Proceedings of the 10th Web as Corpus Workshop (WAC-X) and the EmpiriST Shared Task, pages 82–89,Berlin, Germany, August 7-12, 2016. c©2016 Association for Computational Linguistics

A Global Analysis of Emoji Usage

Nikola LjubesicDept. of Knowledge Technologies

Jozef Stefan InstituteJamova cesta 39

SI-1000 Ljubljana, Slovenianikola.ljubesic@ijs.si

Darja FiserFaculty of Arts

University of LjubljanaAskerceva cesta 2

SI-1000 Ljubljana, Sloveniadarja.fiser@ff.uni-lj.si

Abstract

Emojis are a quickly spreading and ratherunknown communication phenomenonwhich occasionally receives attentionin the mainstream press, but lacks thescientific exploration it deserves. Thispaper is a first attempt at investigating theglobal distribution of emojis. We performour analysis of the spatial distribution ofemojis on a dataset of ∼17 million (andgrowing) geo-encoded tweets containingemojis by running a cluster analysisover countries represented as emojidistributions and performing correlationanalysis of emoji distributions and WorldDevelopment Indicators. We show thatemoji usage tends to draw quite a realisticpicture of the living conditions in variousparts of our world.

1 Introduction

Emojis, pictograms that have recently gained aworldwide momentum, are considered to be a fur-ther development of emoticons, pictorial repre-sentations of facial expressions using punctuationmarks. While the first days of emoticons go asfar as the 19th century (Fitzgerald, 2016), emo-jis were developed in the late 1990s by Shige-taka Kurita for Japanese mobile phone providers.The difference between emoticons and emojis isthat, while emoticons primarily express emotionalstates, emojis offer a wider spectrum of conceptssuch as animals, plants, weather, sports, food etc.

Emojis have been present in the Unicode stan-dard for some time now, with the first Unicodecharacters explicitly intended as emoji added toUnicode 5.2 in 2009. At that point a set of 722characters was defined as the union of emoji char-acters used by Japanese mobile phone carriers

(Davis and Edberg, 2015). Additional emoji char-acters followed in later updates, so that the cur-rent version 8.0 comprises 1624 emoji characters(Unicode Consortium, 2016). The current popu-larity of emojis is primarily due to the inclusion ofemoji characters on the iOS and Android mobileplatforms.

So far, emojis have primarily attracted main-stream media interest, the most prominent beingthe Word of the Year nomination handed by Ox-ford University Press in 2015 for the “Face WithTears of Joy” emoji. For this nomination Ox-ford University Press partnered with the companySwiftKey which is the author of the currently mostdetailed analysis of Emoji usage around the world(SwiftKey, 2015).

Despite their popularity, however, emojis arestill a poorly researched communication phe-nomenon as only a few study have focused on it.

Kralj Novak et al. (2015b) inspect the sentimentof emojis by manually annotating 70,000 tweetswritten in 13 European languages. Their workhas resulted in the Emoji Sentiment Ranking lex-icon (Kralj Novak et al., 2015a) consisting of 751emoji characters with their corresponding senti-ment distribution. The data the sentiment distri-butions were calculated on are also available fordownload (Mozetic et al., 2016).

Pavalanathan and Eisenstein (2016) investigatethe relationship between emojis and emoticons,showing that Twitter users who adopt emojis tendto reduce their usage of emoticons in comparisonwith the matched users who do not adopt emojis.

In this paper we will try to answer the followingquestions:

1. How popular are emojis in different parts ofthe world?

2. Does emoji usage differ in various parts ofthe world?

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3. Does emoji usage in specific parts of theworld reflect local living conditions?

We will answer these questions by performingthe following analyses over large collections ofgeo-encoded tweets:

• estimating the probability of emoji occur-rence in a tweet given the country,

• clustering countries represented as emojiprobability distributions,

• calculating correlation between World Devel-opment Indicators and distributions of spe-cific tweets across countries.

The remainder of the paper is structured as fol-lows: Section 2 describes the two datasets used inthe analyses while the remaining sections addressour three questions: Section 3 gives an analysis ofthe popularity of emojis in different parts of theworld, Section 4 gives an analysis of the spatialdistribution of specific tweets, while in Section 5we present the results of our correlation analysisover specific emojis and the World DevelopmentIndicators.

2 The datasets

2.1 Data collection

Our analyses in this paper are performed on twodatasets of tweets collected through the PublicTwitter Stream API1.

