TikTok and political communication: interaction patterns and engagement rate of candidates and parties in an election campaign

TikTok y comunicación política: pautas de interacción e índice de engagement de candidatos y partidos en campaña electoral

Julen Orbegozo Terradillos

University of the Basque Country. Spain. 

julen.orbegozo@ehu.eus 

 

Ainara Larrondo Ureta

University of the Basque Country. Spain. 
ainara.larrondo@ehu.eus 

 

 

Jordi Morales i Gras

Chamber of Bilbao University Bussines School. Spain. 
jordi.morales@camarabilbaoubs.com 

 

Funding: This research received funding from the Gureiker research group (IT1496-22), category A (2022/2025).  

 

 

ABSTRACT

Introduction: This study examines the influence of TikTok on political communication during the municipal and regional elections in Spain in 2023, highlighting the importance of this platform as a crucial emerging medium for political engagement. Additionally, it focuses on the progressive "lack of relationship" on digital platforms, which are more centered on content and entertainment than on interpersonal relationships. Methodology: Computer tools were used for data capture and visualization, and Google Colab for writing and executing Python scripts. In addition, a correlation matrix is used to analyze the engagement variables of 65 accounts and determine the strength and direction of the relationships between them. Furthermore, a novel formula is employed to calculate the Engagement Rate, a cornerstone of the research and a metric that allows observing the phenomenon through variables such as comments, likes, views, number of followers, and number of posts. Results: General data (presence, activity, interactivity, positive interaction, visualisation and dissemination of content), the temporal distribution of posts, the Engagement Rate and the correlation of metrics are inferred. An overview of activity and interactivity patterns of parties, candidates and users of the social network is thus obtained, finding significant variations in engagement. Discussion and conclusions: The research highlights the way TikTok is transforming political communication, demonstrating that success hinges not on conventional elements, but on digital strategies and alignment with the algorithm. Although some parties experienced significant engagement, this did not translate into electoral victories, suggesting a shift towards politainment and mass content consumption. 

Keywords: TikTok; social networks; political communication; electoral communication; social network analysis; big data; engagement rate.

 

RESUMEN

Introducción: Este estudio examina la influencia de TikTok en la comunicación política durante las elecciones municipales y autonómicas de España en 2023, destacando la importancia de esta plataforma como canal emergente esencial para el engagement político. Lo hace, además, enfocándose en la progresiva “desrelacionalización” de las plataformas digitales, centradas más en el contenido y en el entretenimiento que en las relaciones interpersonales. Metodología: Se utilizaron herramientas informáticas para la captura y la visualización de datos, y Google Colab para escribir y ejecutar scripts en Python. Se emplea una matriz de correlaciones para analizar las variables de engagement de 65 cuentas y determinar la fuerza y dirección de las relaciones entre ellas. Por otro lado, se aplica una fórmula novedosa para calcular el Engagement Rate, pilar de la investigación y métrica que permite observar el fenómeno a través de variables como comentarios, “me gusta”, visualizaciones, número de seguidores y número de publicaciones. Resultados: Se infieren los datos generales (presencia, actividad, interactividad, interacción positiva, visualización y difusión de contenidos), la distribución temporal de posts, el Engament Rate y la correlación de métricas. Se obtiene una perspectiva general de patrones de actividad e interactividad de partidos, candidatos y usuarios de la red social, hallando variaciones significativas en el engagement. Discusión y conclusiones: El estudio destaca cómo TikTok contribuye a remodelar la comunicación política, sustentando ésta en nuevos parámetros de éxito, dependientes de estrategias digitales y de la adaptación al algoritmo, más que de factores tradicionales. El alto engagement de ciertos partidos no obtiene reflejo en términos de éxito electoral, apuntando más bien hacia una evolución en la línea del politaintment y el consumo masivo de contenidos. 

Palabras clave: TikTok; redes sociales; comunicación política; comunicación electoral; análisis de redes sociales; datos masivos; índice de compromiso. 

1.      INTRODUCTION

In the era of the digital revolution (Clarke, 2012; Charlesworth, 2018), social networks have transcended their role as spaces for personal interaction to become scenarios of the public agora that are decisive for the dissemination of political discourses. In this context, consolidated platforms such as X (formerly Twitter) and Facebook, traditionally favored for the development of political campaigns, have given way to new digital social networks such as Instagram or TikTok, especially when targeting new audiences and younger generations (Alonso-López et al., 2023). 

TikTok, owned by the Chinese company ByteDance (Malaspina, 2020), was created in 2016 and is a micro-video platform characterized by short entertainment-oriented content and combining highly addictive algorithms (Wang, & Guo, 2023). TikTok has emerged as the trendy social network, especially among the younger generations (Bossen, & Kottlasz, 2020), although content produced specifically for its environment is already shared on other referential social networks such as X or Instagram, or messaging services such as WhatsApp or Telegram. 

TikTok expanded internationally in 2018 after its merger with the Musical.ly app (Brennan, 2020). It is a digital platform with its own characteristics, especially aimed at producing, sharing, and consuming creative short videos, ranging in length from 15 to 10 minutes (Cheng, & Li, 2023). The videos are recorded vertically, not horizontally, and cover a wide range of content, such as dances, challenges, tutorials, political or educational content, etc. The user navigates through its interface by scrolling up and down their screen and those who create the videos have at their fingertips all kinds of tools such as filters and a huge variety of popular sounds to edit their videos (Herrman, 2019).

In this context, it is clear that TikTok has contributed to significantly reshape part of the electoral communication strategies, as well as the way in which citizens currently consume political speeches (Cervi et al., 2023). This phenomenon has captured the attention of various sectors, including the scientific and academic fields, and has opened new horizons for political communication. Therefore, the progressive use of this tool by political representatives deserves a rigorous academic analysis such as the one presented in this paper. 

In fact, this work constitutes an empirical approach to an incipient social network at a crucial moment for the expansion of TikTok as a political communication tool; and it is developed from a computational approach, acquiring data programmatically, comparing specific metrics that are determinant in this platform and offering a renewed perspective of a basic concept in the world of communication marketing: the engagement of users with certain products, services or content. Thus, new knowledge is contributed to the emerging phenomenon of electoral campaigns in TikTok, with a comparative methodology that can be applied to other phenomena.

