Do academics use emojis on Twitter? Stefanie Haustein analyzed more than 40 million tweets mentioning scientific journal articles, preprints, conference proceedings, and other documents to find out.

Alice Fleerackers

"Should there be an emoji for everything?" asked journalist Sophie
Haigney in a recent [*New Yorker*
article](https://www.newyorker.com/culture/culture-desk/should-there-be-an-emoji-for-everything){target="_blank"
rel="noopener noreferrer"}. "What, exactly, do we want from our emojis?"

The future of the emoji may be uncertain, but one thing is abundantly
clear. Emojis are booming. From classic smiley faces to dancing "party
parrots," there are now almost 3,000 options to choose from. They fill
our text messages, our Slack chats, our emails---even [some of our
books](https://medium.com/@Jefnwk/3-books-written-entirely-in-emojis-can-you-read-them-fffe71119530){target="_blank"
rel="noopener noreferrer"}. In 2015 the "tears of joy" emoji was
declared [Word of the
Year](https://www.independent.co.uk/arts-entertainment/books/news/emoji-named-word-of-the-year-by-oxford-dictionaries-a6737146.html){target="_blank"
rel="noopener noreferrer"} by Oxford Dictionaries.

But despite the digital dominance of emojis, they haven't infiltrated
every aspect of daily life. Recent research by ScholCommLab co-director
Stefanie Haustein reveals that, when it comes to scholarly
communication, emojis mostly get a 👎.

<figure id="attachment_2218" class="wp-caption alignnone"
aria-describedby="caption-attachment-2218" style="width: 1024px">
<img
src="https://www.scholcommlab.ca/wp-content/uploads/2019/08/emojis.jpg"
class="wp-image-2218 size-full" loading="lazy" decoding="async"
srcset="https://www.scholcommlab.ca/wp-content/uploads/2019/08/emojis.jpg 1024w, https://www.scholcommlab.ca/wp-content/uploads/2019/08/emojis-300x225.jpg 300w, https://www.scholcommlab.ca/wp-content/uploads/2019/08/emojis-768x576.jpg 768w"
sizes="(max-width: 1024px) 100vw, 1024px" width="1024" height="768"
alt="Image of emoji plushies" />
<figcaption><a
href="https://www.flickr.com/photos/38417869@N00/23979953332"
target="_blank" rel="noopener noreferrer">“Emojis”</a>by <a
href="https://www.flickr.com/photos/38417869@N00" target="_blank"
rel="noopener noreferrer">xelipe</a> is licensed under <a
href="https://creativecommons.org/licenses/by-nc-sa/2.0/?ref=ccsearch&amp;atype=html"
target="_blank" rel="noopener noreferrer">CC BY-NC-SA
2.0</a></figcaption>
</figure>

## **Why study emojis?**

Stefanie first decided to investigate emojis as part of a [larger
research project exploring how academic work is shared on
Twitter](https://arxiv.org/ftp/arxiv/papers/1806/1806.02201.pdf){target="_blank"
rel="noopener noreferrer"}. The goal was to understand what kinds of
scientific documents are tweeted most, as well as when, how, and by
whom. She hoped the emoji data would offer a more nuanced view of the
academic Twittersphere.

"The idea was to capture another level of meaning and emotion in the
tweets," she explains. "Emojis are a unique form of visual
communication---they have the potential to add to what's shared through
standard means, like words and hashtags, especially in short messages
like tweets."

Using data from Altmetric, she analyzed more than 40 million tweets
mentioning scientific journal articles, preprints, conference
proceedings, and other documents. Then, she analyzed each tweet for
emojis, noting both the type and number used. Here's what she found:

## **Less than 1% of scholarly tweets use emojis**

Although [10-15% of academics use
Twitter](https://onlinelibrary.wiley.com/doi/abs/10.1002/asi.23833){target="_blank"
rel="noopener noreferrer"}, only a small proportion rely on emojis. Of
the 42.5 million tweets Stefanie analyzed, only
286,087---0.7%---contained at least one emoji.

