Linguis ic Fo um
Published by
MARS Publishe s
Published by Licensee MARS Publishe s. Copy igh : © he au ho (s). This a icle is an open access a icle
dis ibu ed unde he e ms and
condi ions o he C ea i e Commons A ibu ion (CC BY) license
(h ps://c ea i ecommons.o g/licenses/by/4.0/).
Emojis, Hash ags, and Code
A Li e a y
Mul imodal Digi al Tex uali y
Co esponding Au ho :
Seha Ja
been
<
seha jabeen@gcu .edu.pk
Seha Khalid
<
khalidseha [email protected]
Zak a Nadeem
<
zak [email protected]
Recei ed: Sep embe 10, 2025
Abs ac
This a icle examines emojis, hash ags, and code
i s -
cen u y digi al communica ion a he han pe iphe al embellishmen s. D awing on a
quali a i e mul imodal discou se analysis, we analyze a pu pose ully sam
pos s om Twi e /X and Ins ag am (2021
Wo kLi eBalance, and S uden Li e
; pos s we e included i hey con ained a leas one emoji and
e idence o a hash ag o code-
swi ching. Analysis p oce
linguis ic/p agma ic unc ions o each esou ce; (2) in e p e ing hei aes he ic and li e a y
a o dances; and (3) si ua ing hei in e ac ion wi hin b oade sociocul u al amewo ks. Findings
indica e ha emojis
p ima ily index s ance and one
i ony—
while hash ags ope a e as indexical “ e ains” ha s uc u e pa icipa ion and in e ex ual
linkage; in asen en ial and ag-
le el code
ISSN (Online) 2707
Volume
h p://doi.o g/
10.5281/zenodo.17308022
Published by Licensee MARS Publishe s. Copy igh : © he au ho (s). This a icle is an open access a icle
condi ions o he C ea i e Commons A ibu ion (CC BY) license
(h ps://c ea i ecommons.o g/licenses/by/4.0/).
Emojis, Hash ags, and Code
-
Swi ching:
A Li e a y
-
Linguis ic Analysis o
Mul imodal Digi al Tex uali y
Resea ch A icle
been
seha ja[email p o ec ed]
>
PhD
Schola , Depa men o Applied
Linguis ics, Go e nmen College Uni e si y,
Faisalabad, Punjab, Pakis an.
Seha Khalid
khalidse[email p o ec ed]
>
MPhil Schola ,
Depa men o Applied
Linguis ics, Go e nmen College Uni e si y,
Faisalabad, Punjab,
Pakis an.
Zak a Nadeem
zak [email protected]
>
MPhil Schola , Depa men o English,
Riphah In e na ional Uni e si y
Pakis an.
Publica ion De ails
Accep ed: Oc obe 5, 2025
Published:
This a icle examines emojis, hash ags, and code
-
swi ching as co e semio ic esou ces in wen y
cen u y digi al communica ion a he han pe iphe al embellishmen s. D awing on a
quali a i e mul imodal discou se analysis, we analyze a pu pose ully sam
pled co pus o 300 public
pos s om Twi e /X and Ins ag am (2021
–
2023) selec ed ia he keywo ds
; pos s we e included i hey con ained a leas one emoji and
swi ching. Analysis p oce
eded in h ee s ages: (1) iden i ying he
linguis ic/p agma ic unc ions o each esou ce; (2) in e p e ing hei aes he ic and li e a y
a o dances; and (3) si ua ing hei in e ac ion wi hin b oade sociocul u al amewo ks. Findings
p ima ily index s ance and one
—
including pa e ned uses o a ec and
while hash ags ope a e as indexical “ e ains” ha s uc u e pa icipa ion and in e ex ual
le el code
-
swi ching, in u n, pe o ms iden i y wo
ISSN (Online) 2707
-5273
Volume
7, Issue 1, 2025
10.5281/zenodo.17308022
Pages 38-51
Published by Licensee MARS Publishe s. Copy igh : © he au ho (s). This a icle is an open access a icle
condi ions o he C ea i e Commons A ibu ion (CC BY) license
Swi ching:
Linguis ic Analysis o
Mul imodal Digi al Tex uali y
Schola , Depa men o Applied
Linguis ics, Go e nmen College Uni e si y,
Faisalabad, Punjab, Pakis an.
Depa men o Applied
Linguis ics, Go e nmen College Uni e si y,
Pakis an.
