The ubiquitous presence of networks and connected digital devices (computers, smartphones, and tablets) enabled the rise of new features and applications for Computer-Mediated Communication (CMC). Digital mediated communication has its distinctions and limitations when compared to the usual face-to-face, since there is a lack of emotional information, such as facial expressions, gestures, tone, pauses, posture, etc. (Derks, Bos, & von Grumbkow, 2008a). Among features applied to overcome these limitations are emojis and emoticons, digitally born paralinguistic devices, which help amplify comprehension and expression in online environments (Holtgraves & Robinson, 2020) features that are lacking when people communicate digitally (e.g., texting.

Although they sound similar, emojis and emoticons are distinct elements. Emoticons (emotion + icon) are a result of the combination of ASCII (American Standard Code for Information Interchange) characters, displayed on computer or smartphone keyboards. They conventionally represent only actions or emotions through facial expressions (Moro, 2017). On the other hand, emojis combine pictographic (a symbol that represents things and objects) and logographic functions (a symbol that can represent or substitute words and ideas). The word emoji is derived from the Japanese terms “e” (image) + “moji” (letter) and it was conceived by a designer who made it available to Japanese cellphones in the ’90s (Danesi, 2017).

In the past two decades, these elements have become more spread throughout different platforms (Android, IOS), social networks (Facebook, Instagram, Twitter), messaging apps (WhatsApp, Telegram), and also as a part of the culture (advertising, games, brands). They have their universal coding system (UNICODE), which holds a register of every emoji made available present or in the past. Given their unique characteristics and popularity, emojis and emoticons have become a topic of interest in diverse areas, such as the law Field (BichCarrière, 2019), marketing (G. Das, Wiener, & Kareklas, 2019), communication (Ganster, Eimler, & Krämer, 2012) computer science (Bataineh & Shambour, 2019) among others. In the healthcare field, the research is diverse and heterogeneous, ranging from communication intervention with patients (Al-Rawi et al., 2020; Blunden & Brodsky, 2021; Hu, Zhao, & Wu, 2016; Martin & Grüb, 2020)there is limited evidence of nonverbal behaviors in text-based communication, especially the kinds of unintentional displays central to emotion perception in face-to-face interactions. We investigate whether unintentional emotion cues occur in text-based communication by proposing that communication mistakes (e.g., typos to composing instrument for data collection (Hanson, Elmore, & Swaney-Stueve, 2020; Marengo, Settanni, & Giannotta, 2019; Setty, Srinivasan, Radhakrishna, Melwani, & DR, 2019). This range of applications shows the need for a scoping review, to identify in which ways these communicative tools are being understood and applied, particularly in the healthcare sector. The objective of this scoping review was to identify and classify the academic literature available on the usage and application of emojis and emoticons, with a focus on healthcare research.

Until the submission of this article, we were able to identify 3 systematic reviews published on the subject. Two of which have the goal of identifying literature on emoji only. The authors Troiano and Nante (2018) performed a systematic review with “emoji” as a search keyword in the Pubmed database. During that time, there were only 5 results, among which were articles and reports published between 2015 and 2018, with only one related to healthcare. Bai, Dan, Mu & Yang (2019) also performed a systematic review with “emoji” as the search keyword in the Web of Science and Google Scholar databases. As a result, they found 167 articles published between 1998 and 2019, with 15 being classified by the authors as related to healthcare (according to their classification: 5 in Medicine and 10 in Psychology). Although the number of articles analyzed was bigger, few were related to the healthcare sector. That can be explained by the author’s choices in search strategy, which did not include healthcare specific databases (such as Pubmed). Lastly, we identified that Lotfinejad et al. (2020) there has been a growing interest in studying their effects in scientific and health-related topics over the past few years. Infection prevention and control (IPC performed a review, but they failed to specify the search terms or strategies applied, the results, and the type of review enforced. The article focuses on emoji use in infection prevention in public health.

