Home / Articles / Volume 20 (2022) / “You’re a rockst⭐r *heart eyes*” – What the Functions of Emoji Reveal about the Age and Gender of their Users on Instagram 
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Emoji have become an integral part of computer-mediated communication, and their use has been soaring since their introduction to Apple phones in 2011. Previous studies have identified age differences in the use, perception, and interpretation of emoji, but age differences regarding the functions that emoji serve and how they are related to pictures they accompany have not yet been investigated. Based on a corpus of 60 Instagram users, this study investigates whether younger Instagram users employ emoji differently from older users, and also whether gender-related differences exist in the choice of emoji and in the functions that emoji serve in relation to both pictures and texts. The analysis makes use of an emoji classification system designed for authorship analysis and expands that system by adding more fine-grained distinctions. Findings indicate higher use of emoji by females and younger users, as well as age- and gender-related functional differences in emoji use.

Emoji were invented in Japan in the late 1990s (Goldman, 2018) and have been described as “preconstructed and largely standardized pictorial characters” (Danesi, 2016, Chapter 1). They are now used world-wide in all forms of digital communication, including on social media platforms (Page et al., 2014) and in text messaging (Goldman, 2018). Even though they were introduced in the 1990s (Novak, Smailovic, Sluban & Mozetic, 2015), their use took off only when they were made accessible for easy use on iPhones 2010 (Evans, 2017). According to Emojipedia (n.d., accessed 2023), more than 700 million emoji are used on Facebook every day. In 2015, the Face with Tears of Joy 😂 [U+1F6021] was voted the Word of the Year by the Oxford Dictionary (Ai et al., 2017). It is thus unsurprising that many researchers have begun to investigate emoji more closely (see, e.g., McMahon & Kirley, 2019; Miller, Kluver, et al., 2017; Oberlaber, 2020; Wagner et al., 2020). The present article focuses on how users’ age and gender are related to emoji use. This is not only relevant for the study of emoji in and of themselves. Such findings are also useful to forensic linguists doing authorship analyses that involve digital data. Forensic authorship analysis is concerned with both authorship comparisons and sociolinguistic profiling. The former addresses linguistic problems in which anonymous texts are compared to investigate potential common authorship, while the latter is used either to narrow down lists of suspects or to establish new leads when no suspects have been identified by connecting linguistic features with sociodemographic information (e.g., Coulthard et al., 2017; Grant & MacLeod, 2020; Nini, 2018). The relevance of extra-linguistic features to authorship analysis has been demonstrated with emoticons (Sousa Silva et al., 2011) and more recently with emoji (Marko, 2020). Given that many digital texts, be it text messages, social media posts, or emails, are rather short, it is likely that few linguistic markers relevant for authorship analysis can be found in these texts. Thus, it is important to venture into the realm of multimodal analysis (Heydon, 2019) and find ways to integrate additional markers of authorship into forensic linguistic analyses. The analyses presented in this article introduce delicate differentiations of emoji use among users of different ages and also take gender into account.

The present study adds to the research on both emoji and forensic linguistics. Firstly, it seeks to replicate previous findings with regard to age- and gender-related use of emoji, as well as explore the functions of emoji on the social media platform Instagram. Further, it establishes a connection between the use of emoji and the picture it accompanies. Lastly, the study expands on the little extant research on the use of emoji in terms of authorship analysis and highlights how such research can be helpful to forensic linguists.

Since emoticons, which are created with ASCII characters, are the predecessors of emoji (e.g., Goldman, 2018), it is worth looking at how emoticon use correlates with age and gender. A comprehensive overview of this topic is provided by Koch et al. (2022). They ­­outline research that has detected negative correlations between younger and older subjects in emoticon use on the social media platform Facebook (e.g., Oleszkiewicz et al., 2017). Research on other modes of computer-mediated communication such as chat rooms, however, has not shown age-related differences (Fullwood et al., 2013). Regarding gender, a longitudinal study of emoticon usage in text messages found noteworthy differences: While Tossell et al.’s (2012) study supports previous findings that indicate a higher use of emoticons by females, the results also suggest that men use a larger variety of emoticons (see also Wolf, 2000).

Research on emoji use has also been manifold, as hinted at above. For instance, Chen et al. (2018) analyzed the use of emoji in relation to gender and found that in their corpus of text messages from Android users, women used more emoji overall, and men and women showed different preferences for specific emoji. Interestingly, Chen et al. (2018) found a difference in the use of faces and hearts: Females in the corpus used a larger range of face emoji, while the males exhibited a greater use of heart emoji.

Studies that have investigated the relation between emoji use and both age and gender are particularly relevant to the present study. Herring and Dainas (2020) examined gender and age differences in emoji interpretation and evaluation. Their study revealed “no significant overall differences in how [men and women] interpreted emoji function” (p. 9); however, they detected age-related differences in the interpretation of emoji that support the notion that older people often do not understand emoji or tend to interpret emoji literally. Younger people, in contrast, often employ emoji to mean something more abstract or conventionalized than the literal message. Similarly, An et al.’s (2018) analysis shows that while older individuals favor simple and positive emoji, younger users tend to use more complex ones. Weiß et al. (2020) found considerable age-related differences in emoji evaluation regarding the smiling emoji. Additionally, Gallud et al. (2018) argue that older people use a rather small variety of emoji compared to younger people.