Thus, the object of this research is to study the electoral communication developed on TikTok by candidates and political parties in the main Spanish cities and autonomous communities during the municipal and autonomous community elections held on May 28, 2023 in the country. The study is positioned at the forefront of the analysis of digital political communication and provides empirical evidence on the patterns of action and interaction of political subjects in an electoral context, during the first local elections in Spain, with TikTok at full capacity. Moreover, it does so from the perspective of a term, that of the user's digital engagement processes (Ballesteros, 2019), which deserves to be re-studied by the scientific community, due to the adaptation of digital social networks to the new communicative paradigm.

In this regard, the research starts from a novel approach, taking as a starting premise the following question: digital social networks, which originally had a function of regrouping friendships (relational perspective), have now evolved into digital platforms oriented to mass entertainment and content consumption in a transversally digitized context (playful-intensive perspective). This fact, as it cannot be otherwise, has had an impact on the logics of participation of content producers and consumers in digital social networks.

1.1.  TikTok and political communication

The influence of TikTok in various areas is notorious, especially in fields such as digital marketing, as it offers new business opportunities in the field of corporate communication (Guarda et al., 2021; Peng, 2021; Flecha-Ortiz et al., 2023); cyberjournalism, as an open door to new journalistic formats (Peña-Fernández et al., 2022); or political communication itself, as a favorable tool for reaching new young audiences through the spectacularization of the message and political content, as well as the humanization of candidates (Cervi et al., 2023; Gómez-García et al., 2023). 

For its part, the field of political communication is a field of study with great potential that can benefit from the so-called data mining and new computational tools capable of capturing and processing the contemporary digital footprint. As Sánchez (2021) recognizes, in the field of political communication there is a “high lack of knowledge of the average TikTok user and it is necessary to attend to the future movements that leaders and their parties may make in this social network” (p. 223).

Apart from this gap regarding the use of big data and computational tools to study the dynamics of this platform, there are other works in the field of political communication that deserve to be highlighted. The scientific community has focused especially on electoral campaigns or campaigns related to public administrations developed in countries such as Spain (Alonso-López et al., 2024; Cervi et al., 2023; Cervi et al., 2021; Morejón, 2023; Gamir-Ríos, & Sánchez-Castillo, 2022) and Peru (Cuevas-Calderón et al., 2022). 

TikTok burst into Spanish politics in a generalized way in the Madrid elections of 2021 (Moreno, 2023) and in the international context it has been used in other recent electoral campaigns such as the Italian elections (Battista, 2023) or the American midterms (Aiyappa et al., 2023; Seppälä, 2022). The Spanish political and cultural context, however, is particularly interesting because TikTok was launched in the country in 2018 and in the five years of operation has already achieved 18.3 million users and a 75% penetration among the 12- to 17-year-old age group (IAB Spain, 2023). According to the We are Social - Metwater study (We Are Social, 2023) 47.3% of Spaniards use TikTok in the 16-64 age group. In addition, during 2020, 2021 and 2022 it was the most downloaded application in the world (Koetsier, 2023), as well as the domain with the most cyber traffic on the planet (Rayon, 2023).

1.2.  Engagement in the new era of social networks

New digital social networks such as TikTok increasingly focus on content-related elements rather than on interpersonal relationships, which alludes to the aforementioned “deregulation” of platforms. This phenomenon has already been detected in studies such as that of Faltesek et al. (2023), which point to the relevance of flow structures for organizing the content offer of contemporary digital social networks. This raises, unsurprisingly, new questions for the scientific community, which thus faces the challenge of analyzing what happens in TikTok in the face of this new paradigm and in terms of engagement.

The concept of engagement can be defined in many ways. Works such as Raposo et al. (2022) or Moreno and Fuentes (2019) carry out a detailed review of the phenomenon, including one of the original definitions by Bowden (2009), who relates engagement to the psychological process that models the underlying mechanisms by which loyalty is formed for customers or users of a given brand. 

The assessment of engagement —also referred to as the user's level of commitment and involvement— in digital media has historically employed three methods of choice: surveys and interviews, implicit measures and web analytics Chan-Olmsted et al., 2017). In this study, focus is on web analytics, which tries to know the engagement through the behavior shown by users through their activity on digital platforms (Ballesteros, 2019). In fact, with the spread of the Internet and social media the availability of digital traces makes it possible to generate a lot of data that allow investigating social dynamics, collective behavior patterns, influence between people and information dissemination mechanisms (Laniado, & Viles, 2018). 

To this end, as is done in this research, a series of metrics related to the digital interaction patterns specified in the methodological section are collected -through computational methods of data capture and processing. It should be noted that each variable analyzed contains a specific meaning in its context. For example, Triantafillidou et al. (2015) theorize around the average number of “likes” and relate it to a kind of user attitude towards certain content; other variables such as comments are related in other studies to the concepts of virality or expressiveness indexes that elicit certain inputs (Barger, & Labrecque, 2013; Bonsón, & Ratkai, 2013). 

However, scholars have traditionally studied this issue from an eminently commercial perspective, relating the concept of branding to that of corporate communication (Li et al., 2023) and focusing most analyses on the weighting of three variables such as the number of “likes”, shares and comments (Hoffman, & Fodor, 2010; Chug et al., 2012). In this research, three other fundamental parameters are added to the equation in the new digital context: plays, number of posts and number of followers, something that is novel in the academic context.

In fact, what is really striking is that in the various formulas used so far in the scientific field, the variables related to the mere potential of a given content to be consumed in isolation or decontextualized are undervalued. This could be seen as a preceding phase in the analysis of digital social networks, where a kind of pure attraction (in a context of consumption unlinked to other parameters) of the published content was underestimated. This phase is giving way to an analytical context where relational metrics (number of followers, content typology, etc.) lose power. 

In this regard, the aforementioned equations, already explored in the academic context, underestimated parameters such as the number of views —privileging more informative or persuasive approaches—, a variable related to a predominantly playful approach attributed to social networks in the contemporary context. Thus, the variable visualizations is included in the formula used in this research, as well as the number of followers an account has. This evolution in the weighting of the Engagement Rate reflected in this study is a contribution that is as necessary as it is novel in the new context of contemporary digital social networks. 

