The Oxford Dictionary Word of the Year (WOTY) is not a word but a pictograph, Face with Tears of Joy. For those of you who are not familiar with this pictograph, it is one of the many emoji available for use on your cell phones, tablets, and other devices. In case you missed it, we transitioned from emoticons [;-)] to emoji in the late 1990s.
Face with Tears of Joy was chosen as WOTY because it reflected the ethos, mood, and preoccupations of 2015 (Oxford Dictionaries, 2015). The Oxford University Press worked with SwiftKey, a mobile technology company, to analyze usage statistics across the world. This emoji was chosen among several competitors because it made up 20 percent of the emoji used in the United Kingdom and 17 percent of those used in the United States.
Even the word emoji has tripled in use since it emerged in 1997. You are probably aware of the smiley faces you get on text messages or how your adolescents order pizza at Dominos. To test your knowledge of emoji, visit the Learning Network at the New York Times (http://learning.blogs.nytimes.com/2014/08/05/test-yourself-emoji/?_r=0).
A BRIEF HISTORY
Shigetaka Kurita developed the concept of emoji while working for a Japanese company, Docomo, that was creating a mobile Internet platform to provide news, weather, email, and reservations. According to Blagdon (2013), “The lack of visual cures made the service more difficult to use…and could benefit majorly from some extra characters to show contextual information.”
I was intrigued by how Kurita viewed the new technology and its impact on communication. According to Kurita, the Japanese traditionally wrote long letters for communication, and the brevity of more casual email sometimes led to miscommunication. Kurita also noticed that face-to-face and phone conversations also provided cues to assess mood or feelings. He concluded “that the promise of digital communication — being able to stay in closer touch with people — was being offset by this accompanying increase in miscommunication” (Blagdon, 2013). Hence, emoji were born as a mechanism to provide contextual information and emotions. Although it took awhile for global acceptance, emoji were eventually adopted into Unicode, a computing industry standard for consistent encoding, representation, and the handling of text expressed in most of the world’s writing systems. Emoji made their international debut in 2011 when they were released on Apple’s iOS5.
From Blagdon’s (2013) article, I was able to link to some interesting websites, for example, Narratives in Emoji (http://narrativesinemoji.tumblr.com/) with Les Miserables. I also found an emoji version of Moby Dick (http://blogs.loc.gov/loc/2013/02/a-whale-of-an-acquisition/) and an emoji “zine” by Womanzine (http://issuu.com/lindseyweber5/docs/emoji_by_womanzine).
Sternbergh (2014) wrote that “elasticity of meaning is a large part of the appeal and, perhaps, the genius of emoji.” Emoji can express emotion on a small screen in an easy manner, making it easier for those of us who find using our thumbs for text messaging to be a challenge. “These seemingly infantile cartoons are instantly recognizable, which makes them understandable even across linguistic barriers.”
IMPLICATIONS FOR RESEARCH, EDUCATION, AND HEALTH CARE
If emoji are recognizable across boundaries, what are their implications for research, education, and health care? To answer this question, I ventured forward to learn more, beyond my simple use of smiley faces. I discovered a growing use of emoji in marketing, education, and, yes, even health care.
A research article (Pavalanathan & Eisenstein, 2015) examines whether emoji will replace ASCII character emoticons. Using a causal inference model, Pavalanathan and Eisenstein compared a treatment group (those using emoji for a specified time) to a control group (those who did not use emoji), with emoticon usage as the dependent variable. They found a statistically significant difference in the use of emoticons, with the treatment group using less and less. In their discussion, they noted that, although emoticons were primarily designed using facial expressions to express emotion, they were also used to “establish more of a conversational connection, a playful interaction or a shared and secret uniqueness within a particular relationship.”
Here are examples of how others use emoji:
* The White House uses emoji to communicate with millennials (Mosendz, 2014).
