Revista Latina de Comunicación Social. ISSN 1138-5820

Mimetic theory applied to interpersonal relationships through social networks: the case of piñagate in Mercadona

María del Carmen Paradinas Márquez

ESIC University/ESIC Business & Marketing School. Spain.

Cristina Marín-Palacios

ESIC University. Spain.

How to cite this article / Standard reference:

Paradinas Márquez, María del Carmen & Marín-Palacios, Cristina (2025). Mimetic theory applied to interpersonal relationships through social networks: the case of piñagate in Mercadona. Revista Latina de Comunicación Social, 84, 1-16. https://www.doi.org/10.4185/RLCS-2026-2469

Date of Receipt: 01/22/2025
Date of Acceptance: 05/24/2025
Date of Publication: 06/24/2025

ABSTRACT

Introduction: Piñagate illustrates how social networks amplify mimetic behaviours. It analyses how desires and behaviours are digitally replicated, highlighting the role of tweets and viral content in their expansion. This case provides new perspectives on mimetic theory applied to digital phenomena and its impact on contemporary communication. Methodology: The analysis was carried out by collecting tweets with “piña” (pineapple) AND “Mercadona” AND “ligar” (flirt). After data cleaning in Excel and Google Colab, a sentiment analysis was carried out with Pipeline (Transformers) to subsequently extract keywords with NLTK and elaborate thematic maps with VOSviewer. Results: The results indicate that the content of the tweets analysed has a positive tone or a favourable response from the community analysed. Four groups are identified: General Context of Mercadona, Strategies for Flirting, Environments and Products, and Moments and Occasions. Discussion: The application of mimetic theory to social networks has generated both interest and criticism. Some argue that it provides a valuable explanation for understanding online behaviour and group dynamics. However, others criticise that it may oversimplify the complexity of human interactions and individual motivations. Conclusions: The piñagate at Mercadona illustrates how mimetic theory explains viral phenomena in social networks, highlighting how trivial symbols, such as a pineapple, are transformed into acts of interpersonal communication. This study combines data analysis and natural language, showing how digital culture redefines social norms and commercial perceptions.

Keywords: mimetic theory; digital communication; viral phenomena; mimicry; social networks; VOSviewer; human interactions.

1. INTRODUCTION

The mimetic theory was formulated by the French philosopher and literary critic René Girard in the 1960s. This theory holds that desire is neither innate nor autonomous, but arises from the observation and imitation of the desires of others (Hernández, 2023). According to Girard, people desire objects not because of their intrinsic value, but because others desire them. This process of imitation, or mimesis, is fundamental to understanding social and cultural dynamics, but it can generate rivalry, given that multiple individuals come to desire the same object or symbol based on the social value it acquires through imitation (Mendelson, 2023).

However, this study does not focus on the possibility of conflict generation by mimetic desire, but on the formation of human desires through the imitation of the desires of others (Livingston, 1994; Lebreton et al., 2012) through communication in social networks from a festive playful behavior free of violence and conflict.

This referred behavior can frequently lead to the use of irony as a linguistic resource when expressing ideas or feelings in social networks (Kovaz et al., 2013). The use of this form of expression is characterized by expressing the opposite of what is said, generating an ambiguity that can be difficult to interpret which can add a layer of interpretive complexity that is not the main focus of mimetic theory.

Irony and sarcasm are a constitutive part of our daily communication becoming even more present in social networks and precisely, ambiguity, which is the root of irony, imposes on the discourses produced in social networks an interpretative subtlety that presents itself as one of the main challenges for discourse analysis due to the lack of non-verbal cues such as voice or gestures.

In addition, irony can be interpreted differently depending on the context and the relationship between the interlocutors (Rothermich & Pell, 2015).

With the study of mimetic theory, the aim is to understand how and why certain behaviors are imitated and spread, rather than in the subtleties of the language used to express those behaviors, as it could be through irony. Including it, could have diverted attention from the main objective of the study, which is to understand how a specific behavior (putting a pineapple upside down in the shopping cart) spread through social networks.

This phenomenon, known as “mimetic desire,” is fundamental to understanding social and cultural dynamics, especially in the context of social networks as this theory has evolved significantly from its beginnings to its connection with social networks today. “Let us think that, if trends and fashions develop dynamically, it is because they do so in a mimetic way” (Sánchez Villalón, 2019, p. 71).