The present study also considers whether emoji are used by themselves (stand-alone, or “naked”; Provine et al., 2007), in compositions, or in strings. Stand-alone emoji can replace more than just a single word and convey a complete sentiment or message (e.g., Goldman, 2018). In contrast to most social media platforms, however, emoji on Instagram can never appear completely alone, since they always have to be accompanied at least by a picture. Thus, in the present context, ‘stand-alone emoji’ refers only to emoji that are not accompanied by text. Emoji used in compositions (i.e., the sequential use of several different emoji without intervening text or punctuation; see Ge, 2019) are said to “change the value of a preceding [emoji]” (Schneebeli, 2017, p. 9) in the same sequence or composition. Lastly, the use of emoji strings (i.e., the sequential use of the same emoji without intervening punctuation and/or text) serves as emphasis and increases the strength of a statement (Schneebeli, 2017). To my knowledge, neither stand-alone emoji nor the use of strings or compositions have been studied in relation to the users’ age and gender.

Emoji have been classified in a variety of ways. The most common and relatively unitary classifications involve grouping emoji into clusters according to what they are supposed to represent. One such example is the classification of emoji on cell phones, which groups emoji into categories such as “events,” “people,” “animals & nature,” “food & drink,” etc. (Barbieri et al., 2016; Donato & Paggio, 2017; Emojipedia, n.d.; Lin et al., 2014; Vidal et al., 2016). Such classifications ignore the functions of emoji in their respective contexts. However, they are still relevant and have led to surprising results, such as in an authorship analysis study (Marko, 2022) which suggested that users are more easily identifiable based on the specific emoji they use rather than on the functions the emoji serve.

Other researchers classify emoji according to the functions they serve in their respective communicative contexts. Evans’ (2017) system is an example thereof and also provides the starting point for the classification system used in this study. Evans argues that emoji serve six functions in the context of written text, namely substitution, complementation, reinforcement, contradiction, emphasis, and discourse management. Danesi (2016) has also provided a system that classifies emoji according to their functions, as have Herring and Dainas (2017), Schneebeli (2017), and Gawne and McCulloch (2019), among others. Gawne and McCulloch (2019) add another layer to the description of emoji by arguing that they resemble not only non-verbal cues but, specifically, gestures in face-to-face interactions.

The present study investigates both the use of emoji types and the functions they serve in their respective contexts, while additionally taking the characteristics of the accompanying picture into account. To my knowledge, no previous study has specifically looked at emoji and their relation to the pictures they accompany. Since the data used in this study is from Instagram, a platform mainly used for sharing pictures (Leaver et al., 2020), emoji always accompany a picture. This is not true for platforms that allow text-only posts as well. Thus, the present data lends itself very well to an analysis of the emoji-picture relationship and has the potential to reveal new and interesting avenues for future research.

Based on the literature reviewed above, the following research questions were developed for this study:

RQ1. How are specific types of emoji related to users’ gender and age?

RQ2. How are the functions of emoji in posts to Instagram related to the users’ gender and/or age?

RQ3. How are the groupings of emoji (i.e., stand-alone emoji, strings, and compositions) related to users’ gender and/or age?

RQ4. How, if at all, are stand-alone emoji related to the picture they are posted with? Is there a difference in the use of such emoji according to the users’ gender and/or age?

The study draws on a corpus of posts produced by 60 individuals who had public Instagram profiles at the time of data collection in 2020. Individuals were chosen in a snowball fashion: after several individuals were identified and deemed appropriate for the study, the profiles of these individuals were used to identify further suitable individuals. Data was collected over several months in the spring and summer of 2020. Individuals were deemed suitable if their profiles clearly indicated the gender they identify with and their age. All individuals in the corpus are US Americans and native speakers of English. The most recent 100 Instagram posts from each user were collected, as well as the emoji from these posts, for a total of 6,000 posts and more than 6,900 emoji. The corpus consists of data from 30 males and 30 females, and the age range of the individuals is 14 to 69, with a mean age of 26.4 years (median: 24 years; mode: 15 years), which reflects the demographics of typical Instagram users (statista, 2020).

Instagram, which was officially launched in 2010, is a social media platform that emphasizes the visual and is “best known for selfies and self-presentation” (Leaver et al., 2020, n.p.). On social media platforms such as Instagram, the use of emoji has been soaring: In 2019, approximately 47.7% of posts to Instagram contained at least one emoji (statista, 2023). The present study focuses particularly on Instagram, because unlike other social media platforms, Instagram does not allow posts to be directed to an individual, and posts made to public profiles presumably have the same large audience, i.e., users of the Internet. Thus, the effect of the audience (Bell, 1984) on the language (and emoji) of the posts can be controlled for better than for other, similar platforms. In particular, Instagram was chosen because this platform does not support text-only posts, meaning that each post has to be accompanied by a picture, although it is possible to post a picture without a caption (Leaver et al., 2020). Figure 1 shows a typical Instagram post, as viewed on a computer screen.

Figure 1. Sample Instagram post

In Figure 1, the picture is on the left and the accompanying text is on the right. The affordances (e.g., Paige et al., 2014) of Instagram are also illustrated in this example: Users, on the one hand, can post the picture and accompanying text, add emoji, hashtags, and tags (@mentions); viewers, on the other hand, can like, comment on, share, and save the post.