|           |                            |            |                 |                       |
|-----------|----------------------------|------------|-----------------|-----------------------|
| **Emoji** | **Category**               | **Tweets** | **Occurrences** | **Tweeted documents** |
| **👍**    | **Liking**                 | **15,688** | **19,018**      | **5,911**             |
| **👇**    | **Pointing**               | **13,432** | **20,308**      | **2,426**             |
| **➡️**    | **Pointing**               | **11,403** | **13,896**      | **2,415**             |
| **♀️**    | **Gender**                 | **10,983** | **13,071**      | **2,590**             |
| **👉**    | **Pointing**               | **8,523**  | **9,805**       | **2,056**             |
| **👏**    | **Liking**                 | **8,459**  | **16,436**      | **3,265**             |
| **💪**    | **Sports**                 | **6,899**  | **8,528**       | **1,746**             |
| **🔒**    | **Reading**                | **6,326**  | **7,063**       | **234**               |
| **❤️**    | **Liking**                 | **6,178**  | **7,597**       | **2,611**             |
| **😂**    | **Faces**                  | **5,505**  | **10,053**      | **3,006**             |
| **😉**    | **Faces**                  | **5,438**  | **5,923**       | **3,094**             |
| **😀**    | **Faces**                  | **5,312**  | **6,222**       | **2,728**             |
| **😊**    | **Faces**                  | **5,251**  | **5,996**       | **2,771**             |
| **✅**    | **Check**                  | **5,151**  | **7,860**       | **642**               |
| **🔬**    | **Science and technology** | **5,142**  | **5,782**       | **1,595**             |
| **👌**    | **Liking**                 | **4,665**  | **5,511**       | **1,958**             |
| **☺️**    | **Faces**                  | **4,628**  | **4,804**       | **1,277**             |
| **🚀**    | **Science and technology** | **4,285**  | **4,412**       | **3,470**             |
| **🏋️**    | **Sports**                 | **3,675**  | **4,443**       | **533**               |
| **😳**    | **Faces**                  | **3,613**  | **4,953**       | **1,396**             |

These results are surprising, given how popular emojis are beyond the
academic sphere. Researchers estimate that more than [90% of people have
used emojis in some form of digital
communication](https://www.adweek.com/digital/report-92-of-online-consumers-use-emoji-infographic/){target="_blank"
rel="noopener noreferrer"}. On Twitter, almost [1 in 5
tweets](https://www.aclweb.org/anthology/W16-2610){target="_blank"
rel="noopener noreferrer"} contain at least one.

Emojis aren't just popular; they're also powerful. Digital marketing
research has found that tweets with [emojis are more than 25% more
engaging](https://www.wordstream.com/blog/ws/2015/11/19/twitter-emoji){target="_blank"
rel="noopener noreferrer"} than those without. Using emojis in social
media can boost [click-through-rates, likes,
shares](https://blog.hubspot.com/marketing/best-emojis){target="_blank"
rel="noopener noreferrer"}, and more.

"I was really taken aback by how few tweets linking to academic papers
contained emojis," Stefanie says, "It made me wonder whether academics
are missing out."

However, academics may just be late to the game: the uptake of emojis
increased over the years from 0.02% in 2013 up to 1.57% in 2017 📈.

## **Academic emojis fall into 6 categories**

The tweets that *did* contain emojis followed very distinct patterns.
Among the 50 most tweeted emojis, most fell into one of the following
six categories:

### 1. Faces    (🤔😱😉😁) {#faces}

26 of the 100 most used emojis were face-based emojis. These tweets
expressed a range of emotions, likely to show the users' personal
opinion about the document they were sharing.

### 2. Liking (👍❤️👏) {#liking}

17% of the 100 most used emojis suggested a positive judgment of some
sort, usually of the scientific document being discussed.

### 3. Science and technology (🔬 🚀 🌏) {#science-and-technology}

13 out of 100 most tweeted emojis included scientific imagery, like
satellites or microscopes. These emojis were often content-specific---as
when rockets appeared in tweets about astrophysics research. Other
times, they played a more general role, simply highlighting the
scientific nature of the tweet.