Because emoji and emoticon research in healthcare are not properly mapped and considering the gaps shown by previous reviews, this article reviews the literature on the subject, published between 2008 and May of 2021. Based on previous studies, we opted for including all peer-reviewed articles published in journals, written in English, Portuguese, or Spanish, with full-text availability in the following databases: LILACS, Scielo, Pubmed, and Web of Science. As a search strategy, the terms “emoji” OR “emoticon” were looked for in titles OR abstracts, OR keywords. The choice to broaden our inclusion criteria was taken to guarantee a preliminary notion of research done with emojis and emoticons in all areas, giving us a basis for a further understanding of specific research in the healthcare area. In this way, we were able also to broaden our understanding of the concept of “health”, including research in areas such as Psychology, Psychiatry, Physical therapy, Anesthesiology, Nursing, Neuroscience, and Nutrition, among others, which may not have appeared if the search terms included “health” (emoji AND health, for example).

This scoping review aims to identify what the academic literature says/has said about emojis and emoticons being used in a context related to healthcare by questioning: a) How have emojis and emoticons been applied to healthcare areas? b) How are emojis and emoticons being used in research? and; c) How can we categorize the research found by its nature, field, and usage of emoji/emoticons?

The conduction of this scoping review was based on the framework and principles reported by Arksey and O’Malley (2005) and further recommendations provided by Levac, Coluhoun, & O’Brien (2010). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)- Extension for Scoping Reviews: checklist and explanation were also followed (Tricco et al., 2018). The preliminary search conducted revealed the body of literature on the matter exhibits a complex and heterogeneous nature. As our primary interest was in identifying a large body of literature on the subject of emoji and emoticon usage in healthcare, the scoping review guidelines helped us map this literature in terms of nature, character, and volume. Following Arksey and O’Malley’s framework, the review followed the basic 5 key steps: Stage 1: Identifying the research question; Stage 2: Identifying relevant studies; Stage 3: Study selection; Stage 4: Charting the data and; Stage 5: Collating, summarizing and reporting the results.

According to the international database of systematic review protocols – Prospero produced by the University of York’s Center for Research and Dissemination (Schiavo, 2019): “PROSPERO does not currently accept registrations for scoping reviews, literature reviews or mapping reviews. PROSPERO is, therefore, unable to accept your application or provide a registration number. This decision should not stop you from submitting your project for publication to a journal.”

• We considered peer-reviewed articles published in journals. We believe adopting this criterion helps us establish a qualitative selection in terms of information quality and credibility of the sources.
• There was no time-of-publication limit as a criterion, first because emoticons and emojis were research topics in different periods, and second, to broaden our results. We got articles dating from 2008 to May 2021.
• The languages accepted as eligible were English, Spanish, and Portuguese. As seen, the other reviews on the topic only took articles in English Following several preliminary scoping searches, which were intended to gain familiarity with the literature and aid with identifying keywords, four databases were searched for relevant literature published until the date of the last search conducted (20th May 2021). The search on databases was the only method applied to the search. The databases used were Pubmed, for its health approach, Web of Science for its humanities approach, and Scielo and LILACS for their records in Portuguese and Spanish. The search strategy was tailored to the specific requirements of each database. There were 2 rounds of search, one conducted on 20/11/2020 and a complimentary one six months later – on 20/05/2021 (Table 1).

Table 1. 1st and 2nd SEARCH conducted.

The process of selecting sources of evidence was undertaken in three stages: 1) Initial screening of title and abstract was performed by the first author and based on our eligibility criteria, 2) Second screening of the full text was implemented to categorize the records by their usage of emoji and emoticon signs. It was performed independently by the first and third authors with an 88% factor of agreement, the discrepancies were then resolved by the second author, 3) Last screening of the full text was performed by the first and second authors to seek out the records which involved emojis and emoticons in a health context (health was defined here as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity” (WHO, 1948). Records that did not meet the criteria applied in the three stages were excluded. The stages of evidence Selection, Identification, Screening, and Eligibility, according to the PRISMA guidelines, are presented in Table 2.