MPhil Schola , Depa men o English,
Riphah In e na ional Uni e si y
, Faisalabad,
Published:
Oc obe 10, 2025
swi ching as co e semio ic esou ces in wen y
-
cen u y digi al communica ion a he han pe iphe al embellishmen s. D awing on a
pled co pus o 300 public
2023) selec ed ia he keywo ds
MeToo,
; pos s we e included i hey con ained a leas one emoji and
eded in h ee s ages: (1) iden i ying he
linguis ic/p agma ic unc ions o each esou ce; (2) in e p e ing hei aes he ic and li e a y
a o dances; and (3) si ua ing hei in e ac ion wi hin b oade sociocul u al amewo ks. Findings
including pa e ned uses o a ec and
while hash ags ope a e as indexical “ e ains” ha s uc u e pa icipa ion and in e ex ual
swi ching, in u n, pe o ms iden i y wo
k and audience
Emojis, Hash ags, and Code-Swi ching. . . LinFo
www.linguis ic o um.com 39 Linguis ic Fo um 7(1), 2025
design, p oducing polyphonic, hyb id u e ances. Taken oge he , hese esou ces o eg ound he
mul imodal, pa icipa o y cha ac e o con empo a y ex uali y, complica ing canonical
dis inc ions be ween speech and w i ing. The s udy a gues o ea ing online discou se as bo h a
linguis ic phenomenon and a e nacula li e a y o m, and ou lines implica ions o sociolinguis ics
(in eg a ing anslanguaging and mul imodali y in o analy ic uni s) and li e a y s udies (ex ending
close eading o ne wo ked, epheme al ex s).
Keywo ds: emojis, hash ags, code-swi ching, mul imodali y, anslanguaging, digi al discou se,
e nacula li e a u e
1. In oduc ion
Digi al communica ion inc easingly elies on mul imodal esou ces ha combine w i en language
wi h isual and pa alinguis ic cues. Emojis, hash ags, and code-swi ching a e no longe pe iphe al
embellishmen s bu ou ine means o shaping s ance, o ganizing pa icipa ion, and pe o ming
iden i y ac oss pla o ms such as Twi e /X, Ins ag am, and Wha sApp (K ess, 2010; Lyons, 2024;
Kon ad e al., 2020). Linguis ic esea ch has documen ed how hese esou ces ope a e
p agma ically—indexing one, alignmen , and audience—while cul u al and li e a y pe spec i es
highligh hei aes he ic and na a ological e ec s in e e yday ne wo ked w i ing (K ess, 2010;
Lyons, 2024). Fo ins ance, empi ical wo k shows ha emojis e icien ly signal in en ion and a ec ,
hough hei in e p e a ion depends on con ex and in e ac ional no ms (Thompson & Filik, 2016;
S a k & C aw o d, 2015; Lim, 2015; Yang, 2024; Aljasi e al., 2024; Ca alhei o e al., 2024).
Hash ags, meanwhile, unc ion as o ganiza ional and connec i e de ices ha ie u e ances in o
sea chable publics and p o essional o poli ical discou se (Enli, 2018; Sinpeng e al., 2021). In
pa allel, anslanguaging and code-swi ching p ac ices online pe o m iden i y and audience design,
p oducing hyb id, polyphonic u e ances (Hua, 2022; Mon es-Alcalá, 2024).
Two gaps mo i a e he p esen inqui y. Fi s , exis ing schola ship o en ea s emojis, hash ags, and
code-swi ching in isola ion, o e ing esou ce-speci ic ypologies a he han an in eg a ed accoun
o how hese semio ic cues co-ope a e wi hin single pos s (c . Kon ad e al., 2020; Thompson &
Filik, 2016; Enli, 2018). Second, despi e ecogni ion ha pla o m logics s uc u e isibili y and
up ake, he e emains limi ed c oss- esou ce analysis g ounded in au hen ic pos s ha o eg ounds
bo h communica i e and aes he ic e ec s (Enli, 2018; Sinpeng e al., 2021). Add essing hese gaps,
he a icle adop s an in e disciplina y lens ha b ings oge he sociolinguis ic concep s—
indexicali y, s ance, audience design, and anslanguaging—wi h li e a y-c i ical no ions o oice,
e ain, in e ex uali y, and polyphony o examine how meaning is co-p oduced in e e yday digi al
ex s (Hua, 2022; Lyons, 2024; Sinpeng e al., 2021).