The first dataset consists of tweets that have lon-gitude and latitude encoded, regardless of whetherthey contain emojis. This dataset’s sole purposewas to estimate the probability of an emoji occur-rence in a specific part of the world. This datasetwas collected during a period of 21 days and con-tains 12,451,835 tweets. We refer to this datasetas the Twitter dataset.

The second dataset consists of tweets that havelongitude and latitude encoded and that containemojis. The purpose of this dataset was to esti-mate the probability distribution of specific emo-jis in different parts of the world. Since we needmore data to estimate the probability of an oc-currence of a specific emoji than the probabilityof the overall emoji occurrence, this dataset wascollected throughout a much longer period of 5

1https://dev.twitter.com/streaming/public

months (and is still running) and currently con-tains 17,458,001 tweets. We refer to this datasetas the Emoji dataset.

2.2 Removing overrepresented users

A frequent problem when analysing data from so-cial networks is the problem of bias towards userswith higher productivity, especially since the mostproductive users tend to be bots with a frequentand specific, if not static, content production.

We apply three methods of removing users withfrequent or temporally regular activity. All threemethods are run on our Emoji dataset which con-tains tweets of 2,623,645 users. The identifiedoverrepresented users are then removed both fromthe Twitter and the Emoji dataset.

The first method removes users who producedon average more than 10 tweets with emojis perday. With that approach we removed 42 users, theuser with the highest emoji productivity posting onaverage 509 tweets per day, the second one posting72 tweets per day.

Given that most of our later analyses are basedon comparing emoji distributions on country level,our second method removes tweets of users thathave contributed more than 10% of the tweets thatcontain emojis in a specific country. Through thisprocedure we assure that the emoji distribution ina specific country is not heavily influenced by asingle user.

We perform this procedure in an iterative man-ner, removing in each iteration all users that con-tribute to a specific country more than 10% of allits data points. After each iteration the distribu-tions of user contributions given the country arerecalculated. We should note that with this proce-dure we remove all users from countries that hadten or fewer contributors. With this method we re-moved 260 users.

The third method focuses primarily on remov-ing bots by calculating the time between two post-ings and removing all users for which the threemost frequent time spans between postings, cal-culated in seconds, cover more than 90% of theiroverall production. This method removed overall16 users from our datasets.

While the precision of all the three presentedmethods is very high, our assumption is that westill suffer from recall issues. Our plan is to focuson the problem of removing overrepresented / non-human users in more detail in future work.

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3 Overall emoji popularity

The analyses in this section are primarily focusedon how popular emojis are on Twitter. The firstpart of the analyses looks at the world as a whole,while the second one focuses on the distributionacross countries.

Given that for these analyses we need bothtweets with and without emojis, we perform allanalyses in this section on our Twitter dataset.

3.1 Global analysis

Emojis are present in nearly a quarter of the tweetsin the dataset (19.6%) and are used by well over athird of the users (37.6%). In this and the follow-ing analyses that are focused on users we take un-der consideration only the users with 100 or moretweets in our dataset as for the remaining users wedo not have enough data gathered to produce sta-ble estimates. There are 8,489 such users in ourTwitter dataset.

While we have already reported that 62.4%of the users do not use emojis, investigating theprobability distribution of using emojis in a tweetamong the remaining users shows that half of themuse emojis in up to 10% of the tweets while 75%use them in not more than 30% of the tweets.However, the distribution shows a surprisinglythick tail: while 5% of emoji users insert them inevery second tweet, 2% of users post less than oneemoji-less tweet in ten.

In the following analyses we investigate thedifferences between the emoji-using and emoji-abstaining users regarding their number of tweets,the number of tweets they have favourited, theirnumber of followers and friends (users that a userfollows). We compare the distributions of the fourvariables among the two types of users with theWilcoxon test as neither of the variables is nor-mally distributed. The null hypothesis assumesthat the median of the two distributions is zero.We always perform a one-tailed test.

By performing our tests on the median we ad-ditionally eliminate the impact of outliers whichis very beneficial given that our procedures for re-moving highly active and temporally regular usersdescribed in Section 2.2 were focused on emoji-producing users only.

The emoji-producing users have significantlymore followers (median 595 vs. 402) and friends(median 438 vs. 288), produce more tweets (me-dian 18280 vs. 12020) and favourite more tweets

(median 1760 vs. 1). All the obtained p-valueslie in the range p < 0.001. One should bear inmind that all the users taken under considerationare highly active on Twitter, producing in the timespan of 21 days on average five or more tweets perday.

