Lexicalization and compositionality of emoji

DOI

Emoji have been ubiquitous in communication for over a decade, yet how they derive meaning remains underexplored. Here we examine two aspects fundamental to linguistic meaning-making: the degree to which emoji have conventional lexicalized meanings (Experiments 1 & 2) and whether their combination allows for compositional meaning-making in linear strings compared to spatial pictures (Experiment 3). Experiment 1 established that participants have a range of agreement for the conventional meanings of emoji. Across Experiments 2 and 3, we measured accuracy and response times to word-emoji pairings in a match/mismatch task. In Experiment 2, we found that accuracy and response time both correlate significantly with the level of population-wide meaning agreement, suggesting that lexical access of single emoji may be comparable to that of words. In Experiment 3, however, presenting emoji-only expressions in a linear, written language-like sentence order incurred a processing cost compared to presenting the same expressions in a nonlinear analog depiction. Altogether, these findings suggest that emoji can allow a range of stored, lexicalized representations, yet they remain constrained in their combinatorial properties.

This study investigates the consistent meanings of emoji (Exp1), the entrenchment of participants' recognition of those emoji (Exp2), and whether emoji combinations vary in their comprehension based on whether they are presented as linear sequences or analog pictures (Exp3). The dataset includes data from three experiments as csv files, their analysis in R, example stimulus items from Experiment 3 embedded within a PDF document, and the "Emoji Language Fluency" questionnaire in a PDF document.

Identifier
DOI https://doi.org/10.34894/GRMYI6
Metadata Access https://dataverse.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34894/GRMYI6
Provenance
Creator Cohn, Neil ORCID logo; Weissman, Benjamin; Engelen, Jan ORCID logo; Thamsen, Lena; Baas, Elise
Publisher DataverseNL
Contributor Cohn, Neil; Tilburg University; DataverseNL
Publication Year 2022
Rights info:eu-repo/semantics/openAccess
OpenAccess true
Contact Cohn, Neil (Tilburg University, Tilburg School of Humanities and Digital Sciences)
Representation
Resource Type Experimental data; Dataset
Format type/x-r-syntax; application/pdf; text/csv; text/plain
Size 2089; 144252; 64595; 678557; 2639; 102041; 3785840; 2380818; 5603436; 1155
Version 1.0
Discipline Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Humanities; Life Sciences; Social Sciences; Social and Behavioural Sciences; Soil Sciences
Spatial Coverage Tilburg University