For several of the analyses presented in this paper, the subjects were grouped into the following age groups: Group<19 (16-19 years; 20 individuals; 10 female, 10 male), Group20 (20-29 years; 20 individuals; 11 female, 9 male), Group30 (30-39 years; 13 individuals; 5 female, 8 male), and Group40+ (40 or older; 7 individuals; 4 female, 3 male). This specific age categorization was chosen for the following reason: even though it is best not to divide age groups in terms of actual age but in terms of important life events (e.g., Coulmas, 2012; Settersten & Mayer, 1997), such as high school, university, (first) jobs, retirement etc., it is impossible to know the social age of the individuals, which stage of life the individuals in the study are in, or which life event dominated their life at the time of data collection. Thus, an approximation must suffice, and the groups were selected to reflect adolescents and teenagers (Group<19), young adults (Group20), adults (Group30), and middle-aged adults (Group40+). This is similar, albeit not identical for reasons of sample size, to the age groupings in Herring and Dainas’s (2020) and Siebenhaar (2018)’s studies. The classification into age groups is used to show behaviors exhibited by different users in terms of emoji use and emoji functions at a group level. However, the correlations presented in this article were performed with age as a continuous variable, in order not to lose valuable information by artificially grouping users into age categories.

In terms of gender, the subjects were grouped into males and females according to how they self-identify (i.e., in terms of social gender rather than biological sex). Gender is viewed as a continuum (cf. Bing & Bergvall, 1998); thus, if a biologically male subject identifies as female, they will be included in the group of females, since social gender has been suggested to have a larger influence on the use of language than biological sex (e.g., Carothers & Reis, 2013; Nini, 2018).

Emoji used in hashtags and re-posts with the original posts present were excluded from the analysis. After applying the exclusion criteria, the corpus was left with a total of 5,755 posts and 6,815 emoji.

Research conducted with social media data requires specific ethical considerations as to the privacy of the included individuals (Townsend & Wallace, n.d.). The present study analyzes a corpus of Instagram posts which are publicly available. As Instagram’s (2018) regulations allow only people older than 13 to have an account with the platform, no person younger than 13 was included in this study. Most of the results are presented in aggregated form, making it impossible to discern who the involved individuals are. Moreover, where examples are given, all names of individuals are anonymized, and, following Gawne and McCulloch (2019) and Ayers et al. (2018), content words are replaced to prevent reverse identification via search engines.

A number of scholars have put forth categories according to which emoji can be classified. Evans’ (2017) system serves as the main reference for the present classification. It describes six functions of emoji; two of them, however, were excluded here. Firstly, the category of emphasis was removed, as it overlaps strongly with reinforcement and was thus abandoned as an individual category. Second, the category of discourse management is not useful in the present context and was thus deleted. Discourse management functions apply in interactive situations in which users have a conversation; however, this is not the case for social media posts. Marko (2020) adapted this reduced version of Evan’s classification system and applied it to forensic authorship analysis, arguing that individual users can be differentiated from one another based on their use of emoji. The present study takes this adapted system as a starting point and refines it to allow for more fine-grained results. Essentially, the original definitions were extended to allow for a clearer separation among the individual functions and to facilitate classification (see Table 1). In this article, the emoji are presented as they were rendered by Instagram in the macOS 10.15 version at the time of data collection.

Table 1. Emoji Functions based on Marko (2020), originally based on Evans (2017)

The first category, substitution, refers to emoji that are used instead of a word, a letter, or a sentence. The emoji is non-redundant (Zanzotto et al., 2011) in that it adds information that is not conveyed by the accompanying text. The second category, reinforcement, is used for emoji that repeat an idea that is already encoded in the text, thereby making the emoji essentially redundant (Zanzotto et al., 2011). Schneebeli (2017) even goes as far as saying that such emoji do not serve any linguistic or paralinguistic functions at all, but that they are merely illustrative or used for aesthetic purposes. Similarly, Siever (2020) argues that such emoji serve only a decorative function. Emoji in the third category contradict the message of the text they accompany. Thus, these emoji are also non-redundant (Zanzotto et al., 2011), as they add a layer of meaning and guide the reader to the correct interpretation of the text. Frequently, contradictory emoji are used to convey humor, to create irony, or to imply that what the text conveys is in fact not true. Lastly, emoji can be used to complement the respective text. These emoji serve as a meta-comment and are therefore non-redundant (Zanzotto et al., 2011). Herring and Dainas (2017) argue that such emoji also help to clarify the intent of the sender and can hedge the illocutionary force of the text, thereby serving as a mitigating strategy (e.g., Siever, 2020) that can also be interpreted in terms of linguistic politeness.

This classification scheme was originally designed for the analysis of emoji in relation to text. For the purposes of the present paper, the scheme was also applied for the analysis of emoji in relation to pictures. However, for such an analysis, the category of substitution had to be eliminated, and the category of contradiction cannot be applied in a straightforward manner. That is, whether or not an emoji contradicts an image can only rarely be completely disambiguated. The other categories, however, are also applicable to the analysis of the emoji-picture relationship. In order to test the applicability of the categories in relation to pictures, they were tested for interrater reliability with three other researchers on a subset of the corpus comprising 100 randomly selected posts with at least one emoji present, and agreement was 81% for the emoji in relation to the text and 73% for emoji in relation to the pictures. This indicates that, as anticipated, the classification in relation to the pictures is more subjective and open to interpretation than the classification in relation to the text. For future studies, the classification system should be adapted even further so that it can be applied to images.

In total, 6,815 emoji (6,672 emoji excluding stand-alone emoji)2 were used by the participants in the study. A first analysis shows that the overall use of emoji decreases with increasing age. Taken together, Group<19 and Group20, with a use of 38.2% (2,548.7) and 40.4% (2,695.5) of all emoji, respectively, far outperform the users older than 30, whose emoji collectively only account for 21.5% (1,434.5) of all emoji in the corpus. The correlation of -0.34 (p=0.008) of emoji use with age is significant, and it is depicted in Figure 2. Further, it was found that females employ emoji more than their male counterparts,3 with a total of 59.8% (3,989.9) of all emoji in the corpus (see Figure 2).