We have also investigated the dependence of theamount of emojis a user produces and the remain-ing four variables we have at our disposal, butnone of the correlations were strong enough to beworth reporting.

Finally, looking into the number of emojis pertweet we find that single emojis occur in 45% ofthe emoji-containing tweets, two emojis make for25% of the tweets, three emojis 15%, four emojis7%, five emojis 3% and tweets with more than fiveemojis make 5% of all emoji-containing tweets.This distribution shows that in more than half ofthe tweets emojis occur with other emojis whichmakes a co-occurrence analysis as a method forobtaining an insight in the meaning of emojis (orrather the similarity of their meanings) very ap-pealing.

3.2 Per-country analysis

In this subsection we investigate the popularity ofemojis on a per-country basis. We quantify theemoji popularity in a specific country by calcu-lating the percentage of geo-encoded tweets thatcontain emojis. By calculating the percentage ofthe tweets containing emojis, and not the over-all amount of the emojis produced on Twitter, weneutralise the differences in popularity of Twitteramong different countries.

Emoji density by country is given in Figure1. The highest density of tweets can easily beobserved in Indonesia (46.5% of tweets contain-ing emojis) and the neighbouring third-rankingPhilippines (34.6%). In South America the kingof emojis, overall ranking second, is Paraguay(37.6%), followed by Argentina, overall rankingsixth (30.7%). In Africa emojis are most popularin the north, with Algeria ranking fourth (33.5%),Egypt ranking seventh (30.4%) and Libya rankingeight (29.7%). In the Arab peninsula Qatar comesfirst (overall ranking fifth, 32.6%), followed byUAE (ranking 10th, 27.1%). The two highestranking European countries are Latvia (24.4%)and Spain (24.1%), followed by the Czech Repub-lic, Portugal and the Russian Federation. Interest-ingly, Japan, the home of emojis, is ranked 163rd

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0 0.466

Figure 1: Emoji density per country measured as the percentage of tweets containing emojis

with only 7% of tweets containing emojis. TheUnited States of America, the country responsi-ble for making the pictograms widely popular, isjust doing slightly better, ranking 152nd with 10%of tweets containing emojis. The highest rankingNorth American state is Mexico (21.8%) in 37thposition.

Regarding the density of tweets on the con-tinent level, Asia has the highest density with26.3% tweets containing emojis, South Americacomes second with 20.9%, followed by Europe(16.7%), Africa (14.9%), Australia (13.7%) andNorth America (11.5%).

One has to stress right here that although thedataset used for estimating this distribution israther large, it is still collected from one sourceonly and therefore reflects the sociodemographicspecificities of Twitter users of a specific country.Investigating the reliability of these estimates cal-culated on one social network only is left for futurework.

4 Popularity of specific emojis

In this section we move from analysing the overallpopularity of emojis to analysing the popularity ofspecific emojis. Again we start with a global anal-ysis, continuing with a per-country one.

This set of analyses is performed on the Emojidataset as here we are not interested in the prob-ability of emoji occurrence, but the probability of

specific emojis among all of them. To estimatethese probabilities we do not require tweets thatdo not contain emojis.

4.1 Global analysis

The overall frequency distribution of emojis showsthat the most frequent emoji on Twitter since De-cember 2015, with around 2.6 million occurrencesin our Emoji dataset, is the “Face with tears of joy”

, representing 6.7% of all emoji occurrences.The second most frequent emoji is the “Smil-ing face with heart-shaped eyes” (3.72%), onthird place we find the “Emoji modifier Fitzpatricktype-1-2” 2 (2.3%), position 4 is taken by “Smil-ing face with smiling eyes” (2.1%), and posi-tion 5 by “Face throwing a kiss” (2.1%).

We give a full list of encountered emojis withtheir frequency and popularity across countries ina separate publication we call The Emoji Atlas.3

4.2 Per-country analysis

In this set of analyses we are interested in howpopular specific emojis are in individual coun-tries. We therefore calculate the probability dis-tribution of specific emojis for each country. Wediscard all the countries having less than 5000 data

2There are 5 emoji modifiers that define the skin tone ofthe emoji. In our analyses we consider these modifiers to beentities by themselves to achieve better generalisation bothamong modifiers and emojis.