Me hodologically, he s udy employs quali a i e mul imodal discou se analysis o a pu pose ully
sampled co pus o public pos s om Twi e /X and Ins ag am (2021–2023). Pos s we e included i
hey con ained a leas one emoji alongside ei he a hash ag o e idence o code-swi ching.
Analysis p oceeded in h ee s ages: (1) iden i ying he p agma ic unc ions o each esou ce wi hin
pos s; (2) in e p e ing hei aes he ic a o dances and ela ions (e.g., how hash ags sca old hema ic
cohesion while emojis in lec one); and (3) si ua ing hese ela ions wi hin wide sociocul u al
ames, including pa icipa ion in issue publics and he pe o mance o iden i y (Enli, 2018;
Emojis, Hash ags, and Code-Swi ching. . . LinFo
www.linguis ic o um.com 40 Linguis ic Fo um 7(1), 2025
Sinpeng e al., 2021). E hical p ocedu es o wo king wi h public social-media da a and
anonymiza ion guidelines we e obse ed.
This design enables he a icle o a icula e bo h linguis ic and li e a y payo s. Linguis ically, i
demons a es how emojis ou inely index s ance and onal nuance, how hash ags coo dina e
alignmen and up ake by linking u e ances ac oss ne wo ks, and how code-swi ching—
in asen en ial and ag-le el—indexes audience, expe ise, and a ilia ion (Thompson & Filik, 2016;
S a k & C aw o d, 2015; Lim, 2015; Enli, 2018; Hua, 2022; Mon es-Alcalá, 2024). F om a li e a y
pe spec i e, he analysis shows how hese esou ces s uc u e hy hm and oice, c ea e e ains and
mo i s, and gene a e polyphonic ex u es ha complica e bounda ies be ween speech and w i ing,
p i a e exp ession and public ci cula ion (K ess, 2010; Lyons, 2024).
The con ibu ion is he e o e wo old. Empi ically, he a icle o e s an in eg a ed accoun o h ee
high- equency esou ces as hey co-occu in na u ally occu ing pos s, mo ing beyond single-
esou ce ypologies (Kon ad e al., 2020; Enli, 2018). Concep ually, i ad ances an in e disciplina y
amewo k ha ea s social-media discou se as a e nacula li e a y o m while e aining he
analy ical p ecision o linguis ic p agma ics and anslanguaging esea ch (Hua, 2022; Lyons, 2024;
Mon es-Alcalá, 2024). This in eg a ed pe spec i e cla i ies how meaning is made in pla o med
en i onmen s and p o ides ools o close eading o e e yday digi al ex s.
2.1 Resea ch Ques ions
1. How do emojis, hash ags, and code-swi ching unc ion indi idually and join ly o index
s ance, o ganize pa icipa ion, and pe o m iden i y in social-media pos s?
2. In wha ways do hese esou ces in e ac o p oduce aes he ic and na a ological e ec s
(e.g., oice, e ain, polyphony) ha wa an eading digi al discou se as e nacula
li e a u e?
3. Wha implica ions ollow o sociolinguis ic analysis (e.g., uni s o analysis ha include
mul imodal cues and anslanguaging) and o li e a y s udies (e.g., ex ending close eading
o epheme al, ne wo ked ex s)?
2. Li e a u e Re iew
This e iew b ings oge he igo ous s udies on h ee widely used semio ic esou ces in digi al
discou se—emojis, hash ags, and code-swi ching/ anslanguaging. We p oceed om ounda ional
accoun s o mul imodali y o ocused s ands on each esou ce, hen in eg a e hese s ands o
su ace p ecise gaps ha mo i a e ou h ee esea ch ques ions. The guiding p emise is ha
pla o med discou se is bo h linguis ic in e ac ion and e nacula ex uali y, equi ing an analysis
ha is a once p agma ic and aes he ic (K ess, 2010; Lyons, 2024; Kon ad e al., 2020; Enli, 2018;
Sinpeng e al., 2021; Thompson & Filik, 2016; S a k & C aw o d, 2015; Lim, 2015; Yang, 2024;
Ca alhei o e al., 2024; Aljasi e al., 2024; Hua, 2022; Mon es-Alcalá, 2024).
2.1 Mul imodali y and Ve nacula Tex uali y
Founda ional mul imodali y esea ch shows ha meaning in digi al en i onmen s is co-p oduced
by ex ual, isual, and pa alinguis ic esou ces a he han alphabe ic language alone (K ess, 2010).