2.      RESEARCH OBJECTIVES AND QUESTIONS

The objective of this research, located at the intersection between digital technology and political processes, is to examine and understand the dynamics of political communication on the TikTok platform during the aforementioned elections. Specifically, the study analyzes the logics of action, viralization patterns and engagement on TikTok in an electoral context, in addition to measuring the eventual professionalization of political activity with parameters related to the frequency of “posting” or the lexicon used in digital accounts.

The research questions guiding the study are the following: 

-       Through their activity on TikTok, do individual and party accounts show professionalized patterns of action in terms of issues such as regular “posting” frequency, balanced temporal distribution of their messages or the specific and intentional lexicon employed? (RQ1).

-       Is there any correlation between the level and forms of participation and variables such as gender, social implantation of the party, ideology of the political subject or territorial sphere of influence? (RQ2)

-       What is the engagement of each account analyzed and with which variables (comments, “likes”, shares, number of followers, etc.) is this metric related? (RQ3).

3.      METHODOLOGY 

3.1.  Context, selected sample and hypotheses

This study takes place during the municipal and autonomic elections held in Spain on May 28, 2023. The monitored period spans from May 1, 2023 to May 31, 2023, and coincides with the last days of the electoral pre-campaign, the campaign days and the three days after voting day. Data are collected on June 10, 2023, two weeks after Election Day.

The selected sample is purposive and non-probabilistic, and representative in relation to its study objective. To obtain it, the following steps and criteria are established: 

-       Two types of accounts are defined: individual profiles (of male and female candidates for municipal elections) and party profiles (national and regional). 

-       Subsequently, the digital social network is monitored to obtain the sample, starting with the criteria of the capital cities and the most populated autonomous communities in Spain (and which hold elections). Thus, the sample guarantees accounts of parties and candidates belonging to Andalusia, Catalonia, Community of Madrid, Galicia, Castile and Leon and the Basque Country, the most populated regions that held elections. Obviously, the sample excludes candidates who, despite being head of the list in their respective municipalities, do not have official profiles on TikTok or who, despite having a profile, have not published any content.

-       Finally, the sample is composed of 65 accounts, 24 accounts of political parties and 42 of candidates in provincial capitals (see tables 1 and 2), which represents the total population of party and candidate accounts in the territories selected for this study. 

Table 1. List of analyzed accounts belonging to political parties.

Parties	Scope of action	Account
PSOE	National	@psoe
PP	National	@partidopopular
Ahora Podemos	National	@ahorapodemos
VOX	National	@vox_espana
Pacma	National	@partidopacma
Adelante Andalucía	Andalusia	@adelante_andalucia
Aragón Existe	Aragon	@aragonexiste
Barcelona en Comú	Catalonia	@barcelonaencomu
Esquerra Republicana	Catalonia	@esquerrarepublicana
Junts Per Cat	Catalonia	@juntspercat
Compromis	Valencian Community	@compromis_net
Euskal Herria Bildu	Euskadi and Navarra	@ehbildu
Foro Asturias	Asturias	@foroasturias
Más Madrid	Madrid	@mas_madrid
VOX Madrid	Madrid	@madrid.vox
Bloque Nacionalista Galego	Galicia	@obloque
Podemos Madrid	Madrid	@podemosmadrid
PP Madrid	Madrid	@ppmadrid
PSOE Madrid	Madrid	@psoemadrid
Recupera Madrid	Madrid	@recuperamadrid
Soria Ya	Castile and Leon	@soria_ya
Teruel Existe	Aragon	@teruelexiste.oficial
Valents	Catalonia	@valents_cat
Union del Pueblo Navarro	Navarra	@upn_navarra

 Source: Elaborated by the authors.

Table 2. List of analyzed accounts belonging to political parties.

Candidate	Gender	Scope of action	Account	Party[1]
Ada Colau	Woman	Barcelona	@adacolau	BEC
Jaume Collboni	Man	Barcelona	@jaumecollboni	PSC
Anna Grau	Woman	Barcelona	@annagraucs	C´s
Eva Parera	Woman	Barcelona	@evapareraescrichs	Valents
Daniel Sirera	Man	Barcelona	@danielsirera	PP
Basha Changuerra	Woman	Barcelona	@bashachanguerra	CUP
Gonzalo de Oro	Man	Barcelona	@gonzalo.de.oro	VOX
J. L. Martínez Almeida	Man	Madrid	@martinez_almeida	PP
Reyes Maroto	Woman	Madrid	@marotoreyes	PSOE
Rita Maestre	Woman	Madrid	@ritamaestre	MM
Javier Ortega Smith	Man	Madrid	@ortega_smith	VOX
Roberto Sotomayor	Man	Madrid	@robertosotomayorm	Podemos
Isabel Díaz ayuso	Woman	C. Madrid	@ayusopresidenta	PP
Juan Lobato	Man	C. Madrid	@juanlobato_es	PSOE
Mónica García	Woman	C. Madrid	@monicagarciag_	MM
Aruca Gómez	Woman	C. Madrid	@arucagomez	C´s
Alejandra Jacinto	Woman	C. Madrid	@alejandrajacintouranga	Podemos
Joan Ribó	Man	Valencia	@joanribovlc	Compromís
Sandra Gómez	Woman	Valencia	@sandragomezvalencia23	PSOE
Pilar Lima	Woman	Valencia	@pilar_lima	Podemos
Carlos Mazón	Man	C. Valenciana	@carlos_mazon	PP
Hector Illueca	Man	C. Valenciana	@hector_illueca	U. Podem
Joan Baldoví	Man	C. Valenciana	@joan_baldoví	Compromís
Antonio Muñoz	Man	Seville	@antoniomunozsev	PSOE
José Luis Sanz	Man	Seville	@jlsanzalcalde	PP
Sandra Heredia Ferná	Woman	Seville	@sahefe	Adelante A.
M. Ángel Aumesquet	Man	Seville	@miguelangelaumesquet	C´s
Lola Ranera	Woman	Zaragoza	@lolaranera	PSOE
Natalia Chueca	Woman	Zaragoza	@nataliachueca_	PP
Clemente Sánchez	Man	Zaragoza	@clementesanchezgarnica	P. Aragonés
Chuaquín Bernal	Man	Zaragoza	@chuaquinbernal	Chunta
Raúl Burillo	Man	Zaragoza	@raulburillo	A. Existe
Ángel Victor Torres	Man	Canarias	@angelvictorcan	PSOE
Manuel Domínguez	Man	Canarias	@manueldominguez_pp	PP
Noemí Santana	Woman	Canarias	@noemisantanaperera	Podemos
Emiliano García Page	Man	C. L. Mancha	@garciapage	PSOE
Carmen Picazo	Woman	C. L. Mancha	@yoconcarmenpicazo	C´s
J. L. García Gascón	Man	C. L. Mancha	@jlgarciagascon	Podemos
Fernando López Miras	Man	Murcia	@lopezmirasfernando	PP
José Vélez	Man	Murcia	@pepevelez_	PSOE
José Ángel Antelo	Man	Murcia	@antelini	VOX
Mª José Ros Olivo	Woman	Murcia	@mjrosolivo	C´s