See Figure 1:

The database search results were exported into a reference manager software – Mendeley Ltd. (version 1.19.8 for Desktop). The duplicate records were excluded using Mendeley’s tool for checking duplicates. The records were all managed through Mendeley In order to develop categories for the data, an inductive content analysis (Mikkonen & Kääriäinen, 2020) was performed on the articles resulting from the search. The inductive analysis starts from reading each article by searching the main objective and theme adopted by them (“What is this article about?”), after identifying the themes it is possible to build the thematic categories, and from them, a theory. The identification and classification of the theme in each article were analyzed by two independent researchers who classified them. The analyses were compared between the researchers and showed no discrepancies between them, thus, the analysis of only first of the researchers was considered. Therefore, its purpose is not to test hypotheses but to comprehend the content of each data analyzed (articles). From a full-text review of the articles, we developed classification methods by grouping thematically each one and summarized the data, starting from classification by year of publishing, language, and emoji or emoticon usage. The following categorization resulted in eight categories based on the usage and application of emojis/emoticons in the research context (Online Applications, Nature of emoji/emoticons, Research instrument, Neural and Cognitive Tests, Health Information Technology, Data for Algorithm Design, Medical Images, and Others). Other 6 categories were developed based on the field of research, taking into consideration the article’s full text, as well as keywords and areas of the journals (Healthcare, Communication, Business/Marketing, Computer Science, Linguistics, and Others). Expanding from this field classification, the articles identified as healthcare-related were divided into eight categories based on themes (Health Information Technology, Nutrition/Food Safety, Neuroscience, Mental Healthcare/well-being, Physiology, Institutional, Biotechnology, and Others).

The literature search retrieved 906 citations, and after duplicates were removed, 708 remained. Overall, 225 references were considered potentially eligible. After the full-text assessment, 67 articles were excluded, and 158 were included. Fig. 2 shows the flow chart of articles through the scoping review.

A full-text read was performed by the first and second authors to determine the categories, sections, or fields of research identified. Upon categorization, the data showed tendencies in research related to emoji/emoticon usage. Specifically, emojis were found to be the predominant ones used in these studies. The data also indicates a growing field of research in this area, with the majority of results being published recently (2020). Another notable finding is the predominance of research in the ‘Nature of emoji/emoticon’ category. While the majority of published studies treat emojis/ emoticons as mere tools for assessing mood or opinion, a considerable amount of research is focused on studying the essence or nature of these devices. See table 2:

Table 2. General characteristics of included records (n=158)

The objective of this scoping review was to identify and classify the academic literature available on the usage and application of emojis and emoticons, with a focus on healthcare research. By focusing first on records from all fields, we were able to determine the tendencies in research on emoji/emoticon usage from a broader perspective. The discussion was portioned into general characteristics of the records such as year of publication, language, and emoji/emoticon usage, followed by the categorization of the records.

Starting exclusively with emoticons, the research on the field had relatively stable numbers from 2008 with 2 publications (Blunden & Brodsky, 2021; Lee, Tang, Yu, & Cheung, 2008) until 2014, with 4 (Churches, Nicholls, Thiessen, Kohler, & Keage, 2014; Han, Yoo, Kim, McMahon, & Renshaw, 2014; Skovholt, Grønning, & Kankaanranta, 2014; Yus, 2014). In 2015 we observed growth in 7 publications (D’Anna et al., 2015; Hudson et al., 2015; Kralj Novak et al., 2015; Settanni & Marengo, 2015; Siegel et al., 2015; Stark & Crawford, 2015; Vasiljevic, Pechey, & Marteau, 2015) and also the firsts articles regarding emojis (Kralj Novak et al., 2015; Stark & Crawford, 2015). This may be explained by the popularization of smartphones and mobile connectivity networks in the early 2010s, with a consequent increase in emoji usage. There after, we saw an increasing number of publications each year, with 2020 having the most publications that met our eligibility criteria (54 records). Some factors can also explain the growth of emoji popularity, such as the inclusion of emojis on Unicode 6.0 in 2010 (a universal and cross-platform character encoding system) (Unicode, 2010) which allows researchers to identify, collect and analyze emojis from different platforms (Fugate & Franco, 2021; Kaye, Wall, & Malone, 2016; Rodrigues, Lopes, Prada, Thompson, & Garrido, 2017). Also, emojis are now a part of the culture, being present and recognized in Ads, brands, movies, and other cultural materials (Danesi, 2017).

