerio de Sanidad
Head of the Health Ministry of
Spain
Ejercitoaire – the Spanish Air
force
umegob - Unidad Militar de
Emergencias
Military Emergencies Unit
Source: Own
RQ3 tried to answer which are the main Twitter
users disseminating the information in the ego net-
works of the most important actors in this campaign
on Twitter
As Everett and Bogartti (2005: 32) pointed out,
“ego networks are drawn from the same basic net-
work, and the objective is to give a network measure
of importance to enable us to compare the central-
ities of the egos in our sample.” For the purpose of
this research, we take the denition of ego networks
by Bogatti et al. (2005). Perhaps a more simplistic
phrasing: Ego-networks consist of a single actor
(ego), the actors connected to it (alters), and all the
links between them.
From the main actors of the Netlytic visibly identi-
ed clusters, ve ego-networks of this particular cam-
paign were analyzed, the prerequisite being that they
were the central actor in a cluster at some point in time.
As stated before, the main actors identied through vis-
ualization and in-degree centrality were: sanidadgob;
sanchezcastejon; mitmagob; guardiacivil; ejercitoaire
and mercadona. As there is a majority of institutional
and political actors, we decided to focus on these and
leave aside the commercial Twitter account (mercado-
na), which we have categorized as “other social actors.”
We can observe which was the ego-network and
betweenness of these ve main actors. Below are the
graphs for the president of the government, Pedro
1235Tous-Rovirosa, A., & Dergacheva, D. Estud. mensaje period. 27(4) 2021: 1227-1241
Sánchez; the rest can be found in the appendices. The
information that we can visualize in these graphs has also enabled us to identify the main actors inside each
ego-network.
Graph 1. Ego-network of the president of the Spanish government, Pedro Sánchez
Table 4
Red Highest
Orange Intermediate
Yellow Medium-level
Green Low
Blue Lowest
In particular, we paid specic attention to the
betweenness centrality in these ego-networks. Be-
tweenness studies the extent to which an actor is be-
tween all other actors within the network (Borgatti &
Everett, 2005), and the more central it is, the more
important for information dissemination the actor
would be. Betweenness centrality identies individ-
uals or organizations that are potentially inuential
and are positioned to impose connections between
groups and to bring to bear the inuence of one group
on another or serve as a gatekeeper between groups
(Carey, 2014).
As with the in-degree centrality typology, we
read through Twitter proles and available tweets
and identied the main ten actors with the highest
betweenness centrality in these ve ego-networks
(excluding egos themselves).
In many cases, we have found that the users that
constitute the most important actors in terms of be-
tweenness centrality in our ve ego-networks are the
same individuals or institutions and mostly from the
socialist political spectrum. For instance, Twitter ac-
counts of the same Socialist Party supporters, as re-
corded in their Twitter accounts (@Teresaperezcep1;
@KilianCD; @Perona10690463; @937908Mcm; @
AlegraAlonso; @nusagatero) are present not only
in one ego-network but several. Moreover, Social-
ist Party members or supporters are present in every
ego-network´s top betweenness centrality actors.
Betweenness centrality in ego-networks of the
main actors of the multidimensional network built on
the hashtag #EsteVirusloParamosUnidos (March 31st
– April 4th)[1]
[DD2] [DD3] As it is visible from the table and
the node clouds, the majority of actors important for
the dissemination of information in the ego-networks
of most important speakers of the campaign, are pol-
iticians from the Socialist Party or government or-
ganizations.
Below are the node clouds of the ego-networks
of Pedro Sánchez, as one of the most important ac-
tors in this campaign. The other most important ac-
tors’ clouds can be checked in Appendices. They are
colored according to betweenness centrality, with
red being the highest. As it is visible from the node
clouds, the previous conclusions about main actors
supporting the opinion leaders are, in many cases,
Twitter proles of either members of the Socialist
1236 Tous-Rovirosa, A., & Dergacheva, D. Estud. mensaje period. 27(4) 2021: 1227-1241
Party or government/military organizations. As for
the ego-network’s color code, for node clouds, red stands for the highest betweenness centrality, and
blue stands for the lowest betweenness centrality.