Emojis, Hash ags, and Code-Swi ching. . . LinFo
www.linguis ic o um.com 41 Linguis ic Fo um 7(1), 2025
Recen applied-linguis ic wo k on “pos -digi al” li e o eg ounds how pla o m a o dances and
o line p ac ices join ly shape p oduc ion and in e p e a ion (Lyons, 2024). Toge he , hese
pe spec i es jus i y eading social-media pos s as e nacula ex ual a e ac s ha wa an bo h
linguis ic analysis and o ms o close eading— aming esea ch ques ion 2 (how esou ce
in e ac ions yield aes he ic/na a ological e ec s) and esea ch ques ion 3 (implica ions o analy ic
uni s and in e p e i e p ocedu es).
2.2 Emojis as Indexical, A ec i e, and In e ac ional Cues
Empi ical s udies show ha emojis e icien ly signal in en ion and s ance, aiding onal
disambigua ion in w i en in e ac ion (Thompson & Filik, 2016). Schola ship also cau ions ha
emoji meanings a e nego ia ed and communi y-dependen , no ixed lexical subs i u es (S a k &
C aw o d, 2015; Lim, 2015). Newe SAGE wo k deepens his accoun : smiling- ace a ian s as
socially si ua ed cues (Yang, 2024), ela ional ou comes o ecip ocal emoji use (Ca alhei o e al.,
2024), and social p esence among A ab use s (Aljasi e al., 2024). These s udies speci y wha
emojis do indi idually—a base o esea ch ques ion 1—and indica e how hey ex u e oice and
hy hm when combined wi h o he esou ces— eeding esea ch ques ion 2.
2.3 Hash ags, Sea chabili y, and Publics
Hash ags unc ion simul aneously as me ada a and he o ic: hey ende pos s disco e able, bundle
dispe sed u e ances in o con e sa ions, and signal pa icipa ion ames (Enli, 2018). Wo k on
hash ag ac i ism shows how ags ec ui a en ion, sca old collec i e na a ion, and align iden i ies
in con en ious publics (Sinpeng e al., 2021). These indings cla i y indi idual hash ag unc ions
pe inen o esea ch ques ion 1 and sugges how hash ags ope a e as e ains o mo i s ac oss pos s,
shaping aes he ic pa e ning ele an o esea ch ques ion 2.
2.4 T anslanguaging, Code-Swi ching, and Audience Design
Language choice online pe o ms iden i y and audience design. Hua (2022) heo izes
anslanguaging as pe o ma i e, unse ling code-bounded iews o mul ilingualism.
Complemen ing his, MDPI esea ch on Spanish–English ex ing in emoji- ich con ex s shows how
in asen en ial and ag-le el swi ching coope a es wi h pa alinguis ic ma ke s o accomplish
nuanced social wo k (Mon es-Alcalá, 2024). These accoun s posi ion code-mixing as in eg al—no
ancilla y— o how use s cho eog aph oice, nego ia e expe ise, and signal a ilia ion wi hin single
pos s, di ec ly in o ming esea ch ques ion 1 and b idging o esea ch ques ion 2.
2.5 In eg a ed Accoun s: he Speci ic Gap
Ac oss s ands, consensus holds ha (i) emojis a e lexible indexical cues (Thompson & Filik, 2016;
S a k & C aw o d, 2015; Lim, 2015; Yang, 2024; Ca alhei o e al., 2024; Aljasi e al., 2024); (ii)
hash ags o ganize pa icipa ion and publics (Enli, 2018; Sinpeng e al., 2021); and (iii)
anslanguaging/code-swi ching pe o ms iden i y and audience design (Hua, 2022; Mon es-
Alcalá, 2024). Wha emains sca ce is in eg a ed, wi hin-pos analyses acing how hese esou ces
co-ope a e in sequence o yield bo h communica i e and aes he ic e ec s. Much exis ing wo k
p i ileges single- esou ce ypologies o decon ex ualized illus a ions, lea ing he in e play among
Emojis, Hash ags, and Code-Swi ching. . . LinFo
www.linguis ic o um.com 42 Linguis ic Fo um 7(1), 2025
emojis, hash ags, and code-swi ching unde -speci ied. This gap mo i a es esea ch ques ion 1 (a
join unc ional accoun ) and esea ch ques ion 2 ( hei in e ac ional–aes he ic payo s).