3http://nlp.ffzg.hr/data/emoji-atlas/

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Figure 2: Results of the k-means algorithm on countries represented through the emoji probability dis-tribution

points from our analyses as the estimated distribu-tion of the 1282 emojis found in our data belowthis threshold would be quite unreliable. Whiledefining this frequency threshold we were not onlylead by the number of variables to be determined,but also by the percentage of countries left for ouranalysis, aiming at a decent global coverage. Byapplying the defined threshold we were left with108 out of 233 countries from which we collectedtweets in the 5-month period.

To obtain a first insight into the similarities anddifferences of emoji distributions among countrieswe ran the K-means clustering algorithm on coun-tries, each country represented by the emoji proba-bility distribution only. We ran the algorithm mul-tiple times on different numbers of clusters andconcluded for the 4-cluster division as presentedin Figure 2 to be most explanatory. Additionally,this clustering result has proven to be very stable.

We refer to the light red cluster covering NorthAmerica, Western Europe, the Russian Federation,and Australia as the “first world” cluster.

We call the blue cluster, covering most of SouthAmerica, India and China, Eastern Europe, Mo-rocco, Algeria and Tunisia the “second world”cluster.

The light blue cluster covering three Africanstates (Angola, Nigeria and Sudan), Jordan, SaudiArabia, Yemen, Pakistan, Nepal and the Philip-pines is referred to as the “third world” cluster.

The lilac cluster covering the remaining Africanstates with enough coverage we call the “fourthworld” cluster.

While most of the clustering decisions, besidesa few that should be inspected more carefully (likeChile belonging to the “first world” cluster), areself-explanatory, we were quite puzzled by theclustering algorithm to pick out Angola, Nigeriaand Sudan from the Sub-Saharan Africa and at-tach them to the cluster of less-fortunate Arab andAsian states. A short online search pointed to theircommon attribute: they have oil. The question re-mains whether the shift in the emoji distribution isdue to better living conditions of the local popu-lation in comparison to most other African statesor to the impact of the oil exploiters on the Twitteremoji production.

We analyse the difference between each clusterand the remaining world by calculating one arith-metic mean emoji vector for the cluster in ques-tion and another arithmetic mean emoji vector forthe remaining clusters. We then subtract the clus-ter vector from the remaining world vector and in-spect the 20 lowest dimensions, i.e. emojis thatare most distinctive for the cluster in question.The twenty most distinctive emojis per cluster aregiven in Table 1.

Interestingly, different to all other clusters, themost distinctive emojis in the “first world” clus-ter are not face emojis, the first one occurring on

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cluster most distinctive emojis“first world”“second world”“third world”“fourth world”

Table 1: Twenty most distinctive emojis by cluster

position 19, the “Sleeping face” emoji . Thetwo most distinctive emojis in the “first world”cluster are Emoji modifiers Fitzpatrick type 1-2

and 3 . The list is continued with a seriesof weather conditions like the sun and snow

, natural occurrences like “Palm tree” , differ-ent types of flowers (“Cherry blossom” , “Hibis-cus” ), “Sparkles” , and celebration symbolssuch as “Party popper” and “Christmas tree”

. The interesting feature of this cluster is thatit lacks direct emotions represented by face emo-jis that make the most distinctive emojis in all theremaining clusters.

The most distinctive “second world” clusteremojis are a series of happy faces like “Facethrowing a kiss” , “Smiling face with sun-glasses” , “Smiling face with open mouth andsmiling eyes” , “Smiling face with heart-shapedeyes” , “Winking face” and “Kissing facewith closed eyes” . They are interrupted with a“Kiss mark” , “Thumbs up sign” , “Sparklingheart” etc.

The 20 emojis most typical of the “third world”cluster contain some happy faces such as “Smilingface with heart-shaped eyes” and “Face withtears of joy” , but also unhappy ones, such as“Loudly crying face” , “Sleepy face” , “Un-amused face” and “Expressionless face” .Here the “Person with folded hands” makes itsfirst appearance in our analyses.