The records showed that most of the research is focused on emoji-only usage. This may be explained by the standardization of the emoji code – mentioned before as opposed to the emoticons, built from ASCII symbols. There is also discussion as to whether the emoticon conveys emotional signals effectively, is less effective than emoji (Boutet, LeBlanc, Chamberland, & Collin, 2021; Kralj Novak et al., 2015)called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive, and because emoji can express emotion more realistically than emoticons (Sampietro, 2020). Also, emojis can represent more things rather than facial expressions (Danesi, 2017).

The first classification conducted, by usage and application, revealed that the main use of emoji/emoticons was as a tool for composing research instruments (29.7%). The examples showed emoji usage: on mood scales (De Angeli, Kelly, & O’Neill, 2020; Deubler, Swaney‐Stueve, Jepsen, & Su‐Fern, 2020; Machata et al., 2009), questionnaires (Ares & Jaeger, 2017; Jaeger, Lee, & Ares, 2018), and tools for emotional assessment of participants (Ikeda, 2020; Jaeger, Roigard, Jin, Vidal, & Ares, 2019; Lee et al., 2008). Using emojis/emoticons as emotional feedback tools allows a quantification of emotion that helps estimate mood, in both online and offline environments. As such, researchers apply it commonly in situations where feedback is needed for products or services. In this category, most of the records corresponded to research in healthcare or business/marketing sectors, confirming that emojis/emoticons are valuable instruments to access the user experience. We noticed a tendency of the emoji/emoticon application in the healthcare sector, especially in the nutrition field, where emoji/emoticons were used in questionnaires about food preferences (Ares & Jaeger, 2017; Hendrie et al., 2019; Kytö et al., 2019; Pinto et al., 2020; Ray & Merle, 2020; Vasiljevic et al., 2015), and to compose instruments dealing with children (da Cruz et al., 2021; Galler, Næs, L. Almli, & Varela, 2020; Hanson et al., 2020; Lima, de Alcantara, Martins, Ares, & Deliza, 2019; Maxwell et al., 2018; Sick, Monteleone, Pierguidi, Ares, & Spinelli, 2020; Siegel et al., 2015; Velardo, Pollard, Shipman, & Booth, 2021).

The use of emojis as assessment tools sometimes was composing pre-designed formularies and questionnaires (De Angeli et al., 2020; Deubler et al., 2020; Hanson et al., 2020; Hendrie et al., 2019) or they are created especially for the research (Ikeda, 2020; Jaeger et al., 2019; Machata et al., 2009; Setty et al., 2019). This shows a tendency to see emojis/emoticons as a way to encapsulate and quantify complex emotions and provide a way to acquire the user or consumer ́s feedback. Emotions are part of the human ensemble known as the “quantifiable self”, which are parts of the self available to be tracked and archived, used commonly in healthcare and marketing strategies (Combs & Barham, 2016; Pentland, 2014). Then, emojis/emoticons are an important piece of information about the individual’s feelings which are also a piece of subjective information that is very difficult to extract (Picard, 1997) even by using physiological sensors (Healey, 2011; Olguin, Gloor, & Pentland, 2009; Pantic & Rothkrantz, 1990). In this category, we saw scientists applying emojis/emoticons to extract, collect and archive emotional data about the participants in a more efficient and large-scale way, and to minimize misinterpretations about their experiences.

What can also explain the number of articles that deal with emojis in instruments to collect data is that, according to Marengo et al. (2019), the development of text-based instruments has its limitations. When it comes to different cultures, varying language skills and educational levels can affect the interpretation capacity of the subjects (Bhattacharya, Singh, & Rillera Marzo, 2019). The authors Bhattacharya et al. (2019) and Marengo et al. (2019) suggest that visual-based instruments, such as those developed with emoji/emoticons, can overcome these limitations and reach a wider range of individuals. In our records, we found research applying emoji/emoticon-based instruments when dealing with different socio-cultural groups: such as children (da Cruz et al., 2021; Galler et al., 2020; Hanson et al., 2020; Jayne, 2019; Lima et al., 2019; Olivos-Jara, Segura-Fernández, Rubio-Pérez, & Felipe-García, 2020; Setty et al., 2019; Sick et al., 2020; Souchet & Aubret, 2016; Van Dam et al., 2019; Vaughn et al., 2020; Velardo et al., 2021), low-literacy populations (Koladycz, Fernandez, Gray, & Marriott, 2018), visually impaired (Machata et al., 2009) and elderly (Koladycz et al., 2018; Kuerbis, van Stolk-Cooke, & Muench, 2017), which may not have the same vocabulary to convey certain emotions.