Over the decades, mimetic theory has been adopted and adapted by psychologists and sociologists to explain human behavior in modern contexts. For example, in social psychology, it has been used to understand phenomena such as conformity and peer pressure. In sociology, it has been applied to analyze power and conflict dynamics in various social structures.

With the advent of social networks in the early 21^st century, mimetic theory has found a new field of application. Social networks amplify mimetic desire by exposing users to the desires and behaviors of a vast network of people (Fuhse, 2020).

Platforms such as Instagram and Facebook encourage social comparison by constantly showcasing the lives and accomplishments of others. Users can develop desires based on what they see in their feeds, mimicking the desires and behaviors of those they follow.

Furthermore, thanks to Artificial Intelligence and natural language processing (NLP) it is possible to perform mimetic analysis of information downloaded from social network content. In particular, in this work, starting from a download of tweets from the social network X, a mimetic analysis of the content of these tweets has been performed.

This theoretical framework is particularly useful for understanding how certain behaviors and symbols become recurrent in a community.

1.1. Social networks and the viralization of social symbols

Social networks have revolutionized the way in which individuals interact and express their identity, allowing certain symbols to spread massively and generate a symbolic “contagion” (Monge & Contractor, 2003). Signs such as the pineapple in the cart acquire a special meaning when they are interpreted as indicative of social availability. Several studies have shown that social networks function as platforms of symbolic signaling, where individuals adopt signs that they replicate when they perceive belonging to a wider community (Da Silva & Baldissera, 2021).

Therefore, viral phenomena in social networks can be seen as examples of mimetic desire on a large scale. A content becomes viral when many people mimic the behavior of sharing and reacting to the same content, creating a collective desire.

These networks amplify the phenomenon of mimetic desire by providing platforms where users can observe and replicate the behaviors and desires of others. The virality of certain content and behaviors on social networks can be explained through this theory. Influencers, for example, act as role models, generating desires and behaviors that their followers imitate (Etienne & Charton, 2024).

The virality of phenomena such as the “pineapple in the Mercadona cart” case, known as piñagate, for flirting, reflects the mimetic dynamics of social networks (Machuco Rosa, 2018), where certain ideas or behaviors are quickly amplified and reproduced due to their ability to capture the attention of users and satisfy social needs. The mimetic theory, formulated by René Girard, argues that people tend to imitate the desires and behaviors of others, a process that in social networks is intensified by the constant exposure to content that immediately engage the collective mindset (Girard, 1965). In this case, the use of the pineapple as a symbol of romantic availability offers an accessible and playful narrative that users can imitate, largely motivated by the desire for belonging and validation in a humorous and easily applied context (Berger, 2016).

Moreover, the ability of networks to popularize specific symbols and signals reflects how memeplexes or viral culture units function as vehicles of shared meaning in modern contexts (Dawkins, 2016). The pineapple in the cart functions here as a meme that, when replicated and reinterpreted, acquires symbolic meaning, causing a common object to convey a shared social message. This form of virality is fueled by the ease of imitation and the effectiveness of the digital medium, which allows the message to travel quickly and reach a critical mass of participants who adopt it as a common code. Thus, phenomena such as Piñagate illustrate how mimetic desire and digital communication environments converge to generate “social fads” that, although ephemeral, exemplify the collaborative nature of modern digital culture (Fuhse, 2015).

The use of symbols in public spaces, such as the case of the pineapple in Mercadona, can be interpreted as an act of social identity construction. This transforms a commercial space into a scenario of social interaction, where individuals not only make purchases, but also communicate social intentions and expectations. The symbolization of the object turns it into a marker that expresses the intention to interact socially (Koltsova & Kartashkova, 2022).

This “code” emerged as a joke in social networks, where it was said that placing a pineapple upside down in the shopping cart in supermarkets (such as Mercadona) could indicate that someone was available for flirting or interested in meeting other people. The prevailing emotion was a mixture of curiosity and joy, as the novelty and humor of the method attracted the attention of many.

The idea became popular, especially on X and other platforms, as a symbol among those looking for a fun and discreet way to interact or flirt while shopping. Although it is largely a joke or meme on networks, some people tried it for fun to see if it attracted any reaction.

Adopting the pineapple in the cart as a symbol of social openness can be understood as the creation of a social micro-ritual. These micro-rituals give participants a sense of belonging and integration, and Mimetic Theory explains how individuals are driven to replicate this behavior due to their desire for inclusion (Roemer, 2007).