Source: Elaborated by the authors.

The final sample, diverse in general terms, brings together the following characteristics based on their typology (individual or collective account), gender, ideology and geographic distribution:
-       24 accounts belonging to 20 political parties from 11 different geographical spheres of influence. 

-       42 individual accounts belonging to candidates, 18 women and 24 men (42,8% and 57,2%, respectively), from 15 different parties and 10 different geographical areas of influence. 

The hypotheses guiding this study are the following:

-       H1. The most significant parties and candidates with greater political representativeness will show greater activity on the network and a more regular frequency of “posting”, and will obtain better results in terms of interactivity, visualizations, etc. (hereinafter, “digital engagement” and “interaction patterns”).

-       H2: Parties with better data in terms of digital engagement and interaction patterns will obtain a better Engagement Rate, concept that will not be related in a preferred way to the variables of number of followers of an account and its followers. 

3.2.  Approach, sections of the study and methodological tools

This article uses the case study research methodology (Fidel, 1984), from an eminently quantitative perspective, based on a determined number of descriptive and explanatory observations through statistical techniques (Martínez-Carazo, 2006). 

In order to achieve the objectives, the following categories of analysis were established: 

1.    General data on TikTok activity: presence, activity, interactivity, positive interaction, visualizations and diffusion. In an exploratory way, this section analyzes some significant patterns that define the activity patterns of the analyzed players and observes the parameters derived from this activity (digital engagement and interaction patterns).
2.    Temporal distribution of posts. A chronology of publications during the analyzed month is offered, to observe the “posting” logics of candidates and political parties.
3.    Engagement Rate and correlation of metrics related to parameters such as “likes” or diggs, followers, views or plays, shares, comments, etc. The objective of this section is to better understand how TikTok works and its recognition logics.

As for the tools used, the messages or data are captured through the tool provided by Ensemble Data, which allows access to activity on digital social networks such as TikTok in an indirect or unofficial way. Ensemble Data is a company founded in 2020 by engineers and mathematicians specializing in artificial intelligence, dedicated to enabling companies to leverage social network data on a large scale through an API (Application Programming Interface). The platform uses “TikTok API”, for example, to perform web scraping in Python code and collect data (Lawson, 2015; Zhao, 2017).

Thus, a data set of inputs and interactions is obtained in TikTok[2], which is then processed by sorting and grouping the data according to certain variables. Thus, groups are made by authors or accounts and unified indicators are constructed for each of them. During the data analysis phase, Google Colaboratory (Colab) is used as a coding platform for writing and executing Python scripts, which facilitates effective collaboration and efficient access to advanced computational resources. Google Colab allows working dynamically with large data sets and performing complex statistical analysis. Through this environment, code for calculating correlations and generating interactive visualizations is developed and tested.

As explained above, this work uses a novel formula to calculate the Engagement Rate, which includes the following variables related to digital social networks: 

-       Number of comments (comments, in Anglo-Saxon terminology), a metric that in this study has been related to the concept of interactivity.

-       Number of “likes” (diggs), a metric related to the concept of positive interaction.

-       Number of plays, a metric that reflects the level of exposure and reach of the content. 

-       Number of shares, a metric that alludes to the dissemination of a given content, its relevance and virality. 

-       Number of followers, metric that refers to the size of the audience or community of an account. 

-       Number of posts, a metric that affects the frequency and consistency of interaction with the audience. 

In this regard, the engagement index is calculated by adding the number of comments, digs, plays and shares, and dividing the result by the number of posts, thus obtaining the average engagement per post; in turn, this result is divided by the number of followers (see figure 1). In this way, a unified measure is obtained (reflecting the performance of each account in data) that allows comparing profiles with different levels of followers or followers. Thus, absolute figures are obtained through aggregate metrics, to provide an overview of the activity on TikTok by the accounts, and then the Engagement Rate of the profiles is compared in detail, analyzing the correlations between the aforementioned variables. 

Figure 1. Formula used to calculate the Engagement Rate or engagement index.

Source: Elaborated by the authors.

Finally, to determine the nature and intensity of the interactions between the various engagement metrics, a correlation matrix was implemented. This statistical analysis tool makes it possible to examine and quantify the linear associations between the selected variables. Correlation coefficients, ranging from -1 to 1, are calculated using Pearson's correlation coefficient, providing a measure of the linear relationship between each pair of variables. Visualization of these coefficients is performed through a heat matrix, where the color and intensity of each cell reflected the strength and direction of the correlation, allowing an intuitive and direct interpretation of the data. Significant patterns and underlying dynamics are thus identified, offering a more detailed perspective on the factors potentially influencing the success of these digital interactions.

4.      RESULTS 

4.1.  General data: presence, activity, interactivity, positive interaction, visualization and dissemination.

According to the analysis conducted, it is observed that most parties, regardless of their size or social and geographic influence, have an official account on TikTok. In the population under study, only Partido Nacionalista Vasco (PNV) and Partido Andalucista (PA), both regional and nationalist-oriented parties, do not have such a profile on the digital social network. 

