<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhou-etal-2026-adoption">
    <titleInfo>
        <title>From Adoption to Adaptation: Tracing the Diffusion of New Emojis on Twitter</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yuhang</namePart>
        <namePart type="family">Zhou</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xuan</namePart>
        <namePart type="family">Lu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Wei</namePart>
        <namePart type="family">Ai</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dallas</namePart>
            <namePart type="family">Card</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Anjalie</namePart>
            <namePart type="family">Field</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Katherine</namePart>
            <namePart type="family">Keith</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Mendelsohn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">San Diego</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-426-2</identifier>
    </relatedItem>
    <abstract>The frequent introduction of new emojis in each Unicode release creates a dynamic shift in social media content, providing a unique opportunity to explore the evolution of digital language. Analyzing a large dataset of sampled English tweets, we examine how newly released emojis gain popularity and evolve in meaning. We find that the community size of early adopters and emoji semantics are positively correlated with their popularity. Certain emojis experienced notable shifts in the meanings and sentiment associations during the diffusion process. Additionally, we propose a novel framework utilizing language models to extract words and pre-existing emojis with semantically similar contexts, which enhances the interpretation of new emojis. The framework demonstrates its effectiveness in improving downstream text classification performance by substituting unknown new emojis with familiar ones. This study offers a new perspective in understanding how new language units are adopted, adapted, and integrated into the fabric of online communication.</abstract>
    <identifier type="citekey">zhou-etal-2026-adoption</identifier>
    <location>
        <url>https://aclanthology.org/2026.nlpcss-1.17/</url>
    </location>
    <part>
        <date>2026-07</date>
        <extent unit="page">
            <start>293</start>
            <end>306</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T From Adoption to Adaptation: Tracing the Diffusion of New Emojis on Twitter
%A Zhou, Yuhang
%A Lu, Xuan
%A Ai, Wei
%Y Card, Dallas
%Y Field, Anjalie
%Y Keith, Katherine
%Y Mendelsohn, Julia
%S Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego
%@ 979-8-89176-426-2
%F zhou-etal-2026-adoption
%X The frequent introduction of new emojis in each Unicode release creates a dynamic shift in social media content, providing a unique opportunity to explore the evolution of digital language. Analyzing a large dataset of sampled English tweets, we examine how newly released emojis gain popularity and evolve in meaning. We find that the community size of early adopters and emoji semantics are positively correlated with their popularity. Certain emojis experienced notable shifts in the meanings and sentiment associations during the diffusion process. Additionally, we propose a novel framework utilizing language models to extract words and pre-existing emojis with semantically similar contexts, which enhances the interpretation of new emojis. The framework demonstrates its effectiveness in improving downstream text classification performance by substituting unknown new emojis with familiar ones. This study offers a new perspective in understanding how new language units are adopted, adapted, and integrated into the fabric of online communication.
%U https://aclanthology.org/2026.nlpcss-1.17/
%P 293-306

Markdown (Informal)

[From Adoption to Adaptation: Tracing the Diffusion of New Emojis on Twitter](https://aclanthology.org/2026.nlpcss-1.17/) (Zhou et al., NLP+CSS 2026)

• From Adoption to Adaptation: Tracing the Diffusion of New Emojis on Twitter (Zhou et al., NLP+CSS 2026)

ACL

• Yuhang Zhou, Xuan Lu, and Wei Ai. 2026. From Adoption to Adaptation: Tracing the Diffusion of New Emojis on Twitter. In Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science, pages 293–306, San Diego. Association for Computational Linguistics.