Table 5 Betweenness Centrality in Ego Networks of the main actors of multidimensional
network built on the hashtag #estevirusloparamosunidos (March 31th – April 4th 2020)
Whose network Socialist party members or
supporters
Government organization/
military Others
President of the Spanish government, Pedro Sánchez 90 0
Sanitary Spanish Ministry 7 1 1
Spanish Army 3 3 3
Spanish Air Force 270
Spanish Guardia Civil 3 2 4
Source: Own elaboration
Graph 2. Node cloud of the ego-network of the president of the Spanish government, Pedro Sánchez
Computer-based text analysis
The following section describes is the comput-
er-based text analysis of the word frequencies from
March 31st – April 4th, 2020 in the campaign #Es-
teVirusloParamosUnidos. Through Netlytic soft-
ware, we made a computer-based text analysis of the
tweets from March 31st-April 4th, 2020. We removed
the ‘meaningless’ words such as verbs, pronouns, and
some common nouns such as “días” or “marzo.” The
most mentioned hashtag, #EsteVirusloParamosUni-
dos, has fewer mentions than tweets for this period
(39,4675 mentions vs. 398,523) because the compu-
tational text analysis did not consider the same hash-
tag written with emojis, for example. What is visible
from the twenty most mentioned words is that the
word ‘government’ was mentioned far than medical
words (such as ‘sanitario’ or ‘sanidadgob’) and that
some military-style terms are present, such as ‘lucha’
(ght) and ‘alarma’ (alarm), as well as administrative
words ‘medidas’ (measures). The words related to
health and sanitary issues such as ‘esenciales,’ ‘tra-
bajadores’ (essential workers), and ‘actividades’ (ac-
tivities) are at the very end of this list.
Table 6. The most mentioned words and hashtags in the texts of tweets of corpus built
on the hashtag #estevirusloparamosunidos (March 31th – April 4th 2020)
# Word in Spanish English translation Number of mentions
1 #estevirusloparamosunidos
hashtag: “we will stop this virus together” 394.675
2#covid19
hashtag “covid19” 85.635
3 Gracias thank you 39.939
1237Tous-Rovirosa, A., & Dergacheva, D. Estud. mensaje period. 27(4) 2021: 1227-1241
# Word in Spanish English translation Number of mentions
4 España Spain 36.402
5 Gobierno Government 33.407
6 Medidas Measures 30.223
7 #quédateencasa hashtag: “stay at home” 24.566
8Sanitario Sanitary 23.165
9Casa Home 20.360
10 Personas Persons 19.470
11 Crisis Crisis 12.987
12 Alarma Alarm 12 560
13 #coronavirus hashtag “coronavirus” 12.354
14 Material Material 12.128
15 Lucha Fight 11.631
16 @sanidadgob Twitter account of the ministry of health 11.231
17 Esenciales Essential 10.338
18 Trabajadores Workers 9.718
19 Actividades Activities 9.670
20 Compañeros Fellows 8.034
8. Discussion and Conclusions
Besides having answered the research questions,
there have been some topics that emerged thanks
to this research. We can see there is a high level of
coincidence between the typology of main actors in
press conferences (which were broadcast by TVE
and almost all generalist channels at that time) and
in the hashtag analyzed, as the communication strat-
egy from the Spanish government consisted clearly
of prioritizing the armed forces (guardiacivil, ejer-
citoaire) in addition to the Spanish administration
(sanidadgob, sanchezcastejon, mitmagob). We have
to bear in mind that the analyzed hashtag belongs to
a wider institutional campaign, #estevirusloparamo-
sunidos, whose aim was to sensitize the citizens. As
stated previously, the computer-based text analysis
reinforces these results, along the lines that the em-
phasis was placed on administration and war-like
language above the health and sanitary issues.
It is fairly clear that the main opinion leaders had
a strong relationship with their “natural spectrum.”