2.6 F om E idence o Analy ical Payo s
Because mul imodali y and pos -digi al accoun s imply ha analy ic uni s mus ma ch how use s
ac ually compose pos s, he e is a me hodological impe a i e o in eg a e p agma ic and li e a y
lenses. Doing so has implica ions o sociolinguis ics (uni s o analysis ha include mul imodal
cues and anslanguaging) and o li e a y s udies (ex ending close eading o ne wo ked mic o-
ex s). Making hese implica ions explici and ope a ional mo i a es esea ch ques ion 3.
2.7 Posi ioning he P esen S udy
To add ess hese aligned gaps, we adop quali a i e mul imodal discou se analysis o au hen ic
pos s (2021–2023) o:
Model how emojis, hash ags, and code-swi ching unc ion indi idually and join ly o index
s ance, o ganize pa icipa ion, and pe o m iden i y—in eg a ing insigh s om Thompson &
Filik (2016), Enli (2018), and Hua (2022).
Explain how co-deploymen yields aes he ic/na a ological e ec s— oice, e ain,
polyphony—connec ing pla o med discou se (Enli, 2018; Sinpeng e al., 2021) wi h
mul imodali y and pos -digi al heo y (K ess, 2010; Lyons, 2024).
A icula e implica ions o sociolinguis ic and li e a y analysis by p oposing in eg a i e
analy ic uni s and in e p e i e p ocedu es aligned wi h ac ual composi ion p ac ices
(Kon ad e al., 2020; Lyons, 2024).
The sec ion p o ides obus esou ce-speci ic indings bu unde - heo izes hei join wo k in
na u ally occu ing pos s. By mapping esea ch ques ion 1, 2 and 3 di ec ly on o hese lacunae, his
s udy o e s a heo e ically cohe en and empi ically anspa en accoun o how pa e ned
combina ions o emojis, hash ags, and code-swi ching yield communica i e e ec s (s ance,
alignmen , audience design) and e nacula li e a y e ec s (mo i , hy hm, polyphony) wi hin he
same mic o- ex s—cla i ying how meaning is made, and made legible, in pla o med discou se.
3. Me hodology
3.1 Resea ch Design and Analy ical S ance
This s udy employs a quali a i e mul imodal discou se analysis wi h an in e p e i e–abduc i e
logic. The ocus is on how emojis, hash ags, and code-swi ching ope a e as semio ic esou ces
wi hin na u ally occu ing social-media pos s and how hei co-deploymen yields communica i e
(p agma ic) and aes he ic (na a ological) e ec s. The analy ical lens in eg a es sociolinguis ic
cons uc s (indexicali y, s ance, audience design, anslanguaging) wi h li e a y concep s ( oice,
e ain, polyphony). Insigh s a e de eloped i e a i ely h ough mo emen be ween exce p s and
heo y.
Emojis, Hash ags, and Code-Swi ching. . . LinFo
www.linguis ic o um.com 43 Linguis ic Fo um 7(1), 2025
3.2 Da a and Sampling
The co pus comp ises publicly accessible pos s om Twi e /X and Ins ag am, selec ed h ough
pu posi e maximum- a ia ion sampling cen e ed on h ee high-signal en y poin s ha consis en ly elici
mul imodal exp ession—#MeToo, #Wo kLi eBalance, and #S uden Li e. Eligible pos s me all
inclusion c i e ia: (a) public a ailabili y; (b) a leas one emoji; and (c) ei he a leas one hash ag o
clea e idence o code-swi ching (in a- o in e -sen en ial, o ag-le el). We excluded con en om
p i a e/p o ec ed accoun s, media-only pos s wi hou accompanying ex , comme cial spam/ad e o ials,
and exac duplica es. The inal da ase o als N = 300 pos s—150 om Twi e /X and 150 om
Ins ag am—balanced ac oss opics, wi h no mo e han h ee pos s con ibu ed by any single accoun o
minimize use -le el clus e ing. Fo each pos , we s o ed he ex ual con en and ligh me ada a (pla o m,
language[s], and coun s o emojis/hash ags) and a chi ed sc eensho s o ensu e audi abili y; all
iden i ie s (handles, URLs, and p ecise imes amps) we e emo ed o masked a he poin o analysis.
3.3 Ope a ionaliza ion
In his s udy, emoji a e ea ed as Unicode pic og aphs embedded in he ex ual s eam o modula e one,
s ance, and alignmen ; a hash ag is de ined as any oken beginning wi h # ha indexes opic, signals
s ance, o ma ks a pa icipa ion ame; and code-swi ching e e s o he al e na ion o linguis ic codes
wi hin o ac oss clauses and/o hash ags, iden i ied h ough o hog aphic, sc ip , and lexical cues
(including s ylized loan o ms).