Symbolic signs in commercial environments foster a collective identity, in which individuals adopt a symbol to indicate their belonging and willingness to interact. This behavior is a reflection of mimetic desire, which encourages participation in social rituals observed in networks, such as the case of the pineapple in Mercadona. This generates a sense of belonging among individuals who participate in these symbolic acts and facilitates casual social interactions (Kalkhoff et al., 2020; Wongkitrungrueng & Assarut, 2020).

1.2. The role of digital media in the migration of symbols to physical space

The interaction between digital space and physical space allows individuals to move behaviors and symbols from one environment to the other. This migration reveals how symbolic communication in both spaces reinforces group identity in everyday environments (Bandura, 2009). Several studies point out that symbols and messages in the digital environment strengthen the sense of belonging in the physical environment, facilitating the adoption of symbols such as the pineapple in the cart (Littwin & Stock, 2020).

The use of symbols to establish connections in commercial spaces allows us to analyze how physical contact is replaced or complemented by symbols with clear intentions in urban environments. This contributes to a deeper understanding of how digital media influence the perception of symbols and how these create a hybrid space that extends the concept of social signage (Jacob & Banisch, 2023).

Symbolic signs of availability in environments such as Mercadona illustrates how social networks have fostered an identity of “public intimacy”, in which symbols allow individuals to show themselves accessible in a physical environment, sharing a common social space, but maintaining their individuality (Corcoran, 2012).

Social networks play a crucial role in the diffusion and amplification of phenomena such as piñagate. They act as catalysts of mimetics by allowing users to share and observe behaviors massively and quickly. This process not only has social value, but can also lead to changes in social norms and expectations (Craig, 2013).

Mimetic desire is a powerful driver behind viral trends in social networks. When a behavior or desire is repeatedly observed on a social platform, it becomes a model for other users to follow. This process of imitation can facilitate the rapid spread of trends, as people tend to align themselves with what they consider attractive or popular.

In addition, this phenomenon generated a variety of emotions among the participants and observers analyzed in this work. The predominant emotion was a mixture of curiosity and joy, as the novelty and humor of the method attracted the attention of a great number of people.

2. OBJECTIVES

First, this study tries to analyze the phenomenon of symbolic signaling in the framework of Mimetic Theory within interpersonal interactions in commercial spaces, with the case of the use of an upside down pineapple in the shopping cart in Mercadona, in principle, with the intention of flirting.

This objective focuses on studying how symbolic elements, in this case the pineapple in the shopping cart, act as signals within a commercial space and serve to convey implicit meanings among consumers. Based on René Girard's Mimetic Theory, this paper investigates how imitation and socially modeled desire influence the adoption of symbols and their use as a nonverbal communication mechanism in everyday interaction in the shopping environment.

Likewise, the aim is to explore the origin and diffusion of the “pineapple in the cart” phenomenon in Mercadona through social networks.

This objective aims to investigate the processes of emergence and propagation of this symbolic sign in the context of digital platforms. It examines how users interpret, reproduce and adapt the phenomenon in social networks, specifically in X, and how these digital interactions drive the visibility and shared understanding of this symbol in the commercial environment, analyzing the dynamics of virality in the diffusion of the phenomenon.

The aim is to analyze how the “pineapple in the cart” acts as an identity sign that influences the perception and construction of the social identity of individuals in a shared space. In addition, the study analyzes how this symbol affects interpersonal interactions in a physical environment, evaluating whether it generates a sense of belonging, mutual recognition or group cohesion among consumers, and how the social dynamics are structured around this sign.

This allows us to evaluate the adequacy of Mimetic Theory as a theoretical framework for interpreting these social behaviors by observing the effectiveness of Mimetic Theory in explaining behaviors associated with the use of signage symbols in consumer contexts. To this end, the fundamental principles of the theory, such as imitation and mimetic desire, were previously analyzed to determine whether they provide an adequate and complete framework for understanding the processes of adoption, dissemination and meaning of symbolic elements in contemporary commercial spaces such as Mercadona.

Another objective is to understand, using VOSviewer and PLN, social interactions and mimetic behaviors through a detailed analysis of how people on X associate certain terms and symbols with their experience at Mercadona, especially with regard to informal behaviors and interaction strategies such as “flirting”. In addition, we seek to identify key patterns and relationships within the data to understand how social networks amplify and replicate such behaviors.