In reference to the unipersonal candidacies, it is verified that we are going through a sort of initial stage of politicians in TikTok, since in most cases the number of followers is located in a range of 23 to 6.248 followers (75% of the sample), a relatively low number for this digital social network and for the contemporary professionalized electoral political context. In this regard, only one candidate, Ada Colau (Barcelona en Comú), exceeds 100.000 followers (121.315). Moreover, the larger the capital city in terms of population, the greater the presence of male and female candidates on TikTok. In the five capitals analyzed (Madrid, Barcelona, Valencia, Zaragoza and Valencia), of the total of 24 candidates presented by different parties, 13 had a profile on the platform. While in Barcelona 77% of the candidates had a presence on TikTok, in Zaragoza and Seville half of the candidates were absent. 

As for the activity index (number of posts), there is no ideological pattern, geographical influence or institutional presence that clearly explains the way in which the parties are ordered in Table 3, which shows the digital activity of each party. The most noteworthy fact is that the average number of publications is 17 in the period analyzed (slightly more than one post every two days) and that almost half of the sample is below this figure. It is worth noting the low participation of regional formations such as Foro Asturias, Podemos Madrid or Adelante Andalucía. This fact contrasts with the “hyperactivity” shown by other regional brands such as Recupera Madrid and PP Madrid (more than two posts per day), Compromís, BNG or PSOE.

In some minority parties, regional or local and recently created as electoral platforms —the case of the aforementioned Recupera Madrid, Aragón Existe, Teruel Existe or Soria Ya!, created in 2021, 2022, 2019 and 2022 respectively—, it is found that they post more content on the new digital social network than the average of the parties (17 posts during the period analyzed).

Table 3. Activity index and Top-10 profiles with the highest digital presence.

	Party	Number of posts			Candidate	Number of posts
1	Recupera Madrid	66		1	Ada Colau	84
2	PP Madrid	60		2	Antonio Muñoz	54
3	Compromís	35		3	Juan Lobato	49
4	PSOE Madrid	33		4	Sandra Gómez	49
5	BNG	33		5	Reyes Maroto	44
6	TeruelExiste_	31		6	Sanz Alcalde Sevilla	41
7	VOX España	31		7	Natalia Chueca	40
8	Barcelona en Comú	30		8	Mónica García	29
9	Aragón Existe	27		9	Roberto Sotomayor	28
10	PACMA	24		10	M.J. Ros Olivo	27
	Más Madrid	23			Joan Ribó	27
	Podemos	22			Manuel Dominguez	27
	Junts per Catalunya	20			Aruca Gómez	26
	Esquerra Republicana	19			Rita Maestre	24
	PSOE official account	15			Javier Ortega Smith	22
	Euskal Herria Bildu	15			Gonzalo de Oro	21
	Partido Popular	14			Fernando López Miras	21
	SORIA ¡YA!	13			Ángel Víctor Torres	19
	Unión del Pueblo Navarro	12			Jaume Collboni	19
	Valents	11			C. Sánchez-Garnica	18
	VOX Madrid	11			Eva Parera	18
	Adelante Andalucía	5			Alejandra Jacinto	18
	Podemos Madrid	3			Daniel Sirera	13
	FORO Asturias	1			Anna Grau	13
	* 17 posts on average for political parties and 22 (21.9) for candidates/candidates (in the case of individual profiles, four candidates from Aragón and Murcia show no activity: @lolaranera, @nataliachueca, @raulburillo, @pepevelez_).				Ayusopresidenta	12
					Pilar Lima	10
					J.L. Martínez Almeida	9
					Sandra Heredia Ferná	7
					Héctor Illueca	7
					J.L. García Gascón	7
					Chuaquín Bernal	6
					Noemí Santana	5
					Joan Baldoví	5
					Carmen Picazo	4
					Emiliano García-Page	3
					Miguel Ángel Aumesquet	2
					Carlos_Mazón	2

Source: Elaborated by the authors.

Regarding the variables that allude to digital engagement and interaction patterns (interactivity, positive interaction, visualization and diffusion), Table 4 provides a list of the 5 parties and 5 individual accounts with the best data in each section, in order to obtain an overview of the results.

Table 4. Digital engagement variables and interaction patterns.

PARTY INDICATORS				INDIVIDUAL ACCOUNT INDICATORS
Comments (Interactivity)				Comments (Interactivity)
1	VOX	14.136		1	Ada Colau	3.825
2	Recupera Madrid	11.526		2	Joan Ribó	2.929
3	PP Madrid	5.711		3	Mónica García	2.383
4	Podemos	2.361		4	Javier Ortega Smith	2.283
5	PSOE	2.264		5	Eva Parera	2.178
Like (Positive interaction)				Like (Positive interaction)
1	Recupera Madrid	410.971		1	Mónica García	96.774
2	VOX	344.136		2	Joan Ribó	69.893
3	PP Madrid	130.251		3	Javier Ortega Smith	59.622
4	Barcelona en Comú	33.417		4	Ada Colau	49.294
5	Podemos	31.592		5	J. L. Martínez Alm.	44.935
Plays (visualization)				Plays (visualization)
1	Recupera Madrid	9.316.897		1	Mónica García	1.299.527
2	VOX	5.892.703		2	Eva Parera	949.069
3	PP Madrid	2.718.895		3	Ada Colau	921.479
4	Barcelona en Comú	757.907		4	Javier Ortega Smith	863.976
5	Podemos	613.435		5	J. L. Martínez Alm.	727.984
Shares (Dissemination)				Shares (Dissemination)
1	Recupera Madrid	67.420		1	Joan Ribó	7.845
2	VOX	46.745		2	Javier Ortega Smith	5.673
3	PP Madrid	36.990		3	J. L. Martínez-Alm.	5.491
4	Barcelona en Comú	3.006		4	Mónica García	5.037
5	PSOE	1.887		5	Eva Parera	3.808

Source: Elaborated by the authors.