### 4. Signs (🔴⚠️❗) {#signs}

8 out of 100 emojis were signs, which seemed intended to grab attention.

### 5. Pointing (👇➡️👉) {#pointing}

Gestures, like pointing fingers or arrows, featured in 6% of the 100
most-tweeted emojis. Many of these pointed to a link for the scientific
document being mentioned.

### 6. Reading (👀 📚 📄) {#reading}

Reading emojis, like books and documents, seemed to play a similar role
as the pointing emojis. They appeared 6 out of 100 times.

Many of the top scholarly emojis overlap with [those that are popular in
general](http://www.emojitracker.com/){target="_blank"
rel="noopener noreferrer"}. But some, like the emojis in the reading and
technology categories, seem to be particular to academia.

## **Different countries use emojis differently**

In addition to these general trends, Stefanie also identified distinct
patterns of emoji use between countries. That is, when sharing research
findings on Twitter, users from Brazil or Germany favoured different
emojis than those from Japan or Canada, for example.

Here are the top 10 emojis used by Brazil, Canada, Germany, the UK,
Japan, and the US:

![A chart of the top 10 emojis in Canada, Germany, the US, UK, Japan,
and
Brazil](https://www.scholcommlab.ca/wp-content/uploads/2019/08/Top-10-Emojis-by-Country.png){.size-full
.wp-image-2217 .alignnone loading="lazy" decoding="async" width="867"
height="623"
srcset="https://www.scholcommlab.ca/wp-content/uploads/2019/08/Top-10-Emojis-by-Country.png 867w, https://www.scholcommlab.ca/wp-content/uploads/2019/08/Top-10-Emojis-by-Country-300x216.png 300w, https://www.scholcommlab.ca/wp-content/uploads/2019/08/Top-10-Emojis-by-Country-768x552.png 768w"
sizes="(max-width: 867px) 100vw, 867px"}

Although it's not possible to draw firm conclusions about these
differences, these results do raise interesting questions. For example,
does the popularity of the lock emoji (🔒) in Brazil, Canada, and the US
suggest that conversations about paywalls and Open Access are
front-of-mind for these countries? Are questions about gender issues (♀️
and ♂️) more common in nations like Japan, Canada, and the US than in
others?

Exploring these questions is a task for future research. But, for now,
these findings offer a more nuanced understanding of emoji-based
communication in the scholarly sphere.

"It was fascinating to see how each country used emojis differently,"
Stefanie says. "No two were the same."

[Emojis may be a 'universal
language'](http://www.bbc.com/future/story/20151012-will-emoji-become-a-new-language){target="_blank"
rel="noopener noreferrer"}, but it seems to be spoken with a wide range
of accents, dialects, and expressions.

***To find out more about the project, check out Stefanie Haustein's
book chapter on [Scholarly Twitter Metrics, available as a preprint at
arXiv.org.](https://arxiv.org/ftp/arxiv/papers/1806/1806.02201.pdf){target="_blank"
rel="noopener noreferrer"}  ***


“Should there be an emoji for everything?” asked journalist Sophie Haigney in a recent
<em>
 New Yorker
</em>
article. “What, exactly, do we want from our emojis?”  The future of the emoji may be uncertain, but one thing is abundantly clear. Emojis are booming. From classic smiley faces to dancing “party parrots,” there are now almost 3,000 options to choose from. They fill our text messages, our Slack chats, our emails—even some of our books.

Emojis in Scholarly Communication: 🔥 or 💩?

Stephen Royle

Our lab is international. People born all over the world have come to
work in my group. I'm proud of this fact, especially in the current
political climate. I've previously used the GoogleMaps API to display a
heat map on our lab webpage. It shows where in the world people in the
lab come from. This was OK, but I wanted to get an R based solution to
make this graphic to make it easier to automate updates.

This post is an explainer and "how to" guide. Code and some example data
are [here](https://github.com/quantixed/Maps-N-Flags/).

The idea is to create graphics to describe the origins of a group of
people. For my use-case it is my research group, but it could be any
group of people. All you need is a list of countries that the people
come from.

