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How Emojis Expressions Changing the Face of Non Verbal Communication? Top Tips to Use Innovately!!

Abstract:

Emoticons’ first popped on the scene in the early 1980s, as a shortcut for people to express their emotions. Fast forward a decade, when its successor, the ‘Emoji’, popped up in Japan and gained widespread popularity with the advent of smartphones and instant messaging applications. The term “emoji” itself combines the Japanese words “e” (meaning “picture”) and “moji” (meaning “character”).

Keywords

“e” (meaning “picture”),  “moji” (meaning “character”), Emotions, Popular, Non verbal Communication, Skills, Expertise 

Learning Outcomes 

After undergoing this article you will be able to understand the following

1. What's exactly an Emoji?

2. Why emojis are important?

3. What's the historical significance of emojis?

4. What's the types of emojis?

5. How emojis are created?

6. How emojis carry meaning?

7. How to use emojis ?

8. Where emojis can perfectly fit?

9. What's the advantages of emojis?

10. What's the disadvantages of emojis?

11. Conclusions

12. FAQs

References 


1. What's exactly an Emoji?

An emoji is a pictogram, logogram, ideogram, or smiley embedded in text and used in electronic messages and web pages. The primary function of modern emoji is to fill in emotional cues otherwise missing from typed conversation as well as to replace words as part of a logographic system.

2. Why emojis are important?

#1 They can help you convey your message in the correct tone and with the appropriate emotion

The biggest drawback of texting is that while it is easy to be blunt and say whatever you want to say, it is very difficult to communicate in a way that is clean and makes your intentions or feelings clear. 

The tone in which the text is received depends completely on the experience and opinion of the recipient rather than the intentions and feelings of the one sending the message. Emojis can help the sender convey the message in the correct tone.

Emojis can be used to express emotions like happiness, sadness, excitement, suspicion, and dread, etc. This reduces the chances of any miscommunication and misunderstanding as the recipient would be able to understand what the sender intends or feels.

#2 They can make the conversation more intimate and personal

It has been found that the use of emojis can make the conversation feel more personal as emojis induce the same responses and feelings in the brain as facial expressions do. 

Emojis make people feel more comfortable as if the conversation is taking place face-to-face instead of over the phone. Texts without emojis seem more serious and formal, eliminating the element of comfort. 

Moreover, the use of emojis is equated with a deeper understanding of each other as it allows a person to better understand the other by presenting a clear and precise picture of emotions or intentions. 

#3 Emojis can help people who have issues with verbal communication express themselves with clarity

The journey of emojis from introduction to their use today is very vast. In today’s time, there are so many emojis available for use and they cover more than just emotions. There are emojis for numbers, directions, objects, food, and flowers, etc.

It is as if emojis denote an entire language system that can be used to communicate ideas and express emotions. People who are incapable of sending and reading texts can talk to people over the phone by using emojis. 

This has widened the user base and included even those people who have not had the opportunity to get an education.

#4 Emojis help convey a critical or negative message in more acceptable manner

You have probably found yourself in a situation where you have a comment to make or something to say to someone that might come off a little rude when composed as a text. 

The best way to tone down the seriousness of the impact of the negative/critical comment being offered is to use an emoji at the end – an appropriate one.

It can help you say what you want to say in a manner that is more acceptable and will not hurt the other person’s emotions. 

While some might say this distorts reality, that is not true.

The message being conveyed is the same, only the intensity with which it is being expressed changes. 

#5 Emojis are extremely engaging and help grab people’s interest

I am sure you would agree that text messages are a lot more engaging if they contain some emojis. Emojis help build interest as the speculation over what the emoji is used for encourages the person to go through the entire text. 

Moreover, it has been proven through scientific studies that people tend to pay more attention to pictures than words and tend to retain them for a lot longer. 

A conversation is only successful when people learn from it and retain something of value, which is exactly what the use of emojis helps with.

3. What's the historical significance of emojis?

The modern-day emoji can be loosely traced back to chatrooms in the 1990s, when primitive emojis were used in conversations, like : ) to signal a smile or ; ) to punctuate a joke or sarcastic jab. But designer Shigetaka Kurita is considered to be the founding father of today's emojis.

An Overview of The Importance of Emojis

There is no denying the importance of emojis, they have changed the way we communicate and added more meaning to the words exchanged. 

They have allowed for the expression of emotions and intentions over feelings over text, preventing any form of miscommunication and misunderstanding. This has improved the quality of conversations and made them more effective.

One of the significant impacts of emojis on online communication is their role as visual cues. Emojis provide non-verbal context and tone to text messages, reducing the chances of misinterpretation. They have made their way into text messages and all forms of online communication.

4. What's the types of emojis?

Types of emojis are classified according to expression such as

5. How emojis are created?

An organization called the Unicode consortium oversees the creation of all new emojis. The group is made up mostly of technology companies such as Apple and Google. Each year, Unicode reviews proposals for new emojis and decides which should be added to our devices.

People think emojis just pop into existencebut a lot of effort is going on behind the scenes to make sure they are as useful as possible for people across the world,” says Keith BroniHe works for Emojipediaan online encyclopedia of emojis and their meanings.

But the ideas for emojis don’t have to come from tech wizards at big companiesAnyone can submit oneincluding you

There’s nothing stopping a [kidfrom putting forward a proposal,” Broni explains. “There’s no age barrier whatsoeverIt’s all about how good the idea is.”

Stillnew emojis must meet several criteriaor standards

The do’s & don’ts below will help you come up with a winning design. 

Think globally.

Emojis are an important form of communication across the globeSo any new symbols should be as easy to understand for people in the U.Sas for those in Japan.  

Avoid getting too specific.

There’s a reason you don’t see official emojis of Abraham LincolnHogwartsor the Nike swooshUnicode will reject proposals for emojis of real peoplefictional charactersand specific buildingseven ones from books or moviesBrands and company logos are also out

Do your homework.

There are already more than 3,600 emojisso yours has to be uniqueAlsomany concepts may already be represented by one or more emojisThere’s no need for a handwashing emojifor examplebecause that can be represented by the emojis for water dropletssoapand hands

Design for diversity.

Think about people and cultures that aren’t represented by emojisIn recent yearsUnicode has worked to make new symbols more inclusive.

6. How emojis carry meaning?

The significance of having a variety of emojis lies in their ability to enhance communication by adding emotional nuance and context to text-based messages. Emojis allow users to convey feelings, expressions, and ideas in a more visually engaging way, fostering better understanding in digital communication.


7. How to use emojis ?

Add emoji as you type

You can quickly replace text with emoji suggestions as you type.

  1. Enter a commonly used word or phrase like “heart” or “thumbs up”, then press Fn-E or -E.

  2. Press Return to replace the text with the suggested emoji, or choose another suggestion.

8. Where emojis can perfectly fit?

Emoji and chatbots are a match made in heaven! One picture is worth ten thousand words — this is also the case for emoji, smileys or emoticons since you can show your intent, mood, feelings or interests with just one character. 

These small pictograms offer a universal simplicity in communication and hence improved user experience when used correctly.

Identify key emojis to match your brand.

