Age often shows up as a significant factor in processing and perceiving messages, especially in electronic communication. Depending on the degree of formality and conciseness, teenagers, young adults, and full adults interpret electronic communication slightly differently. Age differences were examined in line with perception to tone in text messages with or without emoji and emoticon, with the control being plain, grammatically correct simple sentences. The study included participants assigning a score from a range (1 for negative through 6 for positive) to a set of screenshots depicting neutral messages and curated ones. The results of experiment 1were replicated in experiment 2 with ANOVA. Age significantly affected negative/positive reception by respondents. Adults tended to feel more negative towards abbreviated messages and those with emoji/emoticon in text. The findings suggest that emoji, emoticons, and abbreviations on messaging platforms, which is fairly commonplace, receive skewed interpretations across ages.
Age as a Factor in Perception of Tone in Text Messages
Frequently, people find themselves using non-standard language construction and support in interpersonal communications, especially with text messages. With the advent of evolved rich-media texting platforms, this can include emoji, emoticons, well-known abbreviations, or out-right concatenations. That is primarily attributed to the little attention given to formality as opposed to institutional writings such as memos and emails. Recent research has noted the likelihood of these deviations carrying a mixed impact on the receiver, based on their age (Aldunate et al., 2018; Harari et al., 2020; Riordan, 2017). The same has also been suggested to be significantly impactful in select contexts, including adding significant flavor to communication in romantic relationships and defining the interest and creativity of communicators in youthful populations (Rodriguez et al., 2017; Kuerbis et al., 2017). However, significant variations in effect are anticipated in ‘flavoured’ communication between individuals with substantial age differences.
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Method
The research utilizes primary data from an online survey. For this study, student researchers shared a Google Forms link to people outside their class. Participants include anyone who accessed and filled out the form. A total of 249 participants completed and submitted their forms with valid responses. The research was voluntary, and all participants were required to have phones with platforms that support emoji texting. Snowballing was also applied – whereby willing participants were asked to re-share the link with their contacts within their convenience.
Materials
The study utilized Google Forms, the software utility that enables participants to fill in a questionnaire. Data collected was then organized in Microsoft Excel Sheets, from where researchers organized the data into tables. JASP was used for statistical analysis, whereby the sets of three prompts were clustered and paralleled against the three options – plain text, emoji, and emoticon. JASP was used to obtain Post Hoc Tests on responses across the three prompts to obtain the mean difference and distribution metrics. A Microsoft Excel utility was used to draw a simple line plot of the three overlapping sets of prompts on an aggregated scale of 3 to 5.5. Finally, ANOVA was used to perform repeated Measures to obtain the sum of squares, mean squares for p and F values. No physical equipment or materials were used for the study.
Research design
This study informs broader research by Students at Hunter University Department of Sociology investigating the relationship between the form of a text message and its perception by the recipient. The study utilizes a survey questionnaire with 18 screenshots of text messages. The messages are essentially three messages in six different flavors – plain, plain + emoticon, abbreviated, with emoticon, emoji, and abbreviated + emoji. For each message prompt, the user is required to indicate their perception of tone as a metric between 1 and 6, "1" being negative, and "6" being positive. The independent variable is the text message, while the independent variable is the perceived tone. Among other attributes, participants were asked to indicate their age, which is of interest in this discussion. This study is based on the data collected from the survey.
Procedure
We gathered materials necessary for the survey – the Google Forms questionnaire – and JASP for statistical analysis. We then shared the link to the form on diverse online platforms. The platforms included Facebook communities, online campus forums, and Instagram groups. Researchers targeted 120 people and encouraged primary respondents to re-share the link among their circles. Initially, only participants above consent age were targeted, but responses included minors too. 249 people in total responded to the survey. We gave the survey a window of three days, after which we revoked the link and analyzed the data with JASP and repeated measures with ANOVA.