In the example for this post, I took the [Top 100 Footballers voted for
by Guardian readers in
2016](https://docs.google.com/spreadsheets/d/12n0Nu9Uu7CCG_w4sTbhFzijeAKhMbXgkzxkRKlg7vb4/edit#gid=885154705).
In my lab dataset, I store the countries of origin in ISO2 format. This
means Spain is ES, Germany is DE and so on. I converted the Guardian
dataset to ISO2 format using a lookup and then I was ready to put it
into the R script.

::: {.wp-block-syntaxhighlighter-code}
``` {.brush: .r; .title: .; .notranslate title=""}
if (!require("rworldmap")) {
  install.packages("rworldmap")
  library(rworldmap)
}
# ggplot2, ggFlags, dplyr are needed for the bar charts
library(ggplot2)
library(dplyr)
if (!require("ggflags")) {
  devtools::install_github("rensa/ggflags")
  library(ggflags)
}

# csv file with each person as a row and containing a column with the header Origin and
# countries in 2-letter ISO format (change joinCode for other formats)
file_name <- file.choose()
df1 <- read.csv(file_name, header = TRUE, stringsAsFactors = FALSE)
countries_lab <- as.data.frame(table(df1$Origin))
colnames(countries_lab) <- c("country", "value")
matched <- joinCountryData2Map(countries_lab, joinCode="ISO2", nameJoinColumn="country")
```
:::

This part of the script sets up the libraries that are needed. We'll use
the
[rworldmap](https://cran.r-project.org/web/packages/rworldmap/index.html)
package first. This [post](https://www.springboard.com/blog/maps-in-r/)
was very useful for guidance. We load in the csv file which contains the
countries of origin for the people we want to map out. It doesn't need
anything more than one column with the ISO2 codes. If it does it's OK.
As long as the header for the countries column is called "Origin", all
will be OK.

This column is extracted and a new dataframe is made from it which has
each country as a row and the number of instances of that country as a
second column. These are labelled "country" and "value" for convenience.
Now rworldmap does its thing with the joinCountryData2Map line. Next we
make the map!

::: {.wp-block-syntaxhighlighter-code}
``` {.brush: .r; .first-line: .20; .title: .; .notranslate title=""}
# make png of the map
png(file = "labWorldMap.png",
    width = 1024, height = 768)
par(mai=c(0,0,0.2,0))
mapCountryData(matched,
               nameColumnToPlot="value",
               mapTitle= "",
               catMethod = "logFixedWidth",
               colourPalette = "heat",
               oceanCol="lightblue",
               missingCountryCol="white",
               addLegend = FALSE,
               lwd = 1)
dev.off()
```
:::

<figure class="wp-block-image">
<img
src="https://i2.wp.com/quantixed.org/wp-content/uploads/2019/03/labWorldMap.png?fit=640%2C480&amp;ssl=1"
class="wp-image-1786" loading="lazy" decoding="async"
data-attachment-id="1786"
data-permalink="https://quantixed.org/2019/03/20/all-around-the-world-maps-and-flags-in-r/labworldmap/"
data-orig-file="https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/labWorldMap.png?fit=1024%2C768&amp;ssl=1"
data-orig-size="1024,768" data-comments-opened="1"
data-image-meta="{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}"
data-image-title="labWorldMap" data-image-description=""
data-image-caption=""
data-medium-file="https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/labWorldMap.png?fit=300%2C225&amp;ssl=1"
data-large-file="https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/labWorldMap.png?fit=640%2C480&amp;ssl=1"
srcset="https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/labWorldMap.png?w=1024&amp;ssl=1 1024w, https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/labWorldMap.png?resize=300%2C225&amp;ssl=1 300w, https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/labWorldMap.png?resize=768%2C576&amp;ssl=1 768w"
sizes="(max-width: 640px) 100vw, 640px" width="1024" height="768" />
<figcaption>Where in the world…. heat map showing country of origin for
the people in the dataset</figcaption>
</figure>

This makes a nice map. I've hidden the legend which shows what the
colours mean. The map can be customised in lots of ways. I liked the way
this map looked and my other aim was to make a chart to show the
relative numbers of people in each country. Speaking of which...