* Gonzalez (2015) describes various uses of emoji to engage learners; she points to a university campaign to address sexual assault (http://students.ubc.ca/livewell/topics/sexual-assault/consent) and provides a link to the GE Emoji Science website (http://emojiscience.com) with an Emoji Table of Content, lessons plans, and YouTube videos.
* GE Healthcare created a YouTube video with emoji to educate the public about breast density (http://newsroom.gehealthcare.com/breast-density-explained-with-emoji/).
* An app called Abused Emoji (www.abusedemojis.com) was designed by a Swedish not-for-profit group to help children talk about issues such as bullying and being mistreated.
* On World AIDS Day, the Durex Company announced the development of the Safe Sex Emoji Campaign (www.youtube.com/watch?v=WiquWxZHBR4).
* In the Gomer blog (http://gomerblog.com/2014/10/emoji-icd-10/), some ICD-10 emoji, for a variety of health conditions, were developed to give everyone a laugh.
* Writing in the Chronicle of Higher Education, Thompson (2014) tells how researchers took to Twitter (#emojiresearch) to describe their research in emoji because many lay people have difficulty interpreting research articles.
* The best example comes from a group of University of Michigan students who created the Diabeticons app (www.healthdesignby.us/diabeticons/), which started when two teenagers struggling to manage their Type 1 diabetes brought their idea for an app to health researcher Joyce Lee (Tenderich, 2015). Dr. Lee helped the teens participate in the 2014 #MakeHealth, a maker fair, and the University of Michigan students brought the teens’ work to reality.
What great mechanisms for patients and consumers to indicate the impact of their health conditions. Imagine what can happen when people share their thoughts and emotions in this way. You can imagine a support group responding to sad or painful faces by sending their support and encouragement, greatly improving communication in a connected care ecosystem.
The article about diabetes got me thinking about how we might use emoji as a mechanism to address health literacy. I immediately thought of some pioneering work done by researchers at Columbia University under the direction of Dr. Suzanne Bakken and the early work of Dr. Kate Siek of Indiana University. Dr. Bakken’s team explored how infographics can be used to facilitate health literacy and engage patients with their own health data (Arcia et al., 2015; Woollen & Bakken, 2015). Dr. Siek’s work focused on the use of pictures and radio buttons to facilitate health communication for low-literacy patients (Chaudry, Connelly, Siek, & Welch, 2012).
Given the growing use of mhealth apps and their use by diverse populations, including those with low literacy, we could help to remove not only the digital divide but also the health literacy divide. To me, this would be a fascinating area of research and development. Imagine if nursing were to develop a set of emoji that could be used in the care of patients and allow patients to better understand and communicate their challenges with managing their health. There are lots of emoji for emotions, but there are relatively few, if any, that are health related.
So, are you up for the challenge? Let’s see what we can do in 2016. Perhaps we can have a student contest to design health-related emoji and test them across populations. I will throw down the gauntlet with my humble string of emoji. If you can interpret their meaning, send me an email at Diane.Skiba@ucdenver.edu.
Arcia A., Suero-Tejeda N., Bales M. E., Merrill J. A., Yoon S., Woollen J., & Bakken S. (2015). Sometimes more is more: Iterative participatory design of infographics for engagement of community members with varying levels of health literacy. Journal of the American Medical Informatics Association
pii: ocv079. doi:10.1093/jamia/ocv079 [Epub ahead of print].
Chaudry B., Connelly K., Siek K., & Welch J. (2012). Mobile interface designs for low-literacy populations. In Yang C., Lou G., & Liu J. Eds., Proceedings of the 2nd ACM SIGHIT International Informatics Symposium
. New York, NY: Association of Computing Machinery.
Pavalanathan U., & Eisenstein J. (2015). Emoticons vs. emojis on Twitter: A causal inference approach. Cornell University Library
. Retrieved from http://arxiv.org/abs/1510.08480
Woollen J., & Bakken S. (2015). Engaging patients with advance directives using an information visualization approach. Journal of Gerontological Nursing
. doi:10.3928/00989134-20150804-63 [Epub ahead of print].