Results
Here are the summarized aggregate responses
Table 1
|
Age Bracket (Average) |
a | b | c | d | e | f |
|
18 to 35 (22.3) |
4.6875 | 5.357895 | 4.822917 | 4.114583 | 5.210526 | 4.842105 |
|
Over 35 (55.6) |
4.695652 | 5.26087 | 4.869565 | 4.608696 | 5.086957 | 4.727273 |
Table 2
|
Age Bracket (Average) |
g | h | i | j | k | l |
|
18 to 35 (22.3) |
3.452632 | 4.59375 | 3.927083 | 3.178947 | 4.255319 | 3.905263 |
|
Over 35 (55.6) |
3.565217 | 4.434783 | 3.695652 | 3.695652 | 4.636364 | 4.304348 |
Table 3
|
Age Bracket (Average) |
m | n | o | p | q | r |
|
18 to 35 (22.3) |
2.810526 | 4.648936 | 3.93617 | 2.8125 | 4.34375 | 3.739583 |
|
Over 35 (55.6) |
3.086957 | 4.565217 | 3.869565 | 3.217391 | 4.826087 | 3.73913 |
Figure 4
Key:
| Letter | Text | Letter | Text |
| A | Hi, how are you? | G | Where are you? |
| B | Hi, how are you? : ) | H | Where are you? : ) |
| C | Hi, how are you? 🙂 | I | Where are you? 🙂 |
| D | hi how r u | J | Where r u |
| E | hi how r u : ) | K | where r u : ) |
| F | Hi how r u 🙂 | L | where r u 🙂 |
| Letter | Text |
| M | Respond to me as soon as possible |
| N | Respond to me as soon as possible : ) |
| O | Respond to me as soon as possible 🙂 |
| P | Respond to me asap |
| Q | respond to me asap : ) |
| R | respond to me asap 🙂 |
Figure 5
The first JASP test yielded a significant low variance between age and grammar. For instance, the ‘a’ sample (“Hi, how are you?”) had a p-value of 0.308 and an f value of 1.182, compared to gender, with p = 0.491 and f = 1.625. The relationship between emoji and age is equally strong, noting that the cumulative first test yielded p = 0.061 and f = 2.825.
Figure 6
Post Hoc comparisons from JASP further revealed close ties dependence of tone perception with age. However, emoticons compared more favorably with plain text than emoji. For instance, at an absolute mean difference of 0.174, the t value for emoticons perception (0.47) was much lower than that of emoji (2.691), relative to a value of 0.371 for plain text.
Figure 7
Discussion
Generally, average scores for plain text messages improve (tend to the positive) as age gradually increases. Sampling for the control, “Hi, how are you?”, young adults (18 to 30) posted 4.5463, ages 30 to 49 posted 4.85714. Those over 50 posted 5.206 on average. On the other hand, “hi how r u 🙂 ” (with a smiley) recorded 4.66 for young adults, 4.4 for ages 30 to 49, and 4.0 for age 50 and above. Thus, people below 35 found text messages with smileys and emoticons politer and ‘positive,’ while adults averagely found them rather tasteless. Clearly, the trend is relatively consistent with the other two sets, which indicates that as ages increase, the appeal for emoji, emoticon, and abbreviations depreciate.
From this research, it is clear that teens and younger adults are not only admissive of texting with emoji, emoticons, and abbreviated words, but they happen to be accustomed to it. This observation is validated by the following trend: We know that formality (grammatical correctness and no emoji use) declines from left to right (a to f, g to l, and m to r). Note that aggregate scores gradually increase with reducing formality for youth (18 to 35). However, the trend is in reverse for adults. For instance, in table 3 (save for m), the average score falls from 4.5 to 3.8, and finally to 3.7. The reasoning is the same – as formality of the texts decline, adults find the tone increasingly negative. The pattern is consistent with Kingsbury & Coplan’s (2016) hypothesis that emoji and emoticon-laden text messages escalate anxiety and ubiquity in interpretation with older generations, prompting negative ratings.