Figure 2. Correlations of overall emoji use with age (left); gender difference in overall emoji use (right)

In respect to the variety of emoji used in the corpus, the following findings were obtained (numbers in brackets refer to the normalized values per 100 emoji): the youngest group uses 333 (54.6) different emoji, Group20 uses 443 (72.6) different ones, Group30 uses 252 (41.3), and Group40+ uses only 106 (17.4) different ones. For instance, this means that for the youngest group, out of 100 emoji, 54.6 are different ones, for Group20, out of 100 emoji, 72.6 are different ones, and so on. These figures indicate that age differences exist in terms of emoji variety: Gallud et al. (2018) found that younger people use a wider variety of emoji compared to older people, and their findings appear to hold true for Instagram as well. A correlation analysis revealed a statistically significant negative correlation of -0.39 (p=0.006) of the variety of emoji use and the respective users’ age, with a comparably steep decline for both males (-0.21, p=0.3) and females (-0.33, p=0.12), as seen in Figure 3.

Figure 3. Variety of emoji use correlated with age (top), for females (bottom left), and for males (bottom right)

When the most frequently used emoji across the four age groups are examined, interesting results emerge. First, the Red Heart ❤️ [U+2764] is the most common emoji for all age groups except for Group<19. For them, the Camera with Flash 📸 [U+1F4F8] is more common and is mainly used to attribute photo credits to another person. Hearts in general (including hearts of different colors), however, are most common for the youngest individuals. Table 2 summarizes the 10 most common emoji in each group.

Table 2. 10 most common emoji in each age group

As previous studies have indicated, face, gesture, and heart emoji are particularly common, hence these types are examined more closely in the subsequent analyses. First, the use of face emoji declines for males in relation to age (-0.16, p=0.56), while it increases weakly for females (0.03, p=0.87). The youngest males use more face emoji than any of the other males. For example, the males in Group<19 use four different face emoji, and for them, the Beaming Face with Smiling Eyes 😁 is the most common emoji overall. In the same age group, the females use seven different faces, with the Smiling Face with Three Hearts 🥰 as the most frequent. With one exception (Group20), the females use more face emoji than the males. In what follows, we take a closer look at the types of faces they use and whether differences emerge there, as well.

The first interesting finding involves the use of the Face with Three Hearts 🥰: It is used consistently by the females in all age groups. In contrast, it does not appear for the males in any of the age groups investigated. The same is not true for the Smiling Face with Heart Eyes 😍 and the Face Blowing a Kiss 😘 [U+1F618], which are also used by the males of most investigated age groups. “Simple” smiling faces such as The Smiling Face with Smiling Eyes 😊 [U+1F60A] and Beaming Face with Smiling Eyes 😁 are used more frequently by males of all age groups than by females. These findings suggest that the females in the corpus use more complex face emoji such as the Smiling Face with Three Hearts 🥰; but also, faces like the Zany Face 🤪 [U+1F92A], the Star-Struck Face 🤩 [U+1F929], and the Hugging Face 🤗 [U+1F917] appear among their most frequent emoji. In contrast, males use fewer faces but use the same ones more consistently, such as the smiling faces mentioned above and the Face with Tears of Joy 😂, which all place among the highest ranked emoji. Interestingly, in Group40+, the Face with Tears of Joy is used almost three times as often as the next most frequent face emoji. In short, in the present dataset it is the female users who dominate in the use of face emoji, in line with Chen et al. (2018).

Next, let us look at the use of hearts. As shown in Figure 4, hearts are more commonly used by females than by males in the investigated dataset, and they are positively correlated with age. However, the positive correlation of the use of the heart emoji with age is of comparable strength for the male subjects (0.16, p=0.56) and the female subjects (0.19, p=0.56). These findings do not support the results reported by Chen et al. (2018), who found that males dominated in the use of heart emoji. This is likely due to contextual and cultural factors, and it is also dependent on the differences between the platforms investigated (Google Play vs. Instagram).4 The differences in the use of hearts between males and females is statistically significant (p<0.001), as is the difference in the use of gestures (p=0.04). The gender difference in the use of faces (p=0.56), in contrast, is not statistically significant (see Figure 4).

Figure 4. Normalized data for age for heart, gesture, and face emoji according to individuals (top); normalized data for age for heart, gesture, and face emoji according to gender (females bottom left; males bottom right)

Lastly, let us consider the findings for gesture emoji. Gestures, such as the Folded Hands 🙏 [U+1F64F] and the Raising Hands 🙌 [U+1F64C], are very common for the males; the younger females, in contrast, use few gesture emoji. The Folded Hands 🙏, however, seem to be used by females of all age groups, and it is among the top 10 emoji for the females in Group30 and Group40+. Interestingly, as shown in Figure 4, the older females use similar amounts of gesture emoji as their male counterparts, whose use of gestures remains rather stable across all ages. The correlation between age and the use of gestures approaches significance for the females (0.34, p=0.09) but is very weak for the males (-0.03, p=0.9).