Don’t: Use multiple emojis in a row.

Do: Put any important information BEFORE any emojis.

Don’t: Use emojis when talking about serious topics.

Do: Show off your brand’s personality.

Don’t: Force it.

Do: Use emojis to emphasize your point.

Don’t: Use emojis just because everyone else is.

Do: Take advantage of emojis for real-time engagement.

Do: Use emojis to visually appeal to your audience.

Don’t: Use emojis that cross the line and could be viewed as threatening or violent.

Do: Represent the diversity in your team and audience.

9. What's the advantages of emojis?

Emojis can help you communicate more effectively, capture attention, convey tone and emotion, make your messages more memorable, and increase engagement with your audience.

Facilitating the expression of emotions: Emojis can help convey the tone and sentiment behind a message, making it easier for the reader to understand the intent behind the message. Enhancing engagement: Using emojis can make a message more visually appealing and engaging, encouraging the reader to respond.

Advantages of using emojis in online conversations include:

  1. Facilitating the expression of emotions: Emojis can help convey the tone and sentiment behind a message, making it easier for the reader to understand the intent behind the message.
  2. Enhancing engagement: Using emojis can make a message more visually appealing and engaging, encouraging the reader to respond.
  3. Breaking down language barriers: Emojis can be used to communicate across language barriers, as they are often recognized and understood internationally.
  4. Adding personality: Emojis can be used to add personality and uniqueness to a message, making it more memorable and relatable.

10. What's the disadvantages of emojis?

Disadvantages of using emojis in online conversations include:

  1. Misinterpretation: Emojis can be interpreted differently by different people, leading to confusion or misunderstandings.
  2. Lack of professionalism: Overuse of emojis in professional contexts may be viewed as unprofessional or immature.
  3. Inability to convey complex ideas: Emojis are limited in their ability to convey complex ideas or detailed information.
  4. Dependence on technology: The use of emojis requires the availability of the technology to support them, which may not always be the case.
  5. Age-related issues: Some people may be less familiar with the use of emojis, particularly older generations, which can lead to confusion or difficulty in understanding.


11. Conclusions

Based on their emotional distribution, the classification of emojis are into positive, neutral and negative and found that most emojis express positive emotions

Similar studies have found that users tend to use more emoji more in positive messages than negative messages.

Secondly, emojis should not be seen as a replacement for thoughtful and well-crafted messages. They should complement the text and add value to the overall message. PR pros must strike a balance between effectively using emojis and maintaining the professionalism of their clients’ brand image – a fine line that many brands may struggle with.


12. FAQs

Q. What was the point of emoji?

Ans.: 

emojis help to display facial expressions, tone of voice, and human gestures in digital communication. In addition to expressing emotions, emoticons are also used to convey meaning in communication. Hence, their use can add contextual or additional emotional meaning to communication.


References 

Al Rashdi, F. (2018). Functions of emojis in WhatsApp interaction among Omanis. Discourse Context Media 26, 117–126. doi: 10.1016/j.dcm.2018.07.001

CrossRef Full Text | Google Scholar

Al-Azani, S., El-Alfy, E.-S. M., and IEEE. (2018). “Combining emojis with Arabic textual features for sentiment classification,” in Paper Presented at the 2018 9th International Conference on Information and Communication Systems (ICICS) (Irbid). doi: 10.1109/IACS.2018.8355456

CrossRef Full Text | Google Scholar

Albawardi, A. (2018). The translingual digital practices of Saudi females on WhatsApp. Discourse Context Media 25, 68–77. doi: 10.1016/j.dcm.2018.03.009

CrossRef Full Text | Google Scholar

Aldunate, N., and Gonzálezibáñez, R. (2016). An integrated review of emoticons in computer-mediated communication. Front. Psychol. 7:2061. doi: 10.3389/fpsyg.2016.02061

PubMed Abstract | CrossRef Full Text | Google Scholar

Alshenqeeti, H. (2016). Are emojis creating a new or old visual language for new generations? A socio-semiotic study. Adv. Lang. Lit. Stud. 7, 56–69. doi: 10.7575/aiac.alls.v.7n.6p.56

CrossRef Full Text | Google Scholar

Aoki, S., and Uchida, O. (2011). “A method for automatically generating the emotional vectors of emoticons using weblog articles” in Paper Presented at the WSEAS International Conference on Applied Computer and Applied Computational Science (ACACOS'11) 10th (Venice).

Google Scholar

Archer, D., and Akert, R. M. (1977). Words and everything else: verbal and nonverbal cues in social interpretation. J. Pers. Soc. Psychol. 35, 443–449. doi: 10.1037/0022-3514.35.6.443

CrossRef Full Text | Google Scholar

Aull, B. (2019). A study of phatic emoji use in WhatsApp communication. Internet Pragmat. doi: 10.1075/ip.00029.aul

CrossRef Full Text | Google Scholar

Ayvaz, S., and Shiha, M. O. (2017). The effects of emoji in sentiment analysis. Int. J. Comput. Electr. Eng. 9, 360–369. doi: 10.17706/IJCEE.2017.9.1.360-369

CrossRef Full Text | Google Scholar

Balas, B., Kanwisher, N., and Saxe, R. (2012). Thin-slice perception develops slowly. J. Exp. Child Psychol. 112, 257–264. doi: 10.1016/j.jecp.2012.01.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Barbieri, F., Espinosa-Anke, L., and Saggion, H. (2016a). Revealing patterns of Twitter emoji usage in Barcelona and Madrid. Artif. Intell. Res. Dev. 288, 239–244. 10.3233/978-1-61499-696-5-239

Google Scholar

Barbieri, F., Kruszewski, G., Ronzano, F., and Saggion, H. (2016b). “How cosmopolitan are emojis?: exploring emojis usage and meaning over different languages with distributional semantics” in Paper Presented at the 2016 ACM on Multimedia Conference. doi: 10.1145/2964284.2967278

CrossRef Full Text | Google Scholar

Barbieri, F., Ronzano, F., and Saggion, H. (2016c). “What does this emoji mean? A vector space skip-gram model for Twitter emojis,” in Paper presented at the International Conference on Language Resources and Evaluation, LERC.