On the one hand, on the side of the political formations —which obtain higher activity records than the individual profiles—, it is worth highlighting the special relevance registered by two parties, relatively antagonistic in terms of issues such as their ideological orientation and geographical scope of action: Recupera Madrid, a recently founded, transversal and local party; and VOX, in operation since 2013, of conservative Spanish nationalist ideology and national implantation. Both parties are in the top positions in each of the indexes; in this regard, it is striking that, in the field of visualizations, Recupera Madrid almost doubles its successor (VOX) and quintuples the party in third place (PP Madrid). This data indicates that, apart from being the most active formation, the content it publishes is adapted to the TikTok register and obtains good results. As for VOX, the data point to the existence of content that invites the public to express themselves and interact with it through comments.

In addition, it should be noted that most of the digital activity in global terms revolves around issues related to the two most populated capitals of Spain: Madrid and Barcelona. The group that could be called the “Big-6” in each category (the six accounts that appear at least once in the Top-5 of any of the indicators) is composed of VOX, Recupera Madrid, PP Madrid, Podemos, PSOE, for parties; and Ada Colau, Joan Ribó, Mónica García, Javier Ortega Smith, Eva Parera and José Luis Martínez Almeida, for personal candidacies. Only Joan Ribó is limited to a regional geographic scope. 

In the block of individual candidacies, most of the profiles in the Top 5 are filled by consolidated politicians with a long career. The data show that Mónica García approached the public more successfully, offering attractive content (leader in “likes” and “views”) and transcending the limits of her staunch supporters and detractors (she descends in the table in the “comments” and “shares” sections). In this regard, on the opposite side are leaders such as Javier Ortega Smith or Joan Ribó, who with a smaller number of followers obtain better records in matters such as “likes” or “shares”, alluding to a content that digitally mobilizes their supporters better.

Moreover, it is noteworthy that a local figure such as Eva Parera (Valents) focused her campaign on digital social networks such as TikTok, probably seeking notoriety among the general public, offering eye-catching posts to a potential recipient that goes beyond voters and supporters of her own party.

Likewise, there is a scientifically very relevant fact: some of the candidates with more followers do not occupy the top positions in the ranking, such as Isabel Díaz Ayuso, Alejandra Jacinto or Rita Maestre (all in the Top-10 of followers). Finally, from a more geographical perspective, it should be noted that there are candidates such as Joan Ribó who have no rival in their sphere of influence, which highlights, in the absence of competition, a clear advantage of certain candidates when it comes to accessing the potential audience gathered in TikTok. 

4.2.  Temporal distribution of posts

Another significant indicator when describing the action patterns of political actors is related to the temporal distribution of “posting”. In this case, certain patterns can be identified in Figure 2: 

-       Personal candidacies are activated before party accounts and do so prior to the campaign (alluding to the concept of “permanent campaign” in political communication). By May 12, the day on which the electoral process begins, all the candidacies have one or more active posts on TikTok. 

-       In those individual accounts, the usual frequency is one or two posts per day, although there are exceptions such as Jaume Collboni's (eight days without generating content). No candidate creates more than four inputs in the same day. 

-       An example of proportionate temporal distribution is offered by Ada Colau, who starts her activity on the first day of the sequence and extends it until May 29, “posting” one or two contents per day. On the opposite side is Daniel Sirera, who offers a more erratic dynamic, starting late (May 6), going through long periods of inactivity (up to 6 days) and ending his digital campaign before most candidates (neglecting the last days of the campaign). 

-       A significant fact is that only 5 candidates extend their digital posting dynamics beyond election night.

The “posting” dynamics of the parties reveals the electoral strategy of each party: betting on the individual profile of the candidate or focusing the digital campaign on the corporate account. In this regard, we observe paradigmatic examples such as Recupera Madrid, which does not have personal accounts and starts its activity late (two days after the official start of the election campaign), condensing its digital action in just two weeks, “posting” most of the days between 3 and 4 contents. Another significant case is represented by Podemos Madrid, whose data suggest a sort of “digital desert”, probably based on the decision to cede all the spokespersonship to his personal candidacy.

Figure 2. Chronology of posts by the 25 most active authors.

Source: Elaborated by the authors.

On the other hand, the records of organizations such as Soria Ya! or Foro Asturias, which show an absence of a defined digital strategy, are striking. In the first case, 18 days go by without publishing content; in the second, the organization only disseminates one message in the entire month analyzed. Another of the indications derived from the data captured is that there are still parties that, despite their incidence and implementation, make a conjunctural use of the digital tool, joining the digital activity late. Such is the case of the BNG, the Madrid account of the PP, UPN or Podemos. 

Finally, there is a striking detail, and that is that in both categories (parties and individual accounts), many profiles choose to close the activity on May 26, two days before election day. 

4.3.  Engament Rate and correlation metrics

The Engament Rate or participation rate is a standardized indicator to evaluate the effectiveness and relevance of the content, as well as to understand audience participation. In this sense, to the whole series of classifications presented above according to certain parameters, a set of variables is added to measure the interaction and participation of users with the electoral political content: number of followers and number of posts published during the period analyzed.

Table 5 offers a classification of the subjects and political entities analyzed, offering a relevant fact: the top positions are occupied by candidates with significantly low metrics in any of the parameters of the equation: Miguel Ángel Aumesquet (only two posts published), Clemente Sánchez Garnica (only 23 followers), Sandra Heredia Fernández (three shares and no comments), etc. In the section on parties, the phenomenon is replicated with formations such as Foro Asturias (only one post in the entire campaign) and Aragón Existe (one comment and five shares). 

This information indicates that the TikTok algorithm is aimed at facilitating the entry into the social network of recently created subjects or profiles with little activity or few followers. This could be an intelligent strategy of the digital platform to attract new users and accompany them in their first steps (see Table 5).

Table 5. Engagement Rate of parties and individual accounts (underlined, most representative accounts in terms of interaction variables in table 4).