The results show us that, due to fragmentation and
segmentation, this is not only a “segmented appeal”
(Gibson, 2015: 184). We are not exactly talking about
an “elite” of main opinion leaders, but a fairly closed
group (niche) that particularly interacts with its nat-
ural common sphere, with few exceptions (Partido
Popular in Table 1, position 16th, last period analyz-
ed).
It is also an interesting nding that several of the
spokespersons in press conferences do not appear as
relevant actors of these networks inside the top posi-
tions– particularly not as personal Twitter accounts.
Neither the majority of ministers (with the exception
of the Ministry of Health, Salvador Illa, and mitma-
gob) nor the majority of military personell. Instead,
there is a huge leadership of the aforementioned of-
cial accounts representing the same social and in-
stitutional stakeholders. So, this is a sphere that may
be open to outside participation, but, in fact, it only
attracts similar actors. This could be studied in fur-
ther research analyzing its political homophily (Guo
et al., 2020). It is also interesting to realize that Pe-
dro Sánchez and Salvador Illa have more socialist
individuals supporting them in the dissemination of
information, whilst the military has fewer. We have
observed that there is a high relationship between
politicians and militaries in this hashtag, but each of
them remains to have their own “circle.” It is also
important to highlight that ejercitoaire, ejercitotierra,
and guardiacivil did share “saludpublica,” as we can
observe in the ego-network’s clouds.
In this sense, we point out that the behavior ob-
served in the social networks analyzed coincides with
social fragmentation (Liao and Fu, 2014) and homo-
geneous communities (Guo et al., 2020) and could
lead to the so-called echo chambers and polarization.
Therefore social networks, in this case, Twitter, would
reach the opposite objective to that which the Inter-
net was supposed to produce in its beginnings (Mere-
dith, 2013) and, of course, the opposite objective that
the government administration should pursue with
its communication with citizens through institution-
al campaigns. The results of our research are fully in
line with other research on polarization, as they reach
1238 Tous-Rovirosa, A., & Dergacheva, D. Estud. mensaje period. 27(4) 2021: 1227-1241
similar conclusions about homogeneous communities
(Gutiérrez et al., 2021). On the other hand, topic-mod-
eling research concludes that, during the lockdown,
users focused on “the Spanish emergency, considered
health and economic problems,” meanwhile at the
pre-crisis stage, they focused on the international pan-
orama (Agüero-Torales et al., 2021, 186). Although it
is not possible to compare results, due to their method-
ological and sample differences, we must emphasize
that these are messages and language intrinsically re-
lated to the domestic and national sphere.
We can also conclude that the network analyzed
has an autoreferential character (García-Ortega &
Zugasti, 2018), as the main opinion leaders keep in-
teracting with citizens that are particularly politically
aligned to the socialists; furthermore, because of the
“behavior” of its main opinion leaders, as they inter-
act especially with themselves and with related polit-
ical actors. So, we have clearly studied an “in-group”
(Cifuentes & Pino, 2018) that may have “out-groups”
over there. This would certainly lend itself to further
interesting research.
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Anna Tous-Rovirosa. Associate professor at Journalism and Communication Sciences at UAB (Bellaterra,
Spain). Doctor from the same University, her PhD was awarded by the Catalan Audiovisual Council (CAC).
She has been a visiting scholar in the universities of Salvador de Bahia (Brazil), Bochum (Germany) and East
Anglia (Norwich, UK). Her research topics are online journalism, interactivity and TV studies. The results of
their research are available at books and international journals such as Journal of Spanish Cultural Studies, El
profesional de la información and Palabra Clave, amongst others. ORCID: http://orcid.org/0000-0003-4519-
3793
Dr. Daria Dergacheva has just defended her PhD dissertation with distinction at the Autonomous University
of Barcelona. She received a Chevening scholarship and graduated from the School of Media at the University
of Westminster in 2013. Before that, she worked in the Russian media for over 10 years. She has published in
international journals such as Russian Journal of Communication and Quaestio Rossica, amongst others. OR-
CID: http://orcid.org/0000-0001-8773-7934