Typical “female” and “male” emoji that were found are the Sparkles ✨[U+2728], the Kiss Mark 💋 [U+1F48B], and the Raising Hands 🙌, where the use of the former two is dominated by females, and the latter by males. This finding ties in nicely with the finding that the males overall prefer the use of gesture emoji when compared to the females. No single type of emoji, however, seems to be statistically correlated to the users’ age in the dataset. Interestingly, though, the data suggests that, at least in the investigated corpus, gender differences are larger for the younger users and seem to level out but do not disappear completely for older users.

When considering the functions that emoji serve in their respective contexts, it quickly becomes clear that the category of complementation is by far the most common, with a share of 61.4% (4,096.6). However, this is not true for all age groups; thus, the age groups were investigated separately.

Figure 5 illustrates two notable findings in that respect: Firstly, the top of Figure 5 shows that of all emoji used by Group<19, 63.5% (1,616.7) have a complementary function, 27% (687.4) a reinforcing function, 9% (229.1) are used to substitute words, and only 0.3% (7.6) are used in a contradictory manner. The share of emoji with a reinforcing function increases continually across the age groups, while the share of complementary emoji decreases slightly. The use of substitutive emoji remains rather stable, and while 0.3% (7.6) of all emoji in Group<19 are contradictory to the text, Group40+ uses none with that function at all. The top of Figure 5 thus shows that while the dominance of emoji with complementary functions is rather strong in the youngest group, this dominance diminishes continually and gives rise to more emoji with reinforcing functions.

The bottom of Figure 5 shows the correlations of the two most common functions, complementation and reinforcement, with the age of each individual. The correlation of -0.41 (p=0.004) for age and complementation is relatively strong and statistically significant. The observed correlation of -0.20 (p=0.18) between age and the use of reinforcement, however, is not statistically significant.

Figure 5. Functions of emoji in relation to age (top: emoji functions in relation to overall emoji use by the specific group; bottom: correlation of complementation and reinforcement with the age of individuals as continuous variable)

With a correlation of -0.04 (p=0.76), the use of emoji as substitution remains rather stable across all age groups, and the use of emoji to contradict the accompanying text is negligible in all groups. Yet, the correlation of -0.37 (p=0.008) between age and the use of emoji in a contradictory manner is statistically significant. Regarding substitution, however, 45.4% (232.9) of all emoji used in this function come from people in Group<19, 31.7% (162,6) from people in Group20, 17.2% (88.2) from people in Group30, and only 5.7% (29.2) from people in Group40+.

Next, let us consider the sex-disaggregated data. As mentioned before, it is the females who use more emoji overall, and they also lead in the use of three of the four investigated emoji functions: Females use 61.5% (2,517.2) of all emoji with a complementary function, 60.2% (1,229.9) of all emoji with a reinforcing function, and 68.2% (15) of all emoji with a contradictory function. The gender difference in terms of emoji use with a complementary function is statistically significant (p=0.05), and the difference for reinforcement approaches statistical significance (p=0.09), as illustrated in Figure 6. Males take the lead solely with regard to the substitutive function at 55.4% (284.2). A more differentiated picture emerges when the genders are also disaggregated for age. Figure 7 illustrate these results.

Figure 6. Gender differences in the use of emoji with reinforcement and complementation functions

Figure 7. Functions of emoji in relation to age and gender for the female subjects on the left, and for the male subjects on the right (top: relative frequencies; bottom: correlation of complementation, reinforcement, and substitution with age)

While a steady decline in both functions of complementation and reinforcement is visible for the males, the same decline in the data for the females is interrupted by Group20, who use both functions more frequently than the younger females. The correlations are statistically significant between age and complementation (-0.41 for females (p=0.04); -0.48 for males (p=0.03)). For the females, the negative correlation of -0.47 (p=0.03) between age and the contradictory use of emoji is statistically significant. For Group<19, the males use proportionally more complementation, reinforcement, and substitution than the females in the same age group. In Group30, both males and females use emoji with similar functions. In contrast, the results for Group40+ show that the females use more complementation than reinforcement, while the males of the same age prefer the use of emoji with a reinforcing function. Thus, compared to the overall age-related tendencies, finer differences in the use of emoji functions emerge when the data is disaggregated.

The focus of the third analysis is on whether emoji are used by themselves without any accompanying text (stand-alone emoji), whether they are used in strings, or in compositions, and to what extent these uses are related to user age and gender.

First, we will investigate stand-alone emoji, which are those that are not accompanied by additional text. Stand-alone emoji are not particularly common overall. No correlation between age and the use of stand-alone emoji was detected, while a p-value of 0.09 indicates that the difference in use between males and females approaches statistical significance. Out of all the posts containing only emoji, most were made by the females of Group20 (20%; 19 instances). When the data is considered separately for the males and females, it emerges that the males in Group<19 and Group20 use more than twice as many stand-alone emoji as the males in Group30, and six times as many as the males in Group40+. For the females, the decline is less steady: Group<19 and Group30 both use the same numbers of stand-alone emoji (23.3% each, i.e., 14 instances), Group20 uses slightly more (31.7%, 19 instances), and Group40+ uses the fewest (21.7%; 13 instances). This indicates that females across all age groups use stand-alone emoji more consistently than males. Further results related to the functions of stand-alone emoji are reported below (see Analysis 4).

Next, the use of strings and compositions is investigated. Examples of strings are shown below:

1. H.B.: Sunshine ✨✨✨ [20-year-old female]

2. Ju.R.: Happy Father’s Day ♥️♥️♥️ [51-year-old female]

3. C.M.: Incredible dude 🔥🔥 [20-year-old male]

Overall, strings are less common than compositions. Again, the data needs to be disaggregated first for gender and then for age and gender for a complete picture to emerge. Only 3% (119) of the emoji used by females are strings, while 5.8% (155) of the emoji used by males are strings, showing that males slightly favor strings compared to females, even though this difference is not statistically significant (see Figure 8). In terms of age, a statistically significant (p=0.03) correlation of -0.36 indicates a preference by younger users for strings compared to older users.