Besides two rather happy emojis, “Face withtears of joy” and “Smiling face with openmouth and cold sweat” , the “fourth world”cluster is specific for a series of unhappy facessuch as “Loudly crying face” , “Unamusedface” , “Crying face” and “Weary face” .In addition to these face emojis the “Fire” ,“Person raising both hands in celebration” and“Dancer” emojis are very distinctive as well.A series of emojis depicting people’s hands like“Raised hand” , “White down pointing back-hand index” , “White right pointing backhandindex” and “White up pointing backhand in-

dex” are also very specific. The list then con-tinues with a series of music-related emojis like“Multiple musical notes” , “Headphone” ,“Microphone” and “Musical note” (the lasttwo not making the top 20 emojis), the Emojimodifiers Fitzpatrick type 5 and 6 and “Per-son with folded hands” .

While skin modifiers make the list of most dis-tinctive emojis in three out of four clusters, theirpresence does not significantly affect the cluster-ing results as the same cluster structure was ob-tained when these modifiers were removed fromour dataset.

To wrap-up, the “first world” cluster seemsemotionally empty (or at least inexplicit) with al-most no face emojis and only two general celebra-tory emojis, the “second world” cluster is highlypositive with most distinctive emojis being thehappy face emojis. The dominating emojis in the“third world” cluster are a series of unhappy facesand the praying emoji, while the “fourth world”cluster adds a series of hand symbols (for whichwe do not have an explanation yet) and rather ba-sic symbols like fire, dance, music and celebration.

The already mentioned Emoji Atlas containsa full list of countries with their correspondingemoji distributions as well as a full list of emo-jis with their country distributions. We are confi-dent that this resource will be of great use for lesstechnically-oriented researchers who do not havethe opportunity to compile and process such bigdatasets. Additionally, as we continue to run thedata collection procedure, we will regularly up-date the resource and, with time, add the temporalcomponent as well.

5 Emojis and development indicators

In the last set of experiments we look into the cor-relation between the spatial representation of eachemoji and a selection of World Development In-dicators of the World Bank.4 Our spatial repre-

4http://data.worldbank.org

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sentation of an emoji consists of probabilities ofthe emoji given a country which makes it compa-rable to the World Development Indicators sincethey are calculated by country as well.

For this initial analysis we have selected WorldDevelopment Indicators for which we were intu-itively expecting results with a straight-forwardexplanation: “Life expectancy at birth, total(years)”, “Total tax rate (% of commercial prof-its)”, “Trade in services (% of GDP)” and “GDPper capita (current US$)”. Future work should in-clude a wider set of Indicators.

For each indicator we calculate the Pearson cor-relation coefficient with each of the emojis andrank them by absolute value, inspecting all emo-jis with a correlation higher than 0.4.

We again remove data from countries with lessthan 5000 tweets with emojis as we consider theprobability distribution of 1282 emojis calculatedon such little data to be insufficient for a good es-timate.

5.1 Life expectancy

The first indicator we take into account is the “Lifeexpectancy at birth, total (years)” indicator.5

The emoji with absolutely the highest correla-tion with this indicator is the frequently mentioned“Face with tears of joy” emoji (-0.675), sur-prisingly with a negative sign, meaning that thehigher the life expectancy, the lower the usage ofthe emoji. We have already observed this emoji tobe heavily used in our “third world” and “fourthworld” clusters.

The second and fourth absolutely highest corre-lations are the Emoji modifiers Fitzpatrick type 3(0.596) and type 1-2 (0.578), both occurring morefrequently as life expectancy rises. The third po-sition is taken by the “Confused face” emoji (-0.585), the fifth by the “Person with folded hands”

(-0.549), both occurring, as expected, more fre-quently as life expectancy shrinks.

“Dog face” and “Hot beverage” are fol-lowing emojis with positive correlation, whilethe strong ones with negative correlation are“Dancer” , “Fire” , “Baby symbol” and“Person raising both hands in celebration” , allof which have a correlation coefficient higher than0.5 which is considered to be a strong correlation.

5http://data.worldbank.org/indicator/SP.DYN.LE00.IN

5.2 Tax rateThe second indicator we consider is the “Total taxrate (% of commercial profits)” indicator.6

The only two emojis with a correlation above0.4 are “Thumbs down sign” (0.467) and “Pout-ing face” (0.461).

5.3 TradeOur third indicator is the “Trade in services (% ofGDP)” indicator.7

The three emojis with the highest correlationto this indicator are “Slot machine” (0.626),“Game die” (0.584) and “Speedboat”(0.579). Interestingly, there are no emojis with ahigh and negative correlation with this indicator.