Applications online, the second category with the most records, focused mainly on the analysis of communication in online environments, not only on online social networks (AlRawi et al., 2020; Alanazi, 2019; Albawardi & Jones, 2020; Belcastro, Cantini, Marozzo, Talia, & Trunfio, 2020; Darginavičienė & Ignotaitė, 2020; A. Das, 2021; Gabarron et al., 2020; Hu et al., 2016; G.-H. Huang, Chang, Bilgihan, & Okumus, 2020; Hudson et al., 2015; Huesch, Chetlen, Segel, & Schetter, 2017; Kariryaa, Rundé, Heuer, Jungherr, & Schöning, 2022; Konrad, Herring, & Choi, 2020; Kralj Novak et al., 2015; Martin & Grüb, 2020; McShane, Pancer, Poole, & Deng, 2021; Mercier, Senter, Webster, & Henderson Riley, 2020; Mulki, Haddad, Bechikh Ali, & Babaoğlu, 2018; Oleszkiewicz et al., 2017; Pereira-Kohatsu, Quijano-Sánchez, Liberatore, & Camacho-Collados, 2019; Pitarch, 2021; Ricard, Marsch, Crosier, & Hassanpour, 2018; Settanni & Marengo, 2015; Shaari, 2020; Tian, Batterham, Song, Yao, & Yu, 2018; Wang et al., 2016; Wei, Tsang, Wong, & Lok, 2020)but also how English is strategically mixed with photos, drawings, emoji’s, and other languages to create meanings, identities, and relationships. The theoretical framework used to understand these strategies is adopted from ‘geosemiotics’, an approach to discourse that focuses on how meanings (as well as identities and relationships, messaging apps (Cantamutto & Vela Delfa, 2019; L. Li & Yang, 2018; Steinberg, 2020), games (Babin, 2020) and another kind of online social interactions, especially online social networks (Acker & Murthy, 2020; Arsenault, Blouin, & Guitton, 2016; Blunden & Brodsky, 2021; Chung, Cho, & Park, 2021; Ge-Stadnyk, 2021; Honkanen & Müller, 2021; Moloney, Tuke, Dal Grande, Nielsen, & Chaber, 2021; Tian et al., 2018; Wei et al., 2020) there is limited evidence of nonverbal behaviors in text-based communication, especially the kinds of unintentional displays central to emotion perception in face-to-face interactions. We investigate whether unintentional emotion cues occur in text-based communication by proposing that communication mistakes (e.g., typos. Focusing on user behavior, patterns, and tendencies, many of these researches are concerned with the understanding of the user behavior characteristics, emoji/emoticon’s semantical, syntactical, and overall communicative potential, as well as the context in which the emoji/emoticons can be applied to online discourse (Hu et al., 2016; L. Li & Yang, 2018).