Figure 8. Gender differences between the use of strings and compositions

As for compositions, the females use them more (15.7%; 627 instances) than the males (13.9%; 372 instances), with a statistically significant correlation of -0.46 (p=0.03). No such clear difference emerged in relation to strings, and no statistically significant results emerged for the male subjects overall with regard to strings. Figure 9 shows the disaggregated data. The following are examples of compositions from the dataset:

4. K.K.: I won’t complain 🤪❣️ [15-year-old female]

5. H.B.: Post-workout 🌿🌞 [20-year-old female]

6. S.C.: Having fun in a small town 🎥 🍿 🚘 [34-year-old female]

Figure 9: Emoji strings and compositions in relation to age and gender for the female subjects on the left and the male subjects on the right (top: relative frequencies of strings and compositions in relation to overall emoji use by females; bottom: correlations of strings and compositions with age).

Figure 9 visualizes the differences between the use of strings and compositions in the four age groups according to gender. Compositions are generally more common than strings in the dataset. In the youngest age group, the males use more strings than the females, but they use comparable amounts of compositions. Similarly, the males in Group30 use more strings than the females. In Group40+, both uses are rather uncommon, even though the use of compositions is still more frequent than the use of strings. Interestingly, no group uses more strings than compositions, even though the youngest males use the largest number of strings in the dataset.

The fourth analysis reinvestigates stand-alone emoji with a particular focus on the relation between the function of the stand-alone emoji and the pictures they accompany. This relation is particularly relevant for stand-alone emoji, as there is no additional text that the emoji might refer to. For this analysis, only stand-alone emoji were included, and each picture was investigated in terms of its color, shape, or represented action. For example, a palm tree emoji accompanying a picture showing a palm tree on a beach would be classified as reinforcing, but a sun emoji accompanying the same picture would be classified as complementary if the sun is not visible in the picture. Further, if the picture portrays clothes in a particular color and the accompanying heart emoji matches that color, the emoji is classified as reinforcing the image. Figures 10 and 11 serve as illustrations.

Figure 10. Stand-alone emoji: color reinforcement

Figure 11. Stand-alone emoji: object reinforcement

As noted above, while no connection was found between age and the total use of stand-alone emoji, the use of stand-alone emoji was indeed found to be related to user gender. When the stand-alone emoji are analyzed for their functions, the following results are obtained: Complementary stand-alone emoji have a correlation of 0.13 (p=0.47) with age, indicating that older individuals use stand-alone emoji with a complementation function rather more frequently than younger ones, even though this difference is not statistically significant.

Investigations of the sex-disaggregated data reveal a slightly weaker correlation of -0.20 (p=0.51) for the males in the dataset, which indicates that they use fewer stand-alone emoji with a complementation function than the females. For the other functions, no noteworthy differences were observed for the male sample in relation to age. Figure 12 shows the disaggregated data. Statistical tests reveal a correlation of 0.39 approaching significance (p=0.09) for the females and the use of complementation. The most intriguing finding that Figure 12 illustrates is that in Group<19 and Group20, the males use more reinforcement than in Group30 and Group40+; the females use more complementation in Group30 and Group40+ than Group<19 and Group20 – thus, the pattern of use seems to be reversed for males and females in that respect.

Figure 12. Functions of stand-alone emoji in relation to the picture related to age and gender for the female subjects on the left, and the male subjects on the right (top: relative frequencies in relation to overall emoji use; bottom: correlations of picture reinforcement and complementation with age)

Males use no stand-alone emoji with a complementation function in Group40+, while for the females of the same age, that is by far the most common function. Thus, gender as well as age-related differences are clearly visible. For the females in Group20, the dominance of stand-alone emoji with a reinforcing function is the strongest, thereby supporting the observation that these users choose their emoji to reflect something in the picture, such as the color. This is also the only group that employs stand-alone emoji with a contradictory function, setting them apart as particularly innovative and creative in their use of emoji overall.

As digital texts have continuously moved to the center of authorship analysis (e.g., MacLeod & Grant, 2012), the multimodal resources offered by digital communication are likely to become ever more important as markers of authorship (e.g., Heydon, 2019). The results of the present study can thus be of interest to researchers attempting to establish sociolinguistic profiles of anonymous authors. To illustrate a profiling task with a mock trial, consider the following randomly chosen example posts:

7. What am I thinking about? 🌹🥀 [2 complementation] @[name]

8. Happy earth day! 🌍 [reinforcement] 🌟 [complementation] just some amazing pics of Mother Nature and scenic locations✨ 😍 [2 complementation]

9. 😝 💋 [2 complementation stand-alone] ~ 📸 [substitution]: @[name]

10. If you know, you know😜 🤷🏽‍♀️💋✨ [4 complementation]

11. 📸 [substitution]: @[name] you truly outdid yourself 💋💋 [2 complementation]