Google Scholar

Barbieri, F., Saggion, H., and Ronzano, F. (2014). “Modelling sarcasm in twitter, a novel approach,” in Paper Presented at the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. doi: 10.3115/v1/W14-2609

CrossRef Full Text | Google Scholar

Berengueres, J., and Castro, D. (2017). “Differences in emoji sentiment perception between readers and writers,” in Paper Presented at the 2017 IEEE International Conference on Big Data (Boston, MA). doi: 10.1109/BigData.2017.8258461

CrossRef Full Text | Google Scholar

Bich-Carriere, L. (2019). Say it with a smiling face with smiling eyes: judicial use and legal challenges with emoji interpretation in Canada. Int. J. Semiotics Law 32, 283–319. doi: 10.1007/s11196-018-9594-5

CrossRef Full Text | Google Scholar

Boothe, D., and Wickstrom, C. (2017). “Esol learners must confront diverging language pathways between social media and english for specific purposes,” in Paper Presented at the 10th International Conference of Education, Research and Innovation (Beijing). doi: 10.21125/iceri.2017.0714

CrossRef Full Text | Google Scholar

Brody, N., and Caldwell, L. (2019). Cues filtered in, cues filtered out, cues cute, and cues grotesque: teaching mediated communication with emoji pictionary. Commun. Teach. 33, 127–131. doi: 10.1080/17404622.2017.1401730

CrossRef Full Text | Google Scholar

Butterworth, S. E., Giuliano, T. A., White, J., Cantu, L., and Fraser, K. C. (2019). Sender gender influences emoji interpretation in text messages. Front. Psychol. 10:784. doi: 10.3389/fpsyg.2019.00784

PubMed Abstract | CrossRef Full Text | Google Scholar

Cahyaningtyas, R. M., Kusumaningrum, R., Sutikno, S, Riyanto, D. E., and IEEE. (2017). “Emotion detection of tweets in Indonesian language using LDA and expression symbol conversion,” in Paper Presented at the 2017 1st International Conference on Informatics and Computational Sciences (ICICoS) (Semarang). doi: 10.1109/ICICOS.2017.8276371

CrossRef Full Text | Google Scholar

Cappallo, S., Mensink, T., Snoek, C. G. M., and ACM. (2015). “Query-by-emoji video search,” in Paper Presented at the the 2015 ACM Multimedia Conference. doi: 10.1145/2733373.2807961

CrossRef Full Text | Google Scholar

Cappallo, S., Svetlichnaya, S., Garrigues, P., Mensink, T., and Snoek, C. G. M. (2019). New modality: emoji challenges in prediction, anticipation, and retrieval. IEEE Trans. Multimedia 21, 402–415. doi: 10.1109/TMM.2018.2862363

CrossRef Full Text | Google Scholar

Carvalho, P., Sarmento, L., Silva, M. J., and De Oliveira, E. (2009). “Clues for detecting irony in user-generated contents: oh…!! it's so easy,” in Paper Presented at the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion (Hong Kong). doi: 10.1145/1651461.1651471

CrossRef Full Text | Google Scholar

Chairunnisa, S., and Benedictus, A. (2017). Analysis of emoji and emoticon usage in interpersonal communication of Blackberry messenger and WhatsApp application user. Int. J. Soc. Sci. Manage. 4, 120–126. doi: 10.3126/ijssm.v4i2.17173

CrossRef Full Text | Google Scholar

Chao, C., He, X., and Fu, Z. (2019). “Emo-view: convey the emotion of the back-seat passenger with an emoji in rear-view mirror to the driver,” in Paper Presented at the International Conference on Human-Computer Interaction. doi: 10.1007/978-3-030-22580-3_9

CrossRef Full Text | Google Scholar

Chen, X., and Siu, K. W. M. (2017). Exploring user behaviour of emoticon use among Chinese youth. Behav. Inf. Technol. 36, 637–649. doi: 10.1080/0144929X.2016.1269199

CrossRef Full Text | Google Scholar

Chen, Y., Yuan, J., You, Q., and Luo, J. (2018). “Twitter sentiment analysis via bi-sense emoji embedding and attention-based LSTM,” in Paper Presented at the 2018 ACM Multimedia Conference on Multimedia Conference. doi: 10.1145/3240508.3240533

CrossRef Full Text | Google Scholar

Chen, Z., Lu, X., Ai, W., Li, H., Mei, Q., and Liu, X. (2018). “Through a gender lens: learning usage patterns of emojis from large-scale android users,” in Paper Presented at the 2018 World Wide Web Conference on World Wide Web (Lyon). doi: 10.1145/3178876.3186157

CrossRef Full Text | Google Scholar

Cheng, L. (2017). Do I mean what I say and say what I mean? A cross cultural ap-proach to the use of emoticons & emojis in CMC messages. Fonseca J. Commun. 15, 199–217. doi: 10.14201/fjc201715199217

CrossRef Full Text | Google Scholar

Chik, A., and Vasquez, C. (2017). A comparative multimodal analysis of restaurant reviews from two geographical contexts. Vis. Commun. 16, 3–26. doi: 10.1177/1470357216634005

CrossRef Full Text | Google Scholar

Cho, K.-L. (2016). The differences of emoticon use and its effects depending upon problem types and discussion message types in the process of online problem-solving discussions. J. Educ. Technol. 32, 355–390. doi: 10.17232/KSET.32.2.355

CrossRef Full Text | Google Scholar

Cramer, H., Juan, P. D., and Tetreault, J. (2016). “Sender-intended functions of emojis in US messaging,” in Paper Presented at the International Conference on Human-Computer Interaction With Mobile Devices & Services. doi: 10.1145/2935334.2935370

CrossRef Full Text | Google Scholar

Daniel, T. A., and Camp, A. L. (2018). Emojis affect processing fluency on social media. Psychol. Pop. Media Cult. doi: 10.1037/ppm0000219

CrossRef Full Text | Google Scholar

Das, G., Wiener, H. J. D., and Kareklas, I. (2019). To emoji or not to emoji? Examining the influence of emoji on consumer reactions to advertising. J. Bus. Res. 96, 147–156. doi: 10.1016/j.jbusres.2018.11.007

CrossRef Full Text | Google Scholar

Derks, D., Bos, A. E., and Von Grumbkow, J. (2008a). Emoticons and online message interpretation. Soc. Sci. Comput. Rev. 26, 379–388. doi: 10.1177/0894439307311611

CrossRef Full Text | Google Scholar

Derks, D., Fischer, A. H., and Bos, A. E. R. (2008b). The role of emotion in computer-mediated communication: a review. Comput. Hum. Behav. 24, 766–785. doi: 10.1016/j.chb.2007.04.004

CrossRef Full Text | Google Scholar

Dimson, T. (2015). Emojineering part 1: machine learning for emoji trends. Instagr. Eng. Blog 30.

Google Scholar

Donato, G., and Paggio, P. (2017). “Investigating redundancy in emoji use: study on a twitter based corpus,” in Paper Presented at the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. doi: 10.18653/v1/W17-5216

CrossRef Full Text | Google Scholar

Dresner, E., and Herring, S. C. (2010). Functions of the nonverbal in CMC: emoticons and illocutionary force. Commun. Theory 20, 249–268. doi: 10.1111/j.1468-2885.2010.01362.x

CrossRef Full Text | Google Scholar

Dunlap, J. C., Bose, D., Lowenthal, P. R., York, C. S., Atkinson, M., and Murtagh, J. (2016). “Chapter 8 – What sunshine is to flowers : a literature review on the use of emoticons to support online learning,” in Emotions Technology Design & Learning, 163–182. doi: 10.1016/B978-0-12-801856-9.00008-6

CrossRef Full Text | Google Scholar

Elder, A. M. (2018). What words can't say: emoji and other non-verbal elements of technologically-mediated communication. J. Inf. Commun. Ethics Soc. 16, 2–15. doi: 10.1108/JICES-08-2017-0050