::: {.wp-block-syntaxhighlighter-code}
``` {.brush: .r; .first-line: .34; .title: .; .notranslate title=""}
# make bar chart of lab members
countries_lab %>%
  mutate(code = tolower(country)) %>%
  ggplot(aes(x = reorder(country, value), y = value)) +
  geom_bar(stat = "identity") +
  geom_flag(y = -1, aes(country = code), size = 4) +
  scale_y_continuous(expand = c(0.1, 1)) +
  xlab("Country") +
  ylab("Members") +
  theme_bw() +
  theme(legend.title = element_blank()) +
  coord_flip()
ggsave("plot.png", plot = last_plot())
```
:::

Using the data frame we made previously, we can pipe it to ggplot2 via
the wonders of dplyr. I am using geom_flag here from the
[ggflags](https://github.com/rensa/ggflags) library. Note that this is a
fork of ggflags which gives circular flags which look great on the
graph. The geom_flag needs a lowercase entry for each ISO2 country code
so the first step is to mutate the country column to make a new
lowercase column called code.

<figure class="wp-block-image">
<img
src="https://i2.wp.com/quantixed.org/wp-content/uploads/2019/03/plot.png?fit=640%2C577&amp;ssl=1"
class="wp-image-1787" loading="lazy" decoding="async"
data-attachment-id="1787"
data-permalink="https://quantixed.org/2019/03/20/all-around-the-world-maps-and-flags-in-r/plot/"
data-orig-file="https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/plot.png?fit=1974%2C1779&amp;ssl=1"
data-orig-size="1974,1779" data-comments-opened="1"
data-image-meta="{&quot;aperture&quot;:&quot;0&quot;,&quot;credit&quot;:&quot;&quot;,&quot;camera&quot;:&quot;&quot;,&quot;caption&quot;:&quot;&quot;,&quot;created_timestamp&quot;:&quot;0&quot;,&quot;copyright&quot;:&quot;&quot;,&quot;focal_length&quot;:&quot;0&quot;,&quot;iso&quot;:&quot;0&quot;,&quot;shutter_speed&quot;:&quot;0&quot;,&quot;title&quot;:&quot;&quot;,&quot;orientation&quot;:&quot;0&quot;}"
data-image-title="plot" data-image-description="" data-image-caption=""
data-medium-file="https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/plot.png?fit=300%2C270&amp;ssl=1"
data-large-file="https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/plot.png?fit=640%2C577&amp;ssl=1"
srcset="https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/plot.png?w=1974&amp;ssl=1 1974w, https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/plot.png?resize=300%2C270&amp;ssl=1 300w, https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/plot.png?resize=768%2C692&amp;ssl=1 768w, https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/plot.png?resize=1024%2C923&amp;ssl=1 1024w, https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/plot.png?w=1280&amp;ssl=1 1280w, https://i0.wp.com/quantixed.org/wp-content/uploads/2019/03/plot.png?w=1920&amp;ssl=1 1920w"
sizes="(max-width: 640px) 100vw, 640px" width="1974" height="1779" />
<figcaption>Bar chart of the same dataset using flag emojis for the tick
labels</figcaption>
</figure>

That's it! With a csv file and a few lines of R code you can generate
some nice looking graphics.

The dataset shows that the country that produced the biggest fraction of
the world's best footballers (as voted for by Guardian readers) was
Spain. There are no surprises in this dataset. The most prominent
European and South American countries giving a strong showing.

---

The post title is taken from "All Around The World" by The Dead Milkmen.
Many songs in my library with this title, but this paranoid
extraterrestrial tune gets the pick this time.


Our lab is international. People born all over the world have come to work in my group. I’m proud of this fact, especially in the current political climate. I’ve previously used the GoogleMaps API to display a heat map on our lab webpage. It shows where in the world people in the lab come from. This was OK, but I wanted to get an R based solution to make this graphic to make it easier to automate updates.

Messaggi di Rogue Scholar

Emojis in Scholarly Communication: 🔥 or 💩?

All Around The World: Maps and Flags in R