12. Shadows ⚜️[complementation // reinforcing of picture] by @[name]❤️[complementation]

This hypothetical trial was done in a blind manner, which means that the individual identified for analysis was chosen by a colleague of the author of this paper and was not known to the author. The above posts were chosen randomly from this individual’s latest Instagram posts outside the original data collection period. If only such short texts are made available for the creation of a sociolinguistic profile, the brevity of the texts makes the task difficult, and it might be impossible to apply automated approaches for authorship analysis or identification (e.g., Eder, 2015). Thus, it can be useful to have some knowledge about how males and females use emoji, and how emoji use is related to age. In the above examples, an analysis based on the findings of the present study indicates the following:

Firstly, each of the randomly chosen posts contains at least one emoji. This can be indicative of a high overall use of emoji. Further, this person uses compositions, strings, and two stand-alone emoji, which are separated from the Camera with Flash using a tilde (~), as well as emoji with complementation, reinforcement, and substitution functions. This person uses emoji frequently (18 in six posts). Further, the posts exhibit a large variety of emoji (14 different ones in six posts), which are used in a variety of functions. As regards emoji types, the posts contain two face emoji, one heart emoji, and one gesture emoji. Importantly, both face emoji are complex face emoji (the Smiling Face with Heart Eyes and the Winking Face with Tongue 😜 [U+1F61C]). Both stand-alone emoji can be considered to serve a complementary function in this post by indicating the users’ feelings (fun, excitement, playfulness; Emojipedia, n.d.). Based on the results reported in this article, this particular use of emoji excludes an older male as a possible author, since this group is the only one to make no use of emoji with a complementation function at all. Further, two of the posts contain the Sparkles emoji, which this article has suggested is typical of females. Additionally, the Kiss Mark 💋, which is used three times by the anonymous author, is only found for the females in the dataset. Another strong indication that the anonymous author is female is the use of the Woman Shrugging 🤷‍♀️ [U+1F937, U+1f§FD, U+200D, U+2640, U+FE0F] rather than the Man Shrugging 🤷‍♂️ [U+1F937, U+200D, U+2642, U+FE0F].5 Moreover, no typically male emoji, such as the Raising Hands, are used in the investigated posts. Taken together, based on the results presented in this article, these findings provide evidence for the author of the posts being a young female. This turns out to be the case: the author of the posts is a 15-year-old female.

This simplified example suggests that the incorporation of emoji into the tool kit of forensic linguists who work with digital data can be very useful, although emoji should not be the sole indicators of either age or gender. Further studies are needed to confirm (or challenge) the findings of the present study for them to become useful in practical terms. In addition, computational authorship analysis methods (see, e.g., Daelemans et al., 2019; Koppel et al., 2008) would be useful to confirm whether the correct finding in this short case example is based on chance or whether this study’s findings are robust and therefore useful in forensic linguistic casework.

The first analysis in this article shows that it is worth investigating both emoji functions and emoji types. The first research question addressed the types of emoji and how they are related to both age and gender. Typical male emoji that were identified are gestures and, especially, the Raising Hands. Females, in contrast, make use of the Sparkles and the Kiss Mark. Additionally, the females use a larger variety of hearts and faces and, especially, more complex faces (see also Chen et al., 2018). Jones et al. (2020, p. 1) report that females “are better able to detect facial emotion” than men, and that “the smiley emojis may convey greater intensity of emotion for women than for men.” This could explain why women use more, and more complex, face emoji than men. The greater use of emoji and variety of emoji by younger users might be explained by a study conducted by Zilka (2021, p.17), who found that “children, adolescents, and young adults […] feel that there is a strong connection between emoji and their emotions.” Further, as Herring and Dainas (2020) pointed out, older users tend to interpret emoji as “virtual actions” (p. 22) rather than as emotions. These findings provide an interesting basis for future research, and they could be of great importance to digital authorship analysis.

Analysis 2, which aimed at answering the second research question, investigated emoji functions in relation to age and gender, and highlighted complementation as the most common function across all groups. This is hardly surprising, as this category subsumes many different functions. A further classification of complementation into subcategories could reveal more fine-grained differences as regards age and gender. Nevertheless, a strong correlation was found for age, meaning that the older individuals used fewer emoji with a complementation function. In contrast, this trend is reversed for the complementation function of stand-alone emoji for the older females in Group30 and Group40+. That older individuals use more emoji with a reinforcing function lends support to the argument that older users are less creative and more conservative in their use of emoji (e.g., Weiß et al., 2020). The same might be true for the genders: The greater use of emoji with a complementation function by females might reflect their openness to, and quicker adoption of, linguistic innovations (e.g., Eckert & McConnell-Ginet, 2003), which emoji clearly constitute. Further, considering that complementation includes the use of emoji with mitigating and hedging functions (i.e., linguistic politeness), the females’ greater use of emoji with complementation functions might be reflective of their greater use of politeness strategies in general (e.g., Lakoff, 1957; O’Barr, 1982).

The third research question aimed at detecting relations between stand-alone emoji, strings, and compositions and both gender and age. In Analysis 3 it was shown that compositions are generally more frequent than strings. Strings, however, are more strongly related to age, and it is particularly female users who employ emoji strings, except for the youngest males, who use more of them than their female counterparts in the same age group. It has been argued that strings serve both as emphasis by themselves and also enforce the message they accompany (Schneebeli, 2017). Strings are therefore more intense expressions of emotions than are compositions. Thus, the higher use of strings by females than by males is in line with psychological research that shows that “there is increasing evidence that […] females express a variety of emotions more intensely than do males, both verbally and through nonverbal facial expressions” (Brody, 1993, p. 87) – research that also strengthens this study’s findings regarding the high use of face emoji by females.