CrossRef Full Text | Google Scholar

Esposito, G., Hernã, N. P., Van, B. R., and Vila, J. (2017). Nudging to prevent the purchase of incompatible digital products online: an experimental study. PLoS ONE 12:e0173333. doi: 10.1371/journal.pone.0173333

PubMed Abstract | CrossRef Full Text | Google Scholar

Fane, J. (2017). Using emoji as a tool to support child wellbeing from a strengths-based approach. Learn. Commun. Int. J. Lear. Soc. Contexts 21, 96–107. doi: 10.18793/lcj2017.21.08

CrossRef Full Text | Google Scholar

Fane, J., MacDougall, C., Jovanovic, J., Redmond, G., and Gibbs, L. (2018). Exploring the use of emoji as a visual research method for eliciting young children's voices in childhood research. Early Child Dev. Care 188, 359–374. doi: 10.1080/03004430.2016.1219730

CrossRef Full Text | Google Scholar

Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., and Lehmann, S. (2017). “Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm,” in Paper Presented at the 2017 Conference on Empirical Methods in Natural Language Processing (Copenhagen). doi: 10.18653/v1/D17-1169

CrossRef Full Text | Google Scholar

Fernandez-Gavilanes, M., Juncal-Martinez, J., Garcia-Mendez, S., Costa-Montenegro, E., and Javier Gonzalez-Castano, F. (2018). Creating emoji lexica from unsupervised sentiment analysis of their descriptions. Expert Syst. Appl. 103, 74–91. doi: 10.1016/j.eswa.2018.02.043

CrossRef Full Text | Google Scholar

Gallo, K. E., Swaney-Stueve, M., and Chambers, D. H. (2017). A focus group approach to understanding food-related emotions with children using words and emojis. J. Sens. Stud. 32:e12264. doi: 10.1111/joss.12264

CrossRef Full Text | Google Scholar

Ganster, T., Eimler, S. C., and Krämer, N. C. (2012). Same same but different!? The differential influence of smilies and emoticons on person perception. Cyberpsychol. Behav. Soc. Netw.15, 226–230. doi: 10.1089/cyber.2011.0179

PubMed Abstract | CrossRef Full Text | Google Scholar

Gantiva, C., Sotaquira, M., Araujo, A., and Cuervo, P. (2019). Cortical processing of human and emoji faces: an ERP analysis. Behav. Inf. Technol. 1–9. doi: 10.1080/0144929X.2019.1632933.

CrossRef Full Text | Google Scholar

Gaspar, R., Barnett, J., and Seibt, B. (2015). Crisis as seen by the individual: the Norm Deviation Approach. Psyecology 6, 103–135. doi: 10.1080/21711976.2014.1002205

CrossRef Full Text | Google Scholar

Gaspar, R., Pedro, C., Panagiotopoulos, P., and Seibt, B. (2016). Beyond positive or negative: qualitative sentiment analysis of social media reactions to unexpected stressful events. Comput. Hum. Behav. 56, 179–191. doi: 10.1016/j.chb.2015.11.040

CrossRef Full Text | Google Scholar

Gaube, S., Tsivrikos, D., Dollinger, D., and Lermer, E. (2018). How a smiley protects health: a pilot intervention to improve hand hygiene in hospitals by activating injunctive norms through emoticons. PLoS ONE 13:e0197465. doi: 10.1371/journal.pone.0197465

PubMed Abstract | CrossRef Full Text | Google Scholar

Gawne, L., and McCulloch, G. (2019). Emoji as digital gestures. Language@ Internet 17.

Google Scholar

Ge, J., and ACM (2019). “Emoji sequence use in enacting personal identity,” in Paper Presented at the Companion of the World Wide Web Conference. doi: 10.1145/3308560.3316545

CrossRef Full Text | Google Scholar

Ge, J., and Gretzel, U. (2018). Emoji rhetoric: a social media influencer perspective. J. Mark. Manage. 34, 1272–1295. doi: 10.1080/0267257X.2018.1483960

CrossRef Full Text | Google Scholar

Gibson, W., Huang, P., and Yu, Q. (2018). Emoji and communicative action: the semiotics, sequence and gestural actions of ‘face covering hand'. Discourse Context Media 26, 91–99. doi: 10.1016/j.dcm.2018.05.005

CrossRef Full Text | Google Scholar

GülÅŸen, T. T. (2016). “You tell me in emojis,” in Computational and Cognitive Approaches to Narratology (IGI Global), 354–375. doi: 10.4018/978-1-5225-0432-0.ch014

CrossRef Full Text | Google Scholar

Guthier, B., Ho, K., and El Saddik, A. (2017). “Language-independent data set annotation for machine learning-based sentiment analysis,” in Paper Presented at the Systems, Man, and Cybernetics (SMC), 2017 IEEE International Conference on. doi: 10.1109/SMC.2017.8122930

CrossRef Full Text | Google Scholar

Hall, J. A., and Pennington, N. (2013). Self-monitoring, honesty, and cue use on Facebook: the relationship with user extraversion and conscientiousness. Comput. Hum. Behav. 29, 1556–1564. doi: 10.1016/j.chb.2013.01.001

CrossRef Full Text | Google Scholar

Hallsmar, F., and Palm, J. (2016). Multi-class sentiment classification on Twitter using an emoji training heuristic. Ind. Manage. Data Syst. 118, 1804–1820.

Google Scholar

Harris, R. B., and Paradice, D. (2007). An investigation of the computer-mediated communication of emotions. J. Appl. Sci. Res. 3, 2081–2090.

Google Scholar

Hayati, S. A., and Muis, A. O. (2019). “Analyzing incorporation of emotion in emoji prediction,” in Paper Presented at the Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. doi: 10.18653/v1/W19-1311

CrossRef Full Text | Google Scholar

Herring, S. C., and Dainas, A. R. (2018). “Receiver interpretations of emoji functions: a gender perspective,” in Paper Presented at the 1st International Workshop on Emoji Understanding and Applications in Social Media (Emoji2018) (Stanford).

Google Scholar

Hjartstrom, H., Sorman, D. E., and Ljungberg, J. K. (2019). Distraction and facilitation: the impact of emotional sounds in an emoji oddball task. PsyCh J. 8, 180–186. doi: 10.1002/pchj.273

PubMed Abstract | CrossRef Full Text | Google Scholar

Hogenboom, A., Bal, D., Frasincar, F., Bal, M., de Jong, F., and Kaymak, U. (2013). “Exploiting emoticons in sentiment analysis,” in Paper Presented at the 28th Annual ACM Symposium on Applied Computing. doi: 10.1145/2480362.2480498

CrossRef Full Text | Google Scholar

Jack, R. E., Blais, C., Scheepers, C., Schyns, P. G., and Caldara, R. (2009). Cultural confusions show that facial expressions are not universal. Curr. Biol. 19, 1543–1548. doi: 10.1016/j.cub.2009.07.051

CrossRef Full Text | Google Scholar

Jaeger, S. R., and Ares, G. (2017). Dominant meanings of facial emoji: insights from Chinese consumers and comparison with meanings from internet resources. Food Qual. Prefer. 62, 275–283. doi: 10.1016/j.foodqual.2017.04.009