5.4 GDP per capitaOur last indicator is the “GDP per capita (currentUS$)” indicator.8

The three emojis with the strongest correla-tion are “Emoji modifier Fitzpatrick type-3”(0.593), “Fork and knife with plate” (0.592)and “Bottle with popping cork” (0.565). Fur-ther positively strongly correlating emojis are“Airplane” and “Cooking” .

The emojis with the strongest negative correla-tion are “Unamused face” (-0.428) and “Cryingface” (-0.419).

6 Conclusion

In this paper we presented a worldwide spatialstudy of emoji usage by analysing a large datasetof geo-encoded tweets containing emojis. We de-picted the popularity of emojis on Twitter aroundthe world showing that they are most popular inSouth-Eastern Asia and South America, while inthe USA (that technically enabled the rise of emo-jis) and Japan (the origin of emojis) the usage fre-quency on Twitter is multiple times lower.

Inspecting the specificities of the countries re-garding the usage of different emojis, our countryclustering results differentiate between the “firstworld” cluster the most distinctive features ofwhich are rather emotionally empty, the “secondworld” cluster which is specific for highly pos-itive emotions, the “third world” cluster which

6http://data.worldbank.org/indicator/IC.TAX.TOTL.CP.ZS

7http://data.worldbank.org/indicator/BG.GSR.NFSV.GD.ZS

8http://data.worldbank.org/indicator/NY.GDP.PCAP.CD

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contains both positive and negative emotions, andthe “fourth world” cluster which is predominantlynegative with additional, rather basic concepts likefire, dance, music and hand gestures.

Finally, by performing a correlation analysis be-tween emoji distributions across countries and aseries of the World Development Indicators wehave shown that emojis with the strongest corre-lation clearly describe the indicator in questionwhich allows us to conclude that emoji usage is in-dicative of the living conditions in different partsof the world.

However, all our results are to be perceived byhaving in mind that only one social network wasused for building our datasets which opens the nat-ural question of data representativeness as (1) notall people use a specific social network and (2) dif-ferent sociodemographic groups use the same so-cial network in different countries. Nevertheless,this study objectively depicts the state in our socialnetwork of choice.

Our future work goes in three directions. Thefirst one is investigating the impact of using onlyone social network on the final results.

The second direction goes towards the under-standing of the meaning of emojis and using themfor tasks like sentiment identification, emotion de-tection etc. For unsupervised modelling of theemoji meaning we primarily consider distribu-tional models and emoji co-occurrence. We alsowish to investigate semantic shifts of emojis acrossspace. By continuous data collection, the tempo-ral dimension becomes a relevant focus of interestwith a series of similar research questions.

The third direction is aimed at understandinghow emojis are included in natural language syn-tax.

Acknowledgments

The research leading to these results has receivedfunding from the Swiss National Science Founda-tion grant IZ74Z0 160501 (ReLDI), and the Slove-nian Research Agency within the national basicresearch project “Resources, Tools and Methodsfor the Research of Nonstandard Internet Slovene”(J6-6842, 2014-2017).

ReferencesMark Davis and Peter Edberg. 2015. Unicode emoji.

Technical report, Unicode Consortium. http://unicode.org/reports/tr51/.

Britney Fitzgerald. 2016. Did Abra-ham Lincoln pioneer emoticons? 1862speech may offer clues. http://www.huffingtonpost.com/2012/09/19/abraham-lincoln-emoticons_n_1893411.html.

Petra Kralj Novak, Jasmina Smailovic, Borut Sluban,and Igor Mozetic. 2015a. Emoji Sentiment Rank-ing 1.0. Slovenian language resource repositoryCLARIN.SI.

Petra Kralj Novak, Jasmina Smailovic, Borut Sluban,and Igor Mozetic. 2015b. Sentiment of Emojis.PLoS ONE, 10(12).

Igor Mozetic, Miha Grcar, and Jasmina Smailovic.2016. Twitter sentiment for 15 European lan-guages. Slovenian language resource repositoryCLARIN.SI.

Umashanthi Pavalanathan and Jacob Eisenstein. 2016.Emoticons vs. Emojis on Twitter: A Causal Infer-ence Approach. In AAAI Spring Symposium on Ob-servational Studies through Social Media and OtherHuman-Generated Content.

SwiftKey. 2015. SwiftKey Emoji Report, April 2015.Technical report. https://goo.gl/9QXoEn.

The Unicode Consortium. 2016. Full emoji data.http://unicode.org/emoji/charts/full-emoji-list.html.

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