3. METHODOLOGY

Applying a mimetic analysis to a set of tweets is feasible and can provide deep insights into how certain ideas, emotions or behaviors are reflected in social networks (Noor et al., 2020; Hoffman et al., 2023). In this research a mimetic analysis was applied to a set of tweets obtained from the search “pineapple” AND “Mercadona” AND “flirt”. A mimetic analysis can identify elements such as (Rodrigo et al., 2015):

Language and tone: Examine how users express their emotions, concerns or attitudes in tweets and how this mimics or represents certain social attitudes.
Themes and trends: Observe which themes are recurrent and whether they reflect current issues, societal concerns or specific cultural values.
Identity and roles: Analyze how users represent their online identity (whether personal, professional or even fictional) and how this reflects aspects of real-world identity.
Social dynamics and behavior: Looks at interactions such as replies, retweets, and mentions, to see how they reflect social behaviors or dynamics of power, support, confrontation, etc.

First, a search was conducted on social network X for tweets containing the keywords “pineapple” AND “Mercadona” AND “flirt” using the Explore tool (https://x.com/explore). A total of 509 tweets published between August 25 and October 2, 2024 were collected, which allowed capturing a representative sample of conversations in the social network X to observe patterns and trends (Iasulaitis et al., 2025).

The obtained tweets were cleaned using Excel and Google Colab. This cleaning included the removal of punctuation marks, special characters, mentions, URLs and stopwords (empty words), in order to reduce noise in the data.

Sentiment analysis was performed using the Pipeline module of the Transformers library (Zeng et al., 2022), which provides access to pre-trained natural language processing (NLP) models in a simplified interface. A pre-trained model, designed to classify tweets into standard sentiment categories (positive, negative or neutral), was loaded based on state-of-the-art models such as BERT or DistilBERT (Areshey & Mathkour, 2024), trained on large datasets such as IMDb or SST-2 (Fang et al., 2022). For each comment, the sentiment analysis model was applied, which processes the text and returns a prediction with two elements (Ding et al., 2022; Mahrukh et al., 2023): the sentiment label and the probability of the prediction, which represents the degree of confidence of the model (Ma et al., 2024). The results of this analysis were stored in an Excel file called “sentiment tweets”, with two columns, label and score, representing each tweet and its sentiment.

Keyword analysis and empty word removal (Lei et al., 2021) of each of the tweets was performed in Python with NLTK (Natural Language Toolkit), a library widely used in natural language processing (NLP) tasks (Moharil & Sharma, 2023). This process included text cleaning, tokenization (segmentation of the text into individual words), and lemmatization (reduction of each word to its root form). In addition, term frequency was calculated using the Term Frequency (TF) algorithm, which identified the most representative terms in each tweet (Dey & Das, 2023).

Finally, using VOSviewer, a map of keywords grouped by typologies was created (Abubakar et al., 2024). VOSviewer is a network analysis tool that generates a cooccurrence graph of terms or concepts. Each node in the graph represents a word or concept, and the links between nodes indicate cooccurrence or affinity relationships, highlighting the most repeated nodes and relationships between words (Marín-Palacios, 2023).

Table 1 shows the summary of all the tools used to perform the mimetic analysis of the analyzed tweets and their justification.

The research work was carried out in the following steps:

Collecting tweets in X with Explore. (https://x.com/explore)
Cleaning tweets with Excel and Google Colab.
Performing sentiment analysis with the Transformers Pipeline module in Google Colab.
Keyword cleaning and analysis using NLTK (Natural Language Toolkit) in Google Colab.
Keyword map creation with VOSviewer.

Table 1. Summary of the tools that were used and their justification.

Tool	Justification
X (Explore)	It allows access to a large database of tweets, making it easier to obtain a significant sample.
Excel and Google Colab	Accessible and efficient tools to perform data cleaning, removing punctuation marks, special characters, mentions, URLs and stopwords, ensuring the quality of the analyzed data.
Transformers Pipeline Module	It provides access to pre-trained natural language processing (NLP) models, such as BERT and DistilBERT, which are highly accurate and efficient for classifying tweets into sentiment categories (positive, negative or neutral).
NLTK (Natural Language Toolkit)	It is a library widely used in natural language processing (NLP) tasks, offering various linguistic tools and resources for text cleaning, tokenization, lemmatization and term frequency (TF) calculation.
VOSviewer	It is a network analysis tool that generates co-occurrence graphs of terms or concepts, allowing to visualize the relationships between keywords and group them by typologies, facilitating the interpretation of the data.