Compositions, in contrast, are interesting in that each individual emoji in a composition has the potential to modify the meaning of the other emoji in that composition. Thus, the meaning of a composition is larger than the meaning of each emoji by itself. Compositions also do not emphasize the message in the same way that strings do: Compositions by themselves without accompanying text can tell a whole story, while strings can only emphasize and intensify one thought or idea. Thus, compositions are more versatile, which is most likely why they are encountered more frequently than strings. The use of compositions is most strongly related to age in the dataset, but also shows an influence of gender.

Lastly, the fourth research question addressed the use of stand-alone emoji in relation to the picture they accompany. Younger users seem to be more creative and selective in their use of emoji. For them, the use of emoji in general is likely to feel more natural, since many have grown up with them unlike the older users, for whom emoji might be like a new language. Importantly, though, as the interrater reliability of slightly more than 70% shows, much more research is needed for a clearer and thus more widely useful classification system for relations between emoji and pictures to emerge. An analysis of “true” stand-alone emoji on platforms that allow the posting of just emoji without either text or pictures might reveal further interesting correlations in terms of gender and age. This is another promising avenue for future research.

Overall, it seems that younger people select emoji more thoughtfully to complement or reinforce not only the text but also the image the emoji accompanies. For example, the variety of hearts the females use across all age groups might be explained by the fact that the hearts are sometimes chosen to reflect the color in the accompanying picture: Pictures showing the ocean often come with blue hearts, while black and white pictures often come with black and/or white hearts. Whether this finding also holds true for emoji that are not used by themselves but in connection with text needs to be addressed in future studies.

An interesting development to mention, although it was not very frequent in the dataset, is the use of descriptive phrases to substitute for emoji. In the dataset, it was noticed that two females from Group<19 replaced the Smiling Face with Heart Eyes 😍 with the phrase “heart eyes.” This use is reflected in the title of this article, which is a quote from one of the users. They were clearly using this phrase in place of the respective emoji, which intriguingly shows a reversed form of substitution as addressed in this article. This could be an interesting emergent phenomenon worth tracking, as it could provide insight into new uses of emoji and their integration into youth cultures.

The present study has certain limitations. Firstly, even though some results are statistically significant, they could be context- or culture-dependent, as only data collected from Instagram and English-speaking US-Americans was analyzed. Similar studies need to be conducted with data from other social media platforms, such as Facebook and Twitter, but also from messaging services such as WhatsApp, Telegram, Signal, and the like. Through such studies, the findings in this article may become more robust and generalizable, and thus useful for practical applications such as author profiling. This area of research would also profit from similar studies conducted with users of different cultural backgrounds and speakers of different languages.

Another limitation is that only 60 individuals were included in the study; future research should analyze a larger sample. However, since no automated taggers yet exist that can annotate corpora of emoji according to the functions they serve in their respective contexts, such an endeavor is highly labor-intensive at present. Lastly, the data in the corpus include only a few individuals older than 40 years of age. Research on emoji use would benefit from the inclusion of more individuals in that age category. That said, the present corpus does reflect the demographics of typical Instagram users at the time of data collection.

Taking these limitations into account, we cannot infer general patterns of emoji use for a larger population of Instagram users outside the investigated group of individuals. Thus, the results of the authorship profiling analysis should be interpreted with care. In order to limit the possibility of chance identifications, more such profiling analyses need to be conducted in the future.

This study makes original contributions in three main ways: Firstly, it breaks down emoji use systematically by age and gender to an extent unparalleled in previous studies. Secondly, it is among the first to investigate the use of emoji and emoji functions in the context of forensic authorship analysis. Thirdly, it is the first to investigate the relationship between the use of emoji and the pictures they accompany. These areas offer rich avenues for future research and raise new questions that need to be addressed.

The data not only suggest that the younger users choose emoji more carefully to fit their images, but that older users also employ emoji in a variety of functions; however, these functions differ somewhat from the functions employed by the younger users. Further, gender-related differences emerged, some of which level out with users’ increasing age yet do not disappear completely. Even where it was not possible to demonstrate group-specific trends, it is still possible to discern individual preferences for particular emoji and their placement, which could be valuable for forensic linguists in authorship comparison tasks (Marko, 2020). Although they need to be replicated, these findings can form the starting point for future, more practice-oriented forensic linguistic approaches that integrate emoji into their analyses.

  1. Numbers in square brackets refer to the codes provided by the UNICODE Consortium, which tries to standardize the appearance of emoji, but appearances still differ across platforms (Evans, 2017). Emojipedia (n.d.) now notifies users if the appearance of a particular emoji differs grossly, such that misunderstandings could arise, and warns them to “use [this emoji] with caution.”

  2. Stand-alone emoji, i.e., emoji without accompanying text, were excluded from the first two analyses, as these emoji do not serve the same functions as those that are used in conjunction with text. Stand-alone emoji are the focus of analyses 3 and 4.

  3. This difference only approaches statistical significance (p=0.06).

  4. Chen et al. (2018) focused in particular on Android users who adopted the Kika Keyboard for Google Play.

  5. It is beyond the scope of this article to discuss how the dataset indicates that female authors tend to make use of female emoji if available. However, this raises interesting questions about emoji use in terms of self-presentation and identity (cf. Ge, 2019).

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Karoline Marko [karoline.marko@uni-graz.at] is a postdoctoral researcher at the University of Graz, Austria. Her main research interests are in the area of forensic linguistics, including discourse analysis and authorship analysis, particularly in digital contexts.

The author would like to thank the anonymous reviewers and Susan Herring for their helpful comments and input.


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