This category showed different methodologies to extract and analyze emotional data. The sentiment analysis field, or opinion mining, is a leading tendency in these records, especially in the three categories “Research instruments”, “Applications online” and “Data for algorithm design”. It consists of a datafication of sentiment, emotions, behavior, and opinions shared online (Kralj Novak et al., 2015), elements that benefit from the large quantity of data available online. The objective of most of these data mining researches is the construction of sentiment lexicons, and many of them use emoji/emoticons as samples to design and improve machine learning and other computational techniques (Bataineh & Shambour, 2019; Chen et al., 2021; Krommyda, Rigos, Bouklas, & Amditis, 2021; D. Li, Rzepka, Ptaszynski, & Araki, 2020; Peng & Zhao, 2021; Shi et al., 2019; Ullah, Marium, Begum, & Dipa, 2020; Urabe, Rzepka, & Araki, 2021; Wu, Lu, Su, & Wang, 2019). More technical papers dealing with the description of systems designed to analyze the sentiment using emojis and they pointed out some advantages and disadvantages of algorithms built in different perspectives. Basically, the emotional data that came from emojis is mined to feed these sentiment lexicons, aiming to improve the system itself. Our records confirm this tendency of big data extraction for archiving and analyzing user behavior towards different social situations online, such as political discourse (Belcastro et al., 2020; Pitarch, 2021), hate speech (Babin, 2020; Pereira-Kohatsu et al., 2019), experiences in translanguaging (Darginavičienė & Ignotaitė, 2020; Wei et al., 2020) and brand engagement (G.-H. Huang et al., 2020; McShane et al., 2021).

The third category with the most records, the nature of emoji/emoticons, discusses the potential of such signs from a communicative and semiotic perspective (Ahumada & Gherlone, 2019; Boutet et al., 2021; Derks, Bos, & von Grumbkow, 2008b; Estrada Chichón & Ortiz Jiménez, 2020; Fischer & Herbert, 2021; Gesselman, Ta, & Garcia, 2019; Holtgraves & Robinson, 2020; Nexø & Strandell, 2020; Ribeiro, Amorim, & Nunes, 2016; Skovholt et al., 2014; Wagner, Marusek, & Yu, 2020; Wicke & Bolognesi, 2020). Just as the universality of facial expressions has been questioned by some authors (Elfenbein & Ambady, 2002; Jack, Garrod, Yu, Caldara, & Schyns, 2012), the universality of facial emojis/emoticons was also in debate in some cases (Guntuku, Li, Tay, & Ungar, 2019). Not only are there different interpretations of emoji/emoticons across cultures (Estrada Chichón & Ortiz Jiménez, 2020), but some researchers show that gender and age are factors that also impact the emojis/emoticon interpretation (Annamalai & Abdul Salam, 2017; Butterworth, Giuliano, White, Cantu, & Fraser, 2019; Shah & Tewari, 2021). There is also a concern about the different interpretations across platforms (Fugate & Franco, 2021; Ge-Stadnyk, 2021). This may also explain why we faced articles dealing with cognitive and neural tests (see Neural/Cognitive tests” category), trying to answer how these codes are processed and interpreted by different individuals (Barach, Feldman, & Sheridan, 2021; Gantiva et al., 2019; Han et al., 2014; Howman & Filik, 2020; Kim, Lee, Choi, Kim, & Jeong, 2016; Tang, Chen, Zhao, & Zhao, 2020; Thompson, Mackenzie, Leuthold, & Filik, 2016; Weiß, Gutzeit, Rodrigues, Mussel, & Hewig, 2019; Weissman & Tanner, 2018).

Most of the records in the “nature of emoji/emoticon” category argued about how the social and contextual factors heavily impact the emoji usage and understanding. Such discussion is necessary to establish the emoji/emoti- con as a valid resource for scientific research, seeing that social nuance may impact the research results and, therefore, its reliability (Derks et al., 2008b; S. Liu & Sun, 2020; Wicke & Bolognesi, 2020).

The Health Information Technology/Health Intervention category deals with healthcare articles that highlight the use of emojis as a health communication tool in different scenarios, such as: in healthcare provider-patient communication (Adarkwah et al., 2019; Bhattacharya et al., 2019; Fane, MacDougall, Jovanovic, Redmond, & Gibbs, 2018; R. Huang et al., 2020) and campaigns in hygiene promotion and food intake (Franco, da Cunha, & Bianchi, 2021; Mendes, de Jesus Mateus, & Costa, 2020; Patel & Rietveld, 2021). Less expressively, the category “medical images” represent just two records that describe some visual patterns visualized on medical/biological images and named as “emoji-like”, so, not entirely dealing with the code itself (de Mena & Rincon-Limas, 2020; Guarrotxena, García, & Quijada-Garrido, 2018).