CrossRef Full Text | Google Scholar

Jaeger, S. R., Lee, S. M., Kim, K.-O., Chheang, S. L., Jin, D., and Ares, G. (2017a). Measurement of product emotions using emoji surveys: case studies with tasted foods and beverages. Food Qual. Prefer. 62, 46–59. doi: 10.1016/j.foodqual.2017.05.016

CrossRef Full Text | Google Scholar

Jaeger, S. R., Roigard, C. M., and Ares, G. (2018a). Measuring consumers' product associations with emoji and emotion word questionnaires: case studies with tasted foods and written stimuli. Food Res. Int. 111, 732–747. doi: 10.1016/j.foodres.2018.04.010

PubMed Abstract | CrossRef Full Text | Google Scholar

Jaeger, S. R., Roigard, C. M., Jin, D., Vidal, L., and Ares, G. (2019). Valence, arousal and sentiment meanings of 33 facial emoji: insights for the use of emoji in consumer research. Food Res. Int. 119, 895–907. doi: 10.1016/j.foodres.2018.10.074

PubMed Abstract | CrossRef Full Text | Google Scholar

Jaeger, S. R., Vidal, L., Kam, K., and Ares, G. (2017b). Can emoji be used as a direct method to measure emotional associations to food names? Preliminary investigations with consumers in USA and China. Food Qual. Prefer. 56, 38–48. doi: 10.1016/j.foodqual.2016.09.005

CrossRef Full Text | Google Scholar

Jaeger, S. R., Xia, Y., Lee, P.-Y., Hunter, D. C., Beresford, M. K., and Ares, G. (2018b). Emoji questionnaires can be used with a range of population segments: findings relating to age, gender and frequency of emoji/emoticon use. Food Qual. Prefer. 68, 397–410. doi: 10.1016/j.foodqual.2017.12.011

CrossRef Full Text | Google Scholar

Jiang, F., Liu, Y.-Q., Luan, H.-B., Sun, J.-S., Zhu, X., Zhang, M., et al. (2015). Microblog sentiment analysis with emoticon space model. J. Comput. Sci. Technol. 30, 1120–1129. doi: 10.1007/s11390-015-1587-1

CrossRef Full Text | Google Scholar

Jibril, T. A., and Abdullah, M. H. (2013). Relevance of emoticons in computer-mediated communication contexts: an overview. Asian Soc. Sci. 9:201. doi: 10.5539/ass.v9n4p201

CrossRef Full Text | Google Scholar

Juhasz, A., and Bradford, K. (2016). Mobile phone use in romantic relationships. Marriage Fam. Rev. 52, 707–721. doi: 10.1080/01494929.2016.1157123

CrossRef Full Text | Google Scholar

Kaneko, D., Toet, A., Ushiama, S., Brouwer, A.-M., Kallen, V., and van Erp, J. B. F. (2019). EmojiGrid: a 2D pictorial scale for cross-cultural emotion assessment of negatively and positively valenced food. Food Res. Int. 115, 541–551. doi: 10.1016/j.foodres.2018.09.049

PubMed Abstract | CrossRef Full Text | Google Scholar

Kaye, L. K., Wall, H. J., and Malone, S. A. (2016). “Turn that frown upside-down”: a contextual account of emoticon usage on different virtual platforms. Comput. Hum. Behav. 60, 463–467. doi: 10.1016/j.chb.2016.02.088

CrossRef Full Text | Google Scholar

Kelly, R., and Watts, L. (2015). “Characterising the inventive appropriation of emoji as relationally meaningful in mediated close personal relationships,” in Paper Presented at the Experiences of Technology Appropriation: Unanticipated Users, Usage, Circumstances, and Design (Oslo).

Google Scholar

Khandekar, S., Higgs, J., Bian, Y., Ryu, C. W., Talton, J. O., Kumar, R., et al. (2019). “Opico: a study of emoji-first communication in a mobile social app,” in Paper Presented at the Companion of the World Wide Web Conference. doi: 10.1145/3308560.3316547

CrossRef Full Text | Google Scholar

Kim, J.-G., Gong, T., Huang, E., Kim, J., Lee, S.-J., Kim, B., et al. (2019). “Bringing context into emoji recommendations,” in Paper Presented at the Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services. doi: 10.1145/3307334.3328601

CrossRef Full Text | Google Scholar

Kimura, M., and Katsurai, M. (2017). “Automatic construction of an emoji sentiment lexicon,” in Paper Presented at the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. doi: 10.1145/3110025.3110139

CrossRef Full Text | Google Scholar

Kraus, L., Schmidt, R., Walch, M., Schaub, F., and Moeller, S. (2017). “On the use of emojis in mobile authentication,” in Paper Presented at the International Conference on Ict Systems Security & Privacy Protection (Cham: Springer) doi: 10.1007/978-3-319-58469-0_18

CrossRef Full Text | Google Scholar

LeCompte, T., and Chen, J. (2017). “Sentiment analysis of tweets including emoji data,” in Paper Presented at the 2017 International Conference on Computational Science and Computational Intelligence. doi: 10.1109/CSCI.2017.137

CrossRef Full Text | Google Scholar

Lee, D., Hosanagar, K., and Nair, H. (2014). The Effect of Social Media Marketing Content on Consumer Engagement: Evidence From Facebook. Social Science Electronic Publishing.

Google Scholar

Lee, J., Li, J., Mina, A. X., and ACM. (2019). “Hanmoji: what Chinese characters and emoji reveal about each other,” in Paper Presented at the Companion of the World Wide Web Conference. doi: 10.1145/3308560.3316543

CrossRef Full Text | Google Scholar

Lee, J. Y., Hong, N., Kim, S., Oh, J., and Lee, J. (2016). “Smiley face: why we use emoticon stickers in mobile messaging,” in Paper Presented at the 18th International Conference on Human-Computer Interaction With Mobile Devices and Services Adjunct. doi: 10.1145/2957265.2961858

CrossRef Full Text | Google Scholar

Lee, V., and Wagner, H. (2002). The effect of social presence on the facial and verbal expression of emotion and the interrelationships among emotion components. J. Nonverbal Behav. 26, 3–25. doi: 10.1023/A:1014479919684

CrossRef Full Text | Google Scholar

Leslie, E. (2019). This other atmosphere: against human resources, emoji and devices. J. Vis. Cult. 18, 3–29. doi: 10.1177/1470412919825816

CrossRef Full Text | Google Scholar

Li, M., Ch'ng, E., Chong, A. Y. L., and See, S. (2018). Multi-class Twitter sentiment classification with emojis. Ind. Manage. Data Syst. 118, 1804–1820. doi: 10.1108/IMDS-12-2017-0582

CrossRef Full Text | Google Scholar

Li, W., Chen, Y., Hu, T., and Luo, J. (2018). “Mining the relationship between emoji usage patterns and personality,” in Paper Presented at the Twelfth International AAAI Conference on Web and Social Media.

