How to Do Things without Words: Modeling Semantic Drift of Emoji (2022)

Abstract: Emoji have become a significant part of our informal textual communication. Previous work, addressing the societal and linguistic functions of emoji, overlooked the relation between the semantics and the visual variations of the symbols. In this paper we model and analyze the semantic drift of emoji and discuss the features that may be contributing to the drift, some are unique to emoji and some are more general. Specifically, we explore the relations between graphical changes and semantic changes.

## introduction emoji have become a significant part of our informal textual communication. one of the major linguistic functions served by emoji foot_0 is the re-introduction of physical-like gestures in textual messaging @xcite . while emoji is not a 'natural language' in the typical sense, it is a linguistic phenomena that can be addressed from a (computational-) linguistic perspective. moreover, the unique nature and inherent limitations of the use of emoji, provide a distinctive perspective for understanding various linguistic phenomena such as lexcicalization, gesturing, ambiguity and semantic drift. in this paper we focus on temporal distributional semantics of emoji. to the best of our knowledge, this is the first work to address semantic drift of emoji with respect to graphical variation across time and platform. the emoji echo-system the unicode consortium started adding emoji to the unicode standard at 2010, assigning a code to a short description foot_1 . for example, u+1f602 is described as face with tears of joy, u+1f9d0 as face with monocle, and the u+1f36a code is simply defined as cookie. the graphic realization of the description is left for the discretion of the designers of each specific platform. for example, the graphic interpretation of the cookie code (u+1f36a) ranges from variations of single chocolate-chip cookie to a pair of crackers: , , and ( ios 13.2, twitter's twemoji 12.2, and samsung touchwiz 7.1, respectively). the exact meaning of an emoji depends on the social and textual contexts in which it is used -the smiley emoji, for instance, can be used to show empathy, express amusement or to ease tension. the pistol emoji can be used to convey frustration or amusement ("you are killing me"), bragging ("i killed it") or a proper threat ("i will kill you") -a disturbing illocutionary act @xcite , performed without words. consequently, the accurate interpretation of an emoji may have significant legal implications, possibly landing a defendant accused of murder a life in jail, if the emoji is considered a threat -proving premeditation of the murder @xcite . similarly, if the semantics of the have drifted from a vegetable to a sexual reference (which is, indeed, the case), sending this emoji to a colleague could be perceived as a sexual misconduct. semantic drift language keeps changing. semantic drift, often referred to as semantic shift or semantic change, is the process in which words change their meaning over time @xcite . classic examples include 'gay', originally meaning 'happy' or 'cheerful', now used to describe a sexual orientation; and 'awful', originally meaning majestic, then used as 'horrible' before gaining the opposite meaning -'wonderful' @xcite . broadly speaking, a semantic shift is a grass-root process -a result of the way the speaker community use the language, rather than a shift forced by a regulatory body. that is, an institution (e.g., a corporation) may develop an 'anti-virus' (referring to a computer virus, rather than an infectious agent that replicates within a host organism) but speakers may not adopt the new use of the word. organized social groups can press for a change in the ways words should/not be used (e.g., negro, retard, or guys) but these efforts are not institutionalized. standardization enforced by language regulatory bodies tend to fail, as famously illustrated by the evolution of the french negation @xcite @xcite . emoji vs. written text emoji is unique in the way it combines four elements, each is governed by a different mechanism: (i) a subtle contextual meaning (like many proper words in the language), (ii) a strict "spelling" (the unicode code), (iii) a formal description (e.g., "face with tears of joy") decided by a regulatory body (the unicode consortium), and (iv) a flexible visual standard decided by any number of a third-party organizations. together, these elements restrict the ways in which emoji can be creatively manipulated by the users. while users can write 'coz' instead of the formal dictionary entry 'because', or 'on da fon wit ma ma' instead of "on the phone with my mother", they cannot be playful with the unicode of a specific smiley from the 'emoji dictionary'. creativity is typically achieved by emoji collocations and by allusions inspired by visual features. the eggplant emoji , for example, is commonly used as a sexual reference to the male genitalia (replacing the , previously used for the same purpose). recent works use neural models for learning emoji representation in a way useful for downstream tasks. a skip-gram model was used to analyze the use of emoji on instagram @xcite and twitter @xcite . emoji2vec @xcite demonstrates that learning the em-beddings based on the formal description of the unicode yeilds better representation than learning