Engagement Rate (Party indicators)				Engagement Rate (Indicators for ind. accounts)
1	FORO Asturias	10,40		1	M.A. Aumesquet	15,25
2	Aragón Existe	9,041		2	C. Sánchez Garnica	15,11
3	SORIA ¡YA!	6,84		3	Sandra Heredia	14,88
4	UPN	5,958		4	Gonzalo de Oro	11,61
5	Teruel Existe	5,335		5	Daniel Sirera	7,666
6	PSOE	3,976		6	Fernando López Miras	5,085
7	Recupera Madrid	3,93		7	J. L. Martínez-Alm.	4,934
8	Valents	2,801		8	Pilar Lima	4,022
9	EH Bildu	1,796		9	Emiliano García Page	3,634
10	PSOE Madrid	1,507		10	Noemí Santana	3,612
11	VOX	1,204		…
12	Pacma	1,204		18	Eva Parera	2,317
13	Adelante Andalucía	1,056		…
14	Podemos Madrid	0,954		20	Javier Ortega Smith	2,099
15	Compromís	0,775		28	Joan Ribó	0,811
16	PP Madrid	0,73		29	Sandra Gómez	0,769
17	VOX Madrid	0,595		30	Manuel Domínguez	0,488
18	Barcelona en Comú	0,538		31	Mónica García	0,399
19	Más Madrid	0,503		32	Alejandra Jacinto	0,388
20	Junts per Catalunya	0,351		33	Rita Maestre	0,373
21	Esquerra Republicana	0,337		34	Ada Colau	0,363
22	Partido Popular	0,17		35	Carmen Picazo	0,362
23	Bloque Nac. Galego	0,146		36	Jaume Collboni	0,189
24	Podemos	0,066		37	Isabel Díaz Ayuso	0,113

Source: Elaborated by the authors.

In this context, the Engagement Rate could be used to infer another ranking of electoral political actors with greater impact, if said index were referenced to the indicators or interaction variables mentioned above (comments, “likes”, plays, shares). In this regard, Table 5 highlights the six most significant actors during the electoral campaign analyzed (accounts inferred from Table 4), and it is worth noting that none of them is among the first five positions in the ranking.

This visualization of the results also serves to locate the aforementioned players that obtain the best performance from their digital effort in terms of impact. In this sense, the official PSOE account and that of the Madrid candidate, José Luis Martínez Almeida, would be those that achieve the greatest impact in terms of interaction and engagement with the least effort. 

One of the most significant contributions of this study is to analyze the degree of correlation of the various variables selected, and to look especially at their relationship with the aforementioned Engagement Rate (Figure 3). In the case of individual accounts (something similar happens in the relationship matrix for party accounts), for example, it is observed that all the variables introduced in this study correlate negatively with the Engagement Rate[3]. Of the seven categories (posts, “likes”, comments, plays, shares, followers and Engagement Rate) there is a quartet of values that mark an area in the graph of high significance and an intense correlation between them (values close to 1 or -1): diggs or “likes”, plays or views, shares, and to a lesser extent, followers. 

On the other hand, the number of posts is a variable that loses “power” compared to the rest, and offers a lower degree of intensity in terms of its ability to correlate with the other parameters. In other words, the number of posts published may not be decisive in improving the degree of visibility or the “likes” obtained by a given content. 

Figure 3. Correlation of engagement metrics in TikTok.

Source: Elaborated by the authors.

It can thus be inferred that the number of posts published or the number of followers of an account are not the most decisive variables and that, however, the powerful core is centered on the trident made up of the “likes”, plays and shares variables. On the other hand, what is relevant about the Engagement Rate is that it maintains a negative link with the rest of the variables, especially with the number of posts and the number of followers an account has: the fewer the posts or followers, the higher the Engagement Rate. This data could indicate that the social network does not favor a possible network of contacts of a specific profile or its activity, but metrics more directly related to the power of direct attraction of a particular content. 

5.      DISCUSSION AND CONCLUSIONS 

Given the scarcity of studies focused on the use and influence of new political communication tools such as TikTok in municipal and regional electoral contexts, this research focuses on clarifying why this trendy network promotes an evolution of electoral political communication based on new variables of analysis. In doing so, it goes beyond the progress promoted by previous platforms that have proven to be decisive in the digital public sphere (Casero-Ripollés, 2020).

TikTok, is the digital social network of the moment, a channel that significantly influences contemporary cultural production and consumption, which is directly related to the implications of its empirical study in communication and politics (Sánchez, 2021; Cervi et al., 2023; Gómez-García et al., 2023), as well as on the methodological implications implied by its empirical approach (Barger, & Labrecque, 2013; Laniado, & Viles, 2018; Ballesteros, 2019).

With regard to the patterns of action of political leaders and parties (RQ1), the results of the study point to a panorama of great contrasts: while certain leaders and parties show professionalized patterns of TikTok use within the framework of a defined digital political communication strategy, others show certainly limited uses. These professionalized patterns would be determined by specific actions —such as regular and frequent publication based on adapted tempos, or the use of specific terminology— and do not respond to traditional patterns such as gender, party ideology, territorial scope or political representativeness (RQ2). It can be concluded, therefore, that political communication, at least in the context examined, is in a sort of initial phase of adoption and adaptation to this digital platform. These results regarding the still limited use of TikTok coincide with previous studies, such as the one elaborated by Karimi, and Fox (2023) on the 2020 US elections or by Zuykina, and Krinitsyna (2023) in relation to the Russian State Duma elections in 2021.

This research identifies that the accounts related to the big capitals monopolize the attention of a large part of the Internet community (Gamir-Ríos, & Sánchez-Castillo, 2022). However, it is observed that the good results obtained by parties or candidates who concentrate a great effort of their digital campaign on TikTok (cases of Recupera Madrid or Eva Parera, from Valents) in terms of digital engagement, interaction patterns and Engagement Rate, are not directly related to the results during the elections analyzed (Recupera Madrid did not obtain representation in its constituency, for example) (RQ2). This data could open new lines of research to try to draw correlations between political parties, their digital communication and their electoral results.

This fact points to the fact that the activity and interactivity in social networks such as TikTok does not prescribe or mobilize potential voters, but is oriented to offer entertainment in a playful and massive way, without exerting a direct persuasive influence in the political arena. In this context, the platform becomes more a space for distraction and leisure than an effective means for political mobilization or changing electoral opinions. This idea is supported by the inherent characteristics of this social network and by the type of algorithms it uses. In other words, as noted in the introduction, TikTok bases its operation on a type of algorithmic formulation different from that of platforms such as Facebook, X (formerly Twitter) or Instagram, achieving a great influence on the operation of the new paradigm or new stage of contemporary digital social networks.