Source: Elaborated by the authors.

4. RESULTS

As shown in Figure 1 of the confidence analysis of the tweets, an overall high confidence is shown. The graph shows the confidence distribution of the analyzed tweets, where the horizontal axis shows the confidence values and the vertical axis the frequency of occurrence for each confidence interval. Being 98% of the tweets positive and 2% neutral.

This could indicate that, in general, the content of the analyzed tweets has a mostly positive tone, which could reflect a good perception of the specific topic or a favorable response from the analyzed community.

Figure 1. Confidence analysis of the tweets.

Source: Elaborated by the authors.

As shown in Figure 2 of the keyword analysis being performed with VOSviewer, the largest nodes are “Mercadona”, “ligar” (to flirt) and “piña” (pineapple) which are the most frequent or key terms in the context of this analysis. The connection between these nodes indicates that they often appear together in the same context. This type of visualization is useful for identifying patterns of cooccurrence of terms and how certain concepts are related in a corpus of textual data. In this case, it seems to analyze the phenomenon of how the Mercadona store and its environment can be a context for social interaction situations.

Table 2, also obtained with VOSviewer, shows for each word, the group to which it belongs, the number of direct connections of the term with another term (number of links), the sum of the strength of all connections (including indirect ones), which gives an idea of the centrality of the term (number of links), the number of times the term appears in the data corpus (total strength of links) and the number of times the term appears in the data corpus (number of occurrences). The words with the highest link strength are flirt, Mercadona, with one, pineapple, the cart.

Table 2. Summary of terms and relationships in semantic network analysis.

Word	Group	Number of links	Total link strength	Number of occurrences
con una	1	11	25	15
el carro	1	10	21	7
el pasillo	1	8	13	5
ligar al Mercadona	1	9	12	8
los vinos	1	9	15	5
al Mercadona	2	8	16	17
con	2	6	17	11
Mercadona	2	13	67	51
piña	2	13	57	42
piña para ligar	2	3	4	6
abajo	3	6	8	5
el carrito	3	8	14	6
el Mercadona	3	10	28	15
para ligar	3	11	29	24
al revés	4	8	11	5
idea	4	7	11	5
ligar	4	17	81	54
Mercadona con una	4	4	8	8
revés	4	5	9	4

Source: Elaborated by the authors with VOSviewer.

In this case, the following four groups are identified.

Group 1, green in Figure 2. General Context of Mercadona. In this group terms such as “con una” (with a), “el carro” (the cart), “el pasillo” (the aisle) and “ligar al Mercadona” (to flirt in Mercadona) and “los vinos” (the wines) are related to each other and suggest actions or interactions that can be replicated in a mimetic way, by imitation in a social group. For example, phrases such as “piña para ligar" (pineapple to flirt) could indicate the use of certain objects (such as a pineapple) as symbols or strategies that are mimetically replicated to fulfill a purpose (such as initiating a social interaction).

Group 2, yellow in Figure 2. Strategies for Flirting in Mercadona. “al Mercadona” (to Mercadona), “con” (with), “Mercadona”, “ piña” (pineapple), and “piña para ligar” (pineapple for flirting), it is possible to analyze these words from the perspective of how objects, places, and symbols take on cultural meanings that are propagated by imitation, driven by social norms, moods, or trends. It is interesting to note that “Mercadona” and “pineapple” have high link strength values (67 and 57 respectively), indicating that they are core terms.

Group 3, red in Figure 2. Environments and Products for Flirting. “down”, “the cart”, “the Mercadona”, “to flirt”. This approach emphasizes how spatial elements, functional objects and explicit goals such as “to flirt” generate observable patterns that can be copied and adapted, creating a mimetic behavior that redefines the original function in a social and cultural context.