From our second classification of the records, based on the sector or field of research, we were able to identify the articles on the healthcare sector. 9 different subcategories were detected. The foremost category, “Health Information Technology”, representing 24.2% of records, consists of the management of health information data and its exchange between patients, healthcare providers, the government, and others (Bhattacharya et al., 2019). According to the World Health Organization, IT technologies can help collect, store, retrieve, and transfer health information (WHO | World Health Organization, 2015). In our findings, these records showed research that collects and analyzes data on the interaction of the general public with health information systems (Al-Rawi et al., 2020; Arsenault et al., 2016; Bhattacharya et al., 2019; R. Huang et al., 2020; Huesch et al., 2017; Machata et al., 2009; Mercier et al., 2020; Ricard et al., 2018). We saw a particular tendency of monitoring online social networks to understand different aspects of health issues and also the public’s perception or experience with health services (Arsenault et al., 2016; Chung et al., 2021; Gabarron et al., 2020; Huesch et al., 2017; Martin & Grüb, 2020; Ricard et al., 2018; Teoh et al., 2019; Tian et al., 2018).

In this category, we also noticed that emoji/emoticons were being used in the healthcare provider-patient communication (Al-Rawi et al., 2020; Blunden & Brodsky, 2021; Hu et al., 2016; Martin & Grüb, 2020) and as a part of health interventions (Albawardi & Jones, 2020; Kralj Novak et al., 2015; L. Li & Yang, 2018), where health information is delivered with the support of emoji/emoticons. Health interventions and health information delivery were presented in articles from the “Nutrition/Food safety” category (Franco et al., 2021; Hendrie et al., 2019; Lima et al., 2019; Patel & Rietveld, 2021; Ray & Merle, 2020; Siegel et al., 2015; Vasiljevic et al., 2015). We faced some articles that use emoticons/emojis to optimize the health information delivery content (Hendrie et al., 2019; Lima et al., 2019; Siegel et al., 2015), and by doing this, the emoji/emoticon was part of a health intervention strategy.

Essentially, this follows the tendency discussed previously about big data usage in healthcare and reveals a lack of studies that use emojis/emoticons as an independent tool for intervention research. Even in the records that used emoji/emoticons in a research instrument, there is a lack of protagonist. It is often used as a secondary method to assess the participants’ feelings throughout the process. This tendency to use and evaluate emotion through mood scales also has not changed since the first records were published, showing a lack of innovation when it comes to applying such tools.

Scoping reviews are useful for synthesizing research evidence and are often used to categorize existing literature in a field, including health themes (Fakoya, McCorry, & Donnelly, 2020). They are particularly useful when a body of literature exhibits a large, complex, or heterogeneous nature (Peters et al., 2015) as in the case of emoji/emoticons applications. Although we found that linguistic and cultural studies have been reviewing the validity of emoji/emoticon use as a supposed universal code understood across all genders, ages, cultures, and social backgrounds, its usage in academic research seems to be well established. The review showed a growing tendency of using emojis in research, particularly as facilitators in the research instruments composition, where they are used as a tool for expression and interpretation of mood and complex emotions, and as such, end up having a secondary role in most of these studies.

Future research may rely on the use of emojis in health interventions with diverse populations, as resources for patient health-professional communication, and as data to be analyzed and extracted from interaction in digital environments. The creation of more robust algorithms for analysis in addition to research that validates these codes as resources for text interpretation will allow them to be applied in larger contexts. There was also a lack of research that think about the application of these resources in a more ethical way, including the consequences of reading these data by IT companies, the quantification of emotion in online spaces, and the surveillance capitalism approach (Zuboff, 2020).

The large body of articles read made the classification of our systems and categories possible. We adopted a more inductive content analysis technique to create thematic categories and future different approaches can give another way to classify the articles. Correlations between article ́s themes, nationalities of the authors or journals, year of publication could be next steps. Also, even by using the university internet proxy some small numbers of articles were still not available for full access. However, the theme of emojis and emoticons appeared in different kinds of publications that we were not able to access at this moment. The references of the analyzed articles were not accessed and the grey literature (Paez, 2017) (thesis, conference proceedings, preprints, presentations, books, newsletter) was also not analyzed, which can open to different topics and themes.

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