In this regard, the metrics of each account analyzed allow us to conclude that neither the hyperactivity on TikTok nor the large number of followers ensure good engagement data, which could be translated in terms of digital success. The data suggests that TikTok's algorithm is designed to promote the inclusion of new accounts or profiles with limited activity or few followers on the platform. Such an approach could represent a shrewd instant gratification tactic by the social network to engage and guide new users in their first steps. On the other hand, it is corroborated that the “hooking” capacity or the power of attraction of a content is a determining factor.

Specifically, the variables that most influence digital “success” are those related to the mere power of attraction of a given content (often decontextualized from other more “politicized” variables in traditional terms): “likes”, plays or views and shares or times shared. Indicators such as followers, comments or number of posts are relegated to the background. This is, in itself, a significant fact that characterizes the new phase of digital social networks, which are progressively abandoning their more “relational” era (algorithms that offered content based on certain contact networks, for example) and moving towards entertainment and mass consumption of attractive content for all types of users. 

This study recognizes its limitations, particularly in terms of its temporal scope and the incipient nature of the phenomenon. Future studies could confirm the findings of this research by analyzing other cases, expanding the set of actors analyzed and obtaining more voluminous data-sets. Given the speed at which both the platform and its usage practices are evolving, as well as the implementation of algorithm changes, our findings reflect a reality that can rapidly transform, highlighting the need for ongoing and updated research, also from a political communication perspective.

TikTok's relatively open data policy suggests a promising future for the proliferation of scientific studies in this field. In this regard, “Social Network Analysis” emerges as a particularly fertile field for future exploration, offering a range of research options that can range from sentiment analysis of particular phenomena, to the creation of image networks and the detailed and qualitative study of published content. These methodologies will not only allow us to discover patterns of interaction between users and content, but will also open up avenues for understanding how communities are built and maintained within TikTok. 

Likewise, one of the issues to be unraveled in this already noted trend towards the “lack of relationship” of digital platforms is related to the progressive lack of policies of social networks, an issue that deserves critical attention in future research. The possibility that TikTok, like other digital platforms, is contributing to a less politicized public sphere, with more superficial interactions and less focused on substantive debate, raises important questions about the role of these technologies in democracy and citizen participation.

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AUTHORS’ CONTRIBUTIONS, FUNDING AND ACKNOWLEDGMENTS

Authors’ contributions:

Conceptualization: Orbegozo Terradillos, Julen. Software: Morales i Gras, Jordi. Validation: Larrondo Ureta, Ainara. Formal analysis: Orbegozo Terradillos, Julen and Larrondo Ureta, Ainara. Data curation: Morales i Gras, Jordi. Drafting-Preparation of the original draft: Orbegozo Terradillos, Julen and Larrondo Ureta, Ainara. Drafting-Revision and Editing: Orbegozo Terradillos, Julen and Larrondo Ureta, Ainara. Visualization: Morales i Gras, Jordi. Supervision: Larrondo Ureta, Ainara. Project management: Larrondo Ureta, Ainara. All authors have read and accepted the published version of the manuscript: Orbegozo Terradillos, Julen; Larrondo Ureta, Ainara and Morales i Gras, Jordi.

Funding: This research received funding from the Gureiker research group (IT1496-22), category A (2022/2025).  

AUTHORS:

Julen Orbegozo Terradillos

University of the Basque Country.

Degree in Journalism and Advertising and Public Relations. Professor of Public Communication Management and Interpersonal Communication (UPV/EHU). Main area of specialization: Political Communication. Lines of research: activism in social networks, communication and gender, and electoral campaigns. He has published in high impact journals works related to electoral debates, hashtivism, fake news and new electoral narratives with a gender perspective. He has more than ten years of professional experience. He has worked as a journalist in various media and as a communications advisor in the Basque Parliament, participating in numerous electoral campaigns in the Basque and Spanish sphere. 

julen.orbegozo@ehu.eus

Índice H: 6

Orcid ID: http://orcid.org/0000-0002-2959-4397

Google Scholar: https://scholar.google.com/citations?user=8ZFDUb0AAAAJ&hl=es

ResearchGate: https://www.researchgate.net/search.Search.html?type=researcher&query=julen%20orbegozo

Scopus ID: https://www.scopus.com/authid/detail.uri?authorId=57199685036 

Ainara Larrondo Ureta 

University of the Basque Country.

Professor in the Department of Journalism at the University of the Basque Country (UPV/EHU). Her main lines of research are digital, political and gender communication. She is the main researcher of the consolidated research group Gureiker (IT1496-22), funded by the Basque Government.

ainara.larrondo@ehu.eus 

Índice H: 23

Orcid ID: https://orcid.org/0000-0003-3303-4330

Scopus ID: https://www.scopus.com/authid/detail.uri?authorId=56507340200 

Google Scholar: https://scholar.google.es/citations?user=tEpzyvwAAAAJ&hl=es 

ResearchGate: https://www.researchgate.net/profile/Ainara-Ureta

 

Jordi Morales I Gras

Chamber of Bilbao University Bussines School.

D. in Sociology from the University of the Basque Country (UPV/EHU). His area of specialization is Computational Social Science, with a strong emphasis on Social Network Analysis and Artificial Intelligence. She collaborates as a lecturer in the Master of Models and Areas of Social Research of the UPV/EHU, in the Master of Social Media of the UOC and in the Postgraduate in Data Analytics of the Col-legi de Professionals de la Ciència Política i la Sociologia de Catalunya. He is also founder and CEO of Network Outsight, a consulting firm specializing in Big Data sociological analysis.

jordi.morales@camarabilbaoubs.com 

Índice H: 9

Orcid ID: https://orcid.org/0000-0003-4173-3609 

Scopus ID: https://www.scopus.com/authid/detail.uri?authorId=57212511634  

Google Scholar: https://scholar.google.com/citations?hl=es&user=3KwU0SEAAAAJ  

ResearchGate: https://www.researchgate.net/scientific-contributions/Jordi-Morales-i-Gras-2189273512

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[2] The complete data set can be found at the following link: https://zenodo.org/records/10650622 

[3] The asterisks next to the number indicate that the correlation found is statistically significant (the more asterisks, the higher the degree of statistical significance).