Group 4, blue in Figure 2. Moments and Occasions for Flirting. “al revés” (upside down), “idea”, “ligar” (flirt), “Mercadona con una” (Mercadona with a), “revés” (back). These words connect to mimetic behavior by highlighting creativity, surprise, and reinterpretation of the everyday as mechanisms that inspire imitation. The concepts of inversion (“upside down”, “back”), innovation (“idea”) and social purpose (“flirt”, “Mercadona with a”) contribute to create patterns of behavior that are reproduced for their novelty, functionality or symbolic appeal. Some terms such as “flirt” are highly relevant (81 link strength and 54 occurrences), suggesting that it is a key concept.

Figure 2. Map of keywords and their interrelationships.

Source: Elaborated by the authors.

Table 3 summarizes the mimetic analysis of the words highlighted in the analysis with VOSviewer.

Table 3. Summary of terms and their mimetic analysis.

Word	Mimetic analysis
Al revés	Involves breaking the rules or doing something unexpected. It attracts attention because of its disruptive creativity, which can generate imitation.
Idea	Represents the spark of novel behavior. If it is effective or interesting, others replicate or adapt it.
Ligar	A social objective that drives the adoption of strategies observed as successful in public contexts such as Mercadona.
Mercadona con una	An act that combines space and object or company, becoming a social symbol. Wearing something distinctive generates attention and can be replicated.
Revés	Similar to upside down, it implies a disruptive action or approach that surprises, attracting imitators for its impact or humor.
Abajo	Related to strategic locations in a space, which can be perceived as useful and imitated in a social context.
El carrito	Functional object reinterpreted as a social tool (e.g., to attract attention or initiate interactions).
El Mercadona	Space that encourages observable and replicable behaviors due to its everyday and familiar character.
Para ligar	Phrase that synthesizes an explicit social purpose. Strategies observed as successful for this purpose are replicated.
Con	Suggests accompaniment or interaction. The creative use of a company can generate imitable behaviors.
Piña	Symbolic object used to highlight or trigger interactions. Its unusual use generates patterns of imitation out of humor or curiosity.
Piña para ligar	Strategic use of an object with an explicit intention, which encourages imitation if perceived as effective or fun.

Source: Elaborated by the authors.

5. DISCUSSION AND CONCLUSIONS

The application of mimetic theory to social networks has generated both interest and criticism. Some argue that it provides a valuable explanation for understanding online behavior and group dynamics (Zulli & Zulli, 2022; Etienne & Charton, 2024). However, others criticize that it may oversimplify the complexity of human interactions and individual motivations (Livingston, 1994; Richardson & Manglos, 2013; Peters & Allan, 2022).

The piñagate in Mercadona is a fascinating example of how mimetic theory can be applied to understand viral phenomena in social networks. The emotions generated and the dynamics of approach observed in this case reflect the complexity of human interactions in the digital age. Moreover, the relationship between mimetic desire and viral trends demonstrates how social networks can amplify and accelerate the spread of behaviors and desires, thus shaping social norms and expectations.

Mimetic Theory is useful to understand how a seemingly trivial symbol, such as a pineapple in a shopping cart, can be transformed into an act of interpersonal communication driven by social networks and commercial spaces. This theoretical framework facilitates the analysis of how symbols are adopted, replicated and transformed into micro-rituals that generate a sense of belonging in everyday spaces such as Mercadona, evidencing the convergence between digital identity and everyday life.

The migration of symbols from social networks to physical space reflects the interaction between digital and social identity. This allows affirming that digital environments can shape norms and behaviors in real contexts.

However, although mimetic theory provides a solid framework, it may simplify individual motivations and the complexity of human interactions so it is important to consider additional contextual factors in future analyses where the study can also be extended to the analysis of other social networks.

This study highlights how data analysis and natural language processing have allowed us to study and understand the social interaction patterns related to flirting in Mercadona in X. PLN tools have been critical in identifying the relationships between words and their centrality to the topics discussed, which has provided a deep understanding of how mimetic behaviors and social creativity transform everyday objects, places, and symbols into elements of digital interaction. This approach has allowed us to understand how social norms emerge and propagate on digital platforms, as well as the impact of these phenomena on the perception of the Mercadona brand. By combining quantitative analysis with semantic language analysis, this study offers a new perspective on how digital culture and social networks influence human behavior.

These findings demonstrate how mimetic theory can be effectively integrated into the analysis of contemporary social phenomena despite having its origins in the last century.

In the mimetic analysis of tweets that was performed, irony was not taken into account, a fact that can be justified due to the complexity and challenges inherent to its automatic detection and because it is not always easily recognizable and can be confused with literal comments, which could have affected the accuracy of the analysis (Corchado Robles, 2016).