Google Scholar

Li, Y.-M., Lin, L., and Chiu, S.-W. (2014). Enhancing targeted advertising with social context endorsement. Int. J. Electr. Commer. 19, 99–128. doi: 10.2753/JEC1086-4415190103

CrossRef Full Text | Google Scholar

Liebeskind, C., Liebeskind, S., and ACM. (2019). “Emoji prediction for Hebrew political domain,” in Paper Presented at the Companion of the World Wide Web Conference. doi: 10.1145/3308560.3316548

CrossRef Full Text | Google Scholar

Lim, S. S. (2015). On stickers and communicative fluidity in social media. Social. Media+Society 1, 1–3. doi: 10.1177/2056305115578137

CrossRef Full Text | Google Scholar

Lima, M., de Alcantara, M., Martins, I. B. A., Ares, G., and Deliza, R. (2019). Can front-of-pack nutrition labeling influence children's emotional associations with unhealthy food products? An experiment using emoji. Food Res. Int. 120, 217–225. doi: 10.1016/j.foodres.2019.02.027

PubMed Abstract | CrossRef Full Text | Google Scholar

Lin, T.-J., and Chen, C.-H. (2018). A preliminary study of the form and status of passionate affection emoticons. Int. J. Des. 12, 75–90.

Google Scholar

Liu, K.-L., Li, W.-J., and Guo, M. (2012). “Emoticon smoothed language models for twitter sentiment analysis,” in Paper Presented at the Aaai.

Google Scholar

LjubeÅ¡ić, N., and FiÅ¡er, D. (2016). “A global analysis of emoji usage” in Paper Presented at the Proceedings of the 10th Web as Corpus Workshop. doi: 10.18653/v1/W16-2610

CrossRef Full Text | Google Scholar

Lo, S. K. (2008). The nonverbal communication functions of emoticons in computer-mediated communication. Cyberpsychol. Behav. 11, 595–597. doi: 10.1089/cpb.2007.0132

PubMed Abstract | CrossRef Full Text | Google Scholar

López, R. P., and Cap, F. (2017). “Did you ever read about frogs drinking coffee? investigating the compositionality of multi-emoji expressions,” in Paper Presented at the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. doi: 10.18653/v1/W17-5215

CrossRef Full Text | Google Scholar

Luangrath, A. W., Peck, J., and Barger, V. A. (2017). Textual paralanguage and its implications for marketing communications. J. Consum. Psychol. 27, 98–107. doi: 10.1016/j.jcps.2016.05.002

CrossRef Full Text | Google Scholar

Mahajan, K., and Shaikh, S. (2019). “Emoji usage across platforms: a case study for the Charlottesville event,”in Paper Presented at the Proceedings of the 2019 Workshop on Widening NLP.

Google Scholar

Manganari, E. E., and Dimara, E. (2017). Enhancing the impact of online hotel reviews through the use of emoticons. Behav. Inf. Technol. 36, 1–13. doi: 10.1080/0144929X.2016.1275807

CrossRef Full Text | Google Scholar

Marengo, D., Giannotta, F., and Settanni, M. (2017). Assessing personality using emoji: an exploratory study. Pers. Indiv. Differ. 112, 74–78. doi: 10.1016/j.paid.2017.02.037

CrossRef Full Text | Google Scholar

Marengo, D., Settanni, M., and Giannotta, F. (2019). Development and preliminary validation of an image-based instrument to assess depressive symptoms. Psychiatry Res. 279, 180–185. doi: 10.1016/j.psychres.2019.02.059

PubMed Abstract | CrossRef Full Text | Google Scholar

Mayank, D., Padmanabhan, K., and Pal, K. (2016). “Multi-sentiment modeling with scalable systematic labeled data generation via Word2Vec clustering,” in Paper Presented at the 2016 IEEE 16th International Conference on Data Mining Workshops (Barcelona). doi: 10.1109/ICDMW.2016.0139

CrossRef Full Text | Google Scholar

Miller, H., Thebault-Spieker, J., Chang, S., Johnson, I., Terveen, L., and Hecht, B. (2016). “Blissfully happy” or “ready to fight”: varying interpretations of emoji. ICWSM.

Google Scholar

Moreno-Sandoval, L. G., Sanchez-Barriga, C., Espindola Buitrago, K., Pomares-Quimbaya, A., and Carlos Garcia, J. (2018). “Spanish Twitter data used as a source of information about consumer food choice,” in Machine Learning and Knowledge Extraction, Cd-Make 2018, Vol. 11015, eds A. Holzinger, P. Kieseberg, A. M. Tjoa, and E. Weippl, 134–146. doi: 10.1007/978-3-319-99740-7_9

CrossRef Full Text | Google Scholar

Moussa, S. (2019). An emoji-based metric for monitoring consumers' emotions toward brands on social media. Mark. Intell. Plann. 37, 211–225. doi: 10.1108/MIP-07-2018-0257

CrossRef Full Text | Google Scholar

Na'aman, N., Provenza, H., and Montoya, O. (2017). “Varying linguistic purposes of emoji in (twitter) context,” in Paper Presented at the ACL 2017, Student Research Workshop. doi: 10.18653/v1/P17-3022

CrossRef Full Text | Google Scholar

Negishi, M. (2014). Meet Shigetaka Kurita, the father of emoji. Wall Street Journal.

Google Scholar

Njenga, K. (2018). “Social media information security threats: anthropomorphic emoji analysis on social engineering,” in Paper Presented at the IT Convergence and Security 2017 (Seoul). doi: 10.1007/978-981-10-6454-8_24

CrossRef Full Text | Google Scholar

Park, J., Baek, Y. M., and Cha, M. (2014). Cross-cultural comparison of nonverbal cues in emoticons on Twitter: evidence from big data analysis. J. Commun. 64, 333–354. doi: 10.1111/jcom.12086

CrossRef Full Text | Google Scholar

Park, J., Barash, V., Fink, C., and Cha, M. (2013). “Emoticon style: interpreting differences in emoticons across cultures,” in Paper Presented at the ICWSM.

Google Scholar

Perry, M. S., and Werner-Wilson, R. J. (2011). Couples and computer-mediated communication: a closer look at the affordances and use of the channel. Fam. Consum. Sci. Res. J. 40, 120–134. doi: 10.1111/j.1552-3934.2011.02099.x

CrossRef Full Text | Google Scholar

Petra, K. N., Jasmina, S., Borut, S., and Igor, M. (2015). Sentiment of emojis. PLoS ONE 10:e0144296. doi: 10.1371/journal.pone.0144296

CrossRef Full Text | Google Scholar

Pettigrew, J. (2009). Text messaging and connectedness within close interpersonal relationships. Marriage Fam. Rev. 45, 697–716. doi: 10.1080/01494920903224269

CrossRef Full Text | Google Scholar

Phan, W. M. J., Amrhein, R., Rounds, J., and Lewis, P. (2019). Contextualizing interest scales with emojis: implications for measurement and validity. J. Career Assessm. 27, 114–133. doi: 10.1177/1069072717748647

CrossRef Full Text | Google Scholar

Phand, S. A., Chakkarwar, V. A., and IEEE. (2018). “Enhanced sentiment classification using geo location tweets,” in Paper Presented at the 2018 Second International Conference on Inventive Communication and Computational Technologies (Coimbatore).