Assuming the null hypothesis is true, the one-way ANOVA f-value obtained in our study’s second trial indicates a strong dependence of the dependent variable on age. Ideally, both the case of emoji and emoticon (f = 1.625 and f = 2.825) fall within the third quarter of the sampled distribution. On top of the self-explanatory averaged values, the f-values demonstrate the correlation between age and message preferences. Comparing the sum of squares between and within groups further showcases a diminishing variance for the data (with Age). With the Grand Mean at 76.169 at p = 0.008 for Between groups, a slight change in the dataset (age) would largely reduce the f-value. The other set (Figure 6) replicates the relationship with adjusted sums of squares instead. The interpretation is that grammatical correctness affects the mean responses just as much as emoji, with the two varying independently against age (1 and 2 degrees of freedom respectively). Thus, we garner that people’s ages strongly dictate their liking of text messages with emoji, emoticon and abbreviations. From the df and p values in tables 5 and 6, abbreviations are the most sensitive, followed by emoji, and then emoticons.
The fascination of young adults with emoji and unconventional wording syntax stretches beyond messaging preferences. Judging from the ubiquity of emoji imprints on t-shirts, cell phone stickers, and even shoes, it suffices to say people in lower age brackets have stronger devotion, as hinted by Völkel et al., (2019). Far from genuine reservations and individual conservativeness, adults’ biases in their message preferences could be partly informed by the desire not to identify with such arguably ‘attention-seeking’ behavior.
Conclusion
The study findings approve the hypothesis that message preferences are strongly affected by the recipient’s age. Nevertheless, that is not absolute, as slight inconsistencies in the raw data and plots illustrate occasional misalignments, often attributed to personal reasons or flaws in data collection. While this study considerably draws meaningful relations between age and message preferences, it does not probe into the reason behind the observations. That is a potential area of research that social scientists could venture into. Reason being, the subject is crucial to digital communication providers, who are interested in knowing the fate of emoji and emoticon perception as the current youthful population ages. I recommend this study to be replicated with distributed numbers of participants in terms of age. Clearly the patterns in this research are inconsistent because ages were clustered around youth (mean = 28.75 years). As a matter of fact, this study cautions people to be cautious when using abbreviated language, emoji and emoticons in texting, because adults might find them less fancy than youth and teens.
References
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Harari, G. M., Müller, S. R., Stachl, C., Wang, R., Wang, W., Bühner, M., Rentfrow, P. J., Campbell, A. T., & Gosling, S. D. (2020). Sensing Sociability: Individual Differences in Young Adults’ Conversation, Calling, Texting, And App Use Behaviors in Daily Life . Journal of Personality and Social Psychology , 119(1), 204–228. Https://Doi-Org.Proxy.Wexler.Hunter.Cuny.Edu/10.1037/Pspp0000245.Supp (Supplemental)
Kingsbury, M., & Coplan, R. (2016). RU mad @ me? Social anxiety and interpretation of ambiguous text messages. Computers in Human Behavior, 54, 368–379. https://doi.org/10.1016/j.chb.2015.08.032
Kuerbis, A., Van Stolk-Cooke, K., & Muench, F. (2017). An Exploratory Study of Mobile Messaging Preferences by Age: Middle-Aged and Older Adults Compared to Younger Adults . Journal of Rehabilitation and Assistive Technologies Engineering , 4, 2055668317733257.
Riordan, M. A. (2017). The Communicative Role of Non-Face Emojis: Affect and Disambiguation. Computers in Human Behavior, 76, 75-86.
Rodrigues, D., Lopes, D., Prada, M., Thompson, D., & Garrido, M. V. (2017). A Frown Emoji Can Be Worth a Thousand Words: Perceptions of Emoji Use in Text Messages Exchanged Between Romantic Partners. Telematics and Informatics, 34(8), 1532-1543.
Völkel, S. T., Buschek, D., Pranjic, J., & Hussmann, H. (2019, October). Understanding Emoji Interpretation Through User Personality and Message Context. In Proceedings of The 21st International Conference On Human-Computer Interaction with Mobile Devices and Services (Pp. 1-12).