Irony is a form of expression that often relies on subtle contexts and extratextual knowledge, making it difficult to accurately identify using standard PLN tools. According to Van Hee et al. (2018), irony detection in tweets is a challenging task that requires advanced models and specific data to achieve accurate classification (Reyes et al., 2012).

In order to include irony detection in future tweet mimetic analysis studies, the use of advanced natural language processing (NLP) models based on the Transformers architecture is suggested. These models, when specifically trained with large datasets containing irony examples, have been shown to be effective in identifying ironic patterns within text (Xu et al., 2022; Ahuja & Sharma, 2022; Hazrati et al., 2025).

In addition, several studies have highlighted the importance of considering contextual analysis in irony detection, as this approach allows for a deeper understanding of the underlying meaning of tweets (Eke et al., 2021). Also, the application of data enrichment techniques, such as the incorporation of metadata (e.g., hashtags, mentions, and emojis), can provide additional information that facilitates the identification of irony in texts (Saroj & Pal, 2024).

On the other hand, some authors suggest complementing automated approaches with human evaluations, since expert annotators can identify examples of irony that automatic models might not recognize. This process contributes to the creation of a more robust dataset, improving both training and validation of irony detection models (Wen et al., 2022).

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AUTHOR CONTRIBUTIONS, FUNDING AND ACKNOWLEDGEMENTS

Conceptualization: Paradinas Márquez, María del Carmen. Software: Marín Palacios, Cristina. Validation: Paradinas Márquez, María del Carmenand Marín Palacios, Cristina. Formal analysis: Paradinas Márquez, María del Carmen. Data curation: Marín Palacios, Cristina. Drafting-Preparation of the original draft: Paradinas Márquez, María del Carmen and Marín Palacios, Cristina. Drafting-Revision and Editing: Paradinas Márquez, María del Carmen and Marín Palacios, Cristina. Visualization: Paradinas Márquez, María del Carmen and Marín Palacios, Cristina. Supervision: Paradinas Márquez, María del Carmen and Marín Palacios, Cristina. Project management: Paradinas Márquez, María del Carmen and Marín Palacios, Cristina. All authors have read and accepted the published version of the manuscript: Paradinas Márquez, María del Carmen and Marín Palacios, Cristina.

Funding: This research received no external funding.

AUTHORS:

María del Carmen Paradinas Márquez

ESIC University/ESIC Business & Marketing School.

Law Degree from the Autonomous University of Madrid (1996). Master's Degree in Human Resources Management and Labor Relations from Camilo José Cela University of Madrid (2016) and Doctor in Tourism from Rey Juan Carlos University of Madrid with Cum Laude mention (2021). One six-year term. Lawyer with more than 20 years of professional practice. Mediator. Lecturer at ESIC University in the Departments of Humanities and Business Management. She teaches History of Spanish Institutions and various subjects of Law (civil, commercial and labor) in Official Degrees, as well as social and labor relations and conflict management in the Master's Degree in People Management and Organizational Development and in the MBA. Director of the academic department of external internships.

carmen.paradinas@esic.university

Índice H: 4

Orcid ID: https://orcid.org/0000-0001-6154-9556

Google Scholar: https://scholar.google.es/citations?user=M0IKBHQAAAAJ&hl=es

ResearchGate: https://www.researchgate.net/profile/Carmen-Paradinas-Marquez

Academia.edu: https://independent.academia.edu/CarmenParadinasM%C3%A1rquez

Cristina Marín Palacios

ESIC University.

Associate Professor accredited by ANECA in Quantitative Methods for Economics and Business, with a six-year research period. Degree in Mathematics (UCM), PhD in Business Economics (URJC), Master in Computer Science and Technology (UC3M) and in Business Management (URJC). My research deals with data analysis and mathematical models applied to ethics, social behavior, gender, disability, employment, and education in marketing and economics. Currently professor of Computer Science in Artificial Intelligence at ESIC University and director of the academic department of Computer Science and New Technologies at ESIC University.

cristina.marin@esic.university

Índice H: 6

Orcid ID: https://orcid.org/0000-0002-4631-1811

Google Scholar: https://scholar.google.es/citations?user=8vsV85IAAAAJ&hl=es

ResearchGate: https://www.researchgate.net/profile/Cristina-Marin-Palacios

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