Google Scholar

Prada, M., Rodrigues, D. L., Garrido, M. V., Lopes, D., Cavalheiro, B., and Gaspar, R. (2018). Motives, frequency and attitudes toward emoji and emoticon use. Telematics Inform. 35, 1925–1934. doi: 10.1016/j.tele.2018.06.005

CrossRef Full Text | Google Scholar

Prasad, A. G., Sanjana, S., Bhat, S. M., Harish, B. S., and IEEE. (2017). “Sentiment analysis for sarcasm detection on streaming short text data,” in Paper Presented at the 2017 2nd International Conference on Knowledge Engineering and Applications (ICKEA). doi: 10.1109/ICKEA.2017.8169892

CrossRef Full Text | Google Scholar

Rathan, M., Hulipalled, V. R., Murugeshwari, P., and Sushmitha, H. (2017). “Every post matters: a survey on applications of sentiment analysis in social media,” in Paper Presented at the Smart Technologies for Smart Nation (SmartTechCon), 2017 International Conference On (Bangalore). doi: 10.1109/SmartTechCon.2017.8358463

CrossRef Full Text | Google Scholar

Rathan, M., Hulipalled, V. R., Venugopal, K. R., and Patnaik, L. M. (2018). Consumer insight mining: aspect based Twitter opinion mining of mobile phone reviews. Appl. Soft Comput. 68, 765–773. doi: 10.1016/j.asoc.2017.07.056

CrossRef Full Text | Google Scholar

Redmond, M., Salesi, S., Cosma, G., and IEEE. (2017). “A novel approach based on an extended cuckoo search algorithm for the classification of tweets which contain emoticon and emoji,” in Paper Presented at the 2017 2nd International Conference on Knowledge Engineering and Applications (London). doi: 10.1109/ICKEA.2017.8169894

CrossRef Full Text | Google Scholar

Reyes, A., Rosso, P., and Veale, T. (2013). A multidimensional approach for detecting irony in Twitter. Lang. Res. Eval. 47, 239–268. doi: 10.1007/s10579-012-9196-x

CrossRef Full Text | Google Scholar

Riordan, M. A. (2017a). The communicative role of non-face emojis: affect and disambiguation. Comput. Hum. Behav. 76, 75–86. doi: 10.1016/j.chb.2017.07.009

CrossRef Full Text | Google Scholar

Riordan, M. A. (2017b). Emojis as tools for emotion work: communicating affect in text messages. J. Lang. Soc. Psychol. 36, 549–567. doi: 10.1177/0261927X17704238

CrossRef Full Text | Google Scholar

Riordan, M. A., and Kreuz, R. J. (2010). Emotion encoding and interpretation in computer-mediated communication: reasons for use. Comput. Hum. Behav. 26, 1667–1673. doi: 10.1016/j.chb.2010.06.015

CrossRef Full Text | Google Scholar

Rodrigues, D., Prada, M., Rui, G., Garrido, M. V., and Lopes, D. (2017). Lisbon emoji and emoticon database (LEED): norms for emoji and emoticons in seven evaluative dimensions. Behav. Res. Methods 50, 1–14. doi: 10.3758/s13428-017-0878-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Sadiq, M., and Shahida, IEEE. (2019). “Learning Pakistani culture through the namaz emoji,” in Paper Presented at the 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies. doi: 10.1109/ICOMET.2019.8673479

CrossRef Full Text | Google Scholar

Sakai, N. (2013). The role of sentence closing as an emotional marker: a case of Japanese mobile phone e-mail. Discourse Context Media 2, 149–155. doi: 10.1016/j.dcm.2013.07.001

CrossRef Full Text | Google Scholar

Sampietro, A. (2019). Emoji and rapport management in Spanish WhatsApp chats. J. Pragmat. 143, 109–120. doi: 10.1016/j.pragma.2019.02.009

CrossRef Full Text | Google Scholar

Sari, Y. A., Ratnasari, E. K., Mutrofin, S., and Arifin, A. Z. (2014). User emotion identification in Twitter using specific features: hashtag, emoji, emoticon, and adjective term. Jurnal Ilmu Komputer dan Informasi 7, 18–23. doi: 10.21609/jiki.v7i1.252

CrossRef Full Text | Google Scholar

Schouteten, J. J., Verwaeren, J., Gellynck, X., and Almli, V. L. (2019). Comparing a standardized to a product-specific emoji list for evaluating food products by children. Food Qual. Prefer. 72, 86–97. doi: 10.1016/j.foodqual.2018.09.007

CrossRef Full Text | Google Scholar

Settanni, M., and Marengo, D. (2015). Sharing feelings online: studying emotional well-being via automated text analysis of Facejournal posts. Front. Psychol. 6:1045. doi: 10.3389/fpsyg.2015.01045

PubMed Abstract | CrossRef Full Text | Google Scholar

Shi, L., Wang, Z., Qian, Z., Huang, N., Puteaux, P., and Zhang, X. (2019). Distortion function for emoji image steganography. Computers Mater Continua 59, 943–953. doi: 10.32604/cmc.2019.05768

CrossRef Full Text | Google Scholar

Siegel, R. M., Anneken, A., Duffy, C., Simmons, K., Hudgens, M., Lockhart, M. K., et al. (2015). Emoticon use increases plain milk and vegetable purchase in a school cafeteria without adversely affecting total milk purchase. Clin. Ther. 37, 1938–1943. doi: 10.1016/j.clinthera.2015.07.016

PubMed Abstract | CrossRef Full Text | Google Scholar

Singh, A., Blanco, E., and Jin, W. (2019). “Incorporating emoji descriptions improves tweet classification,” in Paper Presented at the Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). doi: 10.18653/v1/N19-1214

CrossRef Full Text | Google Scholar

Skiba, D. J. (2016). Face with tears of joy is word of the year: are emoji a sign of things to come in health care? Nurs. Educ. Perspect. 37:56. doi: 10.1097/01.NEP.0000476112.24899.a1

PubMed Abstract | CrossRef Full Text | Google Scholar

Sodikin, M. (2018). “Emoticon symbols applied for traffic signs,” in Paper Presented at the International Conference on Applied Science and Engineering (ICASE 2018). doi: 10.2991/icase-18.2018.2

CrossRef Full Text | Google Scholar

Stark, L., and Crawford, K. (2015). The conservatism of emoji: work, affect, and communication. Social Media+ Society 1, 1–11. doi: 10.1177/2056305115604853

CrossRef Full Text | Google Scholar

Sugiyama, S. (2015). Kawaii meiru and Maroyaka neko: mobile emoji for relationship maintenance and aesthetic expressions among Japanese teens. First Monday 20:1. doi: 10.5210/fm.v20i10.5826

CrossRef Full Text | Google Scholar

Swaney-Stueve, M., Jepsen, T., and Deubler, G. (2018). The emoji scale: a facial scale for the 21st century. Food Qual. Prefer. 68, 183–190. doi: 10.1016/j.foodqual.2018.03.002