the embeddings using the emoji unicode as a textual token. deepmoji @xcite uses millions of tweets for training a multi-layer (bi-lstm with attention layers) network in learning embeddings for 64 popular emoji. the quality of the representation is reported across tasks and benchmarks (sentiment analysis, emotion detection, sarcasm detection). a cbow model was used in a comparative study of the use of emoji on twitter and sina-weibo @xcite . all of the works cited above assume a static meaning of the emoji, although ambiguity or cultural differences are acknowledged. few works do address changes in emoji usage. temporal variations in usage and popularity of emoji are addressed by @xcite . to the best of our knowledge, the only work to address semantic change of emoji from a linguistic perspective is @xcite . no prior work study the semantic change emoji with regards to visual differences and graphical changes across time and platforms. semantic drift semantic change is studied by linguists for decades, see (hock and joseph, 2009) for a survey. the availability of large textual corpora gave rise to the computational study of semantic change. early work used changes in word frequency in the span of two centuries, inferring grammatical and semantic shifts @xcite and the changing relations between syntax and meaning @xcite . the change in gender and racial stereotypes through the 20 th century was modeled by @xcite . other works use word embeddings to capture semantic change by measuring the mean-shift (kulkarni et al., 2015) or the spearman correlation between time series @xcite . the intersection between the sets of k nearest neighbors of a word w in different time spans is demonstrated to provide stable and interpretable results @xcite . the linguistic factors and processes that facilitate the semantic change include word frequency, polysemy, grammatical category, subjectification, and grammaticalization @xcite 3 data data collection using the twitter streaming api we collected 1% (daily) of all english tweets containing emoji between may 2016 and april 2019. in total, our data contained over 209 million tweets posted by 37 million unique users. tweets users all 209m 37m ios 113m 20m android 56m 13m web 33m 8.7m the longevity of the data covers multiple changes in the emoji visualizations across platforms, including some significant changes such as the pistol ( → ) and the cookie ( → ), among many other less pronounced changes, e.g., the rofl emoji ( → ). these changes provide the unique opportunity to study the effect of the visual realization on the semantics of emoji, as well as drifts unrelated to a forced visual change 3 . ## results and discussion using the method described in section 4.2, we identify dozens of drifting emoji. token before change after change rally politics, geo-locations college, sports florence tourism hurricanes, disaster boomer terror, gaming condescension, millennials anger, death, failure, gun rights games, gaming, online trends drug abuse, weapons medicine sweets, desserts baking, food expected drift following @xcite example's of twitter drifts (rally: @xmath0; sandy: @xmath1, we explicitly verified the existence of three assumed word drifts in our data: rally, florence foot_5 , and the recent [ok] boomer trend. indeed, we identified the drifts, as expected (see ## limitations and future work there is a number of limitations to this work, some are inherent to short papers. first, reporting on drift in a quantitative way (e.g., "we find n emoji drifting in our data" is hard and misleading due to the exploratory nature of the task and the fact that drift is a complex phenomenon -some drifts are cyclic and some are permanent; some are more pronounced and some are nuanced as the drift occurs on a specific sense of the emoji. we were trying to illustrate the various effects through qualitative examples. this approach is consistent with prior work, for example @xcite . second, the literature proposes a number of approaches to measure semantic drift (see section 2), each has its pros and cons. in this short paper we propose another measure, inspired by (gonen et al., 2020), and apply it on emoji. future work should address changes in words and study the differences between the different measures. another limitation of the proposed method is a result of the fixed embeddings used. an emoji may have multiple senses, some may drift while others may be stable. future work should account for the contextual differences in the use of an emoji within the basic time unit. finally, we study emoji since they provide a glimpse to the way visual changes affect semantics. however, some emoji went though major changes while other were changed slightly (compare the three visualizations , , for the same code u+1f36a). quantifying the visual distance is not a trivial task, although it may be required to get more accurate results (and see footnote 3). ## conclusion this work presents the first analysis of semantic drift of emoji, accounting for the graphical variability of emoji over time and platform. we proposed a novel statistical method for catching drifts and offered a substantive discussion of some of the factors that are unique to emoji use and to the modeling of drift in general. we note that the method we proposed is not specific to emoji and can be applied to any token, as demonstrated in 2 (top).