CrossRef Full Text | Google Scholar

Sweeney, M. E., and Whaley, K. (2019). Technically white: emoji skin-tone modifiers as American technoculture. First Monday 24. doi: 10.5210/fm.v24i7.10060

CrossRef Full Text | Google Scholar

Tan, L., Hui, J. T., Lai, K. S., and Low, J. A. (2018). Sensitivity and specificity analysis: use of emoticon for screening of depression in elderly in Singapore. J. Am. Psychiatr. Nurses Assoc 24, 452–456. doi: 10.1177/1078390318766665

PubMed Abstract | CrossRef Full Text | Google Scholar

Tauch, C., and Kanjo, E. (2016). “The roles of emojis in mobile phone notifications,” in Paper Presented at the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (Heidelberg). doi: 10.1145/2968219.2968549

CrossRef Full Text | Google Scholar

Thompson, D., Mackenzie, I. G., Leuthold, H., and Filik, R. (2016). Emotional responses to irony and emoticons in written language: evidence from EDA and facial EMG. Psychophysiology 53, 1054–1062. doi: 10.1111/psyp.12642

PubMed Abstract | CrossRef Full Text | Google Scholar

Thomson, S., Kluftinger, E., and Wentland, J. (2018). Are you fluent in sexual emoji?(sic): exploring the use of emoji in romantic and sexual contexts. Can. J. Hum. Sex. 27, 226–234. doi: 10.3138/cjhs.2018-0020

CrossRef Full Text | Google Scholar

Tigwell, G. W., and Flatla, D. R. (2016). “Oh that's what you meant!: reducing emoji misunderstanding,” in Paper Presented at the 18th International Conference on Human-Computer Interaction With Mobile Devices and Services Adjunct (Florence). doi: 10.1145/2957265.2961844

CrossRef Full Text | Google Scholar

Tossell, C. C., Kortum, P., Shepard, C., Barg-Walkow, L. H., Rahmati, A., and Zhong, L. (2012). A longitudinal study of emoticon use in text messaging from smartphones. Comput. Hum. Behav. 28, 659–663. doi: 10.1016/j.chb.2011.11.012

CrossRef Full Text | Google Scholar

Troiano, G., and Nante, N. (2018). Emoji: what does the scientific literature say about them?-A new way to communicate in the 21th century. J. Hum. Behav. Soc. Environ. 28, 528–533. doi: 10.1080/10911359.2018.1437103

CrossRef Full Text | Google Scholar

Urumutta Hewage, G., Wang, Z., and Liu, Y. (2018). Effects of Facial Asymmetry on Emoji Evaluation and Product Preference. ACR European Advances.

Google Scholar

Vandergriff, I. (2013). Emotive communication online: a contextual analysis of computer-mediated communication (CMC) cues. J. Pragmat. 51, 1–12. doi: 10.1016/j.pragma.2013.02.008

CrossRef Full Text | Google Scholar

Vanin, A. A., Freitas, L. A., Vieira, R., and Bochernitsan, M. (2013). “Some clues on irony detection in tweets” in Paper Presented at the 22nd International Conference on World Wide Web. doi: 10.1145/2487788.2488012

CrossRef Full Text | Google Scholar

Vidal, L., Ares, G., and Jaeger, S. R. (2016). Use of emoticon and emoji in tweets for food-related emotional expression. Food Qual. Prefer. 49, 119–128. doi: 10.1016/j.foodqual.2015.12.002

CrossRef Full Text | Google Scholar

Wall, H. J., Kaye, L. K., and Malone, S. A. (2016). An exploration of psychological factors on emoticon usage and implications for judgement accuracy. Comput. Hum. Behav. 62, 70–78. doi: 10.1016/j.chb.2016.03.040

CrossRef Full Text | Google Scholar

Walther, J. B., and D'Addario, K. P. (2001). The Impacts of emoticons on message interpretation in computer-mediated communication. Soc. Sci. Comput. Rev. 19, 324–347. doi: 10.1177/089443930101900307

CrossRef Full Text | Google Scholar

Wang, S. S. (2016). More than words? The effect of line character sticker use on intimacy in the mobile communication environment. Soc. Sci Comput. Rev. 34, 456–478. doi: 10.1177/0894439315590209

CrossRef Full Text | Google Scholar

Wang, W., Lu, C., Thirunarayan, K., and Sheth, A. P. (2012). “Harnessing Twitter “big data” for automatic emotion identification,” in Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Confernece on Social Computing (SocialCom) (Amsterdam: IEEE), 587–592. doi: 10.1109/SocialCom-PASSAT.2012.119

CrossRef Full Text | Google Scholar

Wang, Z., Cui, X., Gao, L., Yin, Q., Ke, L., and Zhang, S. (2016). A hybrid model of sentimental entity recognition on mobile social media. EURASIP J. Wirel Commun. Netw. 2016:253. doi: 10.1186/s13638-016-0745-7

CrossRef Full Text | Google Scholar

Wijeratne, S., Balasuriya, L., Sheth, A., Doran, D., and ACM. (2017). “A semantics-based measure of emoji similarity,” in Paper Presented at the 2017 IEEE/WIC/ACM International Conference on Web Intelligence. doi: 10.1145/3106426.3106490

CrossRef Full Text | Google Scholar

Wolf, A. (2000). Emotional expression online: gender differences in emoticon use. CyberPsychol. Behav. 3, 827–833. doi: 10.1089/10949310050191809

CrossRef Full Text | Google Scholar

Xuan, L., Wei, A., Liu, X., Qian, L., Ning, W., Gang, H., et al. (2016). “Learning from the ubiquitous language: an empirical analysis of emoji usage of smartphone users,” in Paper Presented at the ACM International Joint Conference on Pervasive & Ubiquitous Computing (Heidelberg).

Google Scholar

Yakin, V., and Eru, O. (2015). An application to determine the efficacy of emoji use on social marketing ads. Int. J. Soc. Sci. Educ. Res. 3, 230–240. doi: 10.24289/ijsser.270652

CrossRef Full Text | Google Scholar

Zanzotto, F. M., and Santilli, A. (2018). “SyntNN at SemEval-2018 task 2: is syntax useful for emoji prediction? embedding syntactic trees in multi layer perceptrons,” in Paper Presented at the Proceedings of The 12th International Workshop on Semantic Evaluation. doi: 10.18653/v1/S18-1076

CrossRef Full Text | Google Scholar

Zerkina, N., Chusavitina, G., and Lomakina, Y. (2017). Verbal aggression in virtual environment. Mod. J. Lang. Teach. Methods 7, 750–756.

Google Scholar

Zhou, R., Hentschel, J., and Kumar, N. (2017). “Goodbye text, hello emoji: mobile communication on wechat in China,” in Paper Presented at the Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. doi: 10.1145/3025453.3025800

CrossRef Full Text | Google Scholar

Keywords: emoji, communication, emotion expression, semantic expression, systematic review

Citation: Bai Q, Dan Q, Mu Z and Yang M (2019) A Systematic Review of Emoji: Current Research and Future Perspectives. Front. Psychol. 10:2221. doi: 10.3389/fpsyg.2019.02221


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