3.4 Analy ic P ocedu e
Analysis p oceeded in h ee s ages; each s age explici ly add esses he esea ch ques ions.
S age 1 — Resou ce-Speci ic Coding
Each pos was anno a ed o he unc ions o each esou ce using non-exclusi e labels:
Emoji: s ance/a ec , i ony, emphasis, mi iga ion, alignmen cue.
Hash ag: opical index, pa icipa ion ame, alignmen signal, e ain/mo i ,
me acommen a y.
Code-swi ching: audience design, iden i y display, expe ise/indexicali y, play/quo a ion.
S age 2 — Rela ional Analysis
Wi hin-pos sequencing and co-occu ence we e analyzed o model how esou ces in e ac .
Aes he ic/na a ological ou comes— oice ( onal ex u ing), e ain ( epea ing hash ag mo i s), and
polyphony (mul i oicedness ia swi ching)—we e linked back o he p agma ic unc ions iden i ied
in S age 1.
Emojis, Hash ags, and Code-Swi ching. . . LinFo
www.linguis ic o um.com 44 Linguis ic Fo um 7(1), 2025
S age 3 — Pa e n Syn hesis and Theo iza ion
C oss-case ma ices ( esou ce × unc ion × pla o m/ opic) su aced ecu en con igu a ions (e.g.,
emoji-as-mi iga ion + hash ag-as- e ain + ag-le el swi ching). F om hese, he s udy a icula es
implica ions o (a) uni s o analysis in sociolinguis ics (mul imodal cue bundles) and (b) li e a y
close eading (mo i / hy hm a mic o- ex scale).
3.5 Reliabili y and Analy ic Rigo
Fi s , o es ablish sha ed in e p e i e no ms, we ini ia ed code aining, du ing which wo code s
independen ly anno a ed a 20% pilo subse (n = 60). A e wo calib a ion ounds, K ippendo ’s
α (nominal) eached ≥0.75 o esou ce iden i ica ion and ≥0.70 o unc ion labels— igu es ha we
ega d as accep able o in e p e i e quali a i e wo k. Whe e disag eemen s pe sis ed, hey we e
adjudica ed, and he codebook was e ined acco dingly, ensu ing ha subsequen coding p oceeded
on a mo e s able oo ing.
Nex , o enhance c edibili y, we implemen ed analys iangula ion ( wo code s plus he au ho ),
main ained i e a i e memos o make in e p e i e mo es explici , and conduc ed nega i e-case
analysis o p obe and, whe e necessa y, e ise eme gen claims. Impo an ly, hese s a egies we e
applied h oughou he analysis a he han con ined o an ini ial pilo , he eby sus aining igo
ac oss he co pus.
Finally, o secu e audi abili y, we main ained e sioned codebooks, decision logs, and an
anonymized exce p bank ( ex wi h edac ed sc eensho s). Taken oge he , hese ma e ials
cons i u e a ep oducible audi ail ha is a ailable o edi o s upon eques . In sum, his s epwise
p o ocol— aining, c edibili y checks, and audi documen a ion—sys ema ically aligns ou analy ic
p ocedu es wi h es ablished s anda ds o eliabili y and igo .
3.6 E hics
Only public pos s we e analyzed. Iden i ie s (handles, p o ile images, URLs, p ecise imes amps)
we e emo ed o blu ed. Quo a ions a e ligh ly pa aph ased when necessa y o a oid back- acing
while p ese ing meaning; such ins ances a e ma ked. No con ac wi h use s occu ed, and
pla o m e ms we e obse ed.
3.7 Repo ing Con en ions
Findings a e p esen ed as (a) exempla igne es wi h minimal me ada a o suppo in e ence, and
(b) c oss-case ables summa izing ecu en con igu a ions. The subsec ions explici ly signpos how
e idence add esses esea ch ques ions 1 o 3.
3.8 Limi a ions and Scope Condi ions
The co pus is es ic ed o wo pla o ms and h ee opical en y poin s; claims a e analy ically
( heo y-building), no s a is ically, gene alizable. Visual-only pos s and p i a e con en all ou side
Emojis, Hash ags, and Code-Swi ching. . . LinFo
www.linguis ic o um.com 45 Linguis ic Fo um 7(1), 2025
scope. Pla o m a o dances e ol e; in e p e a ions e lec he in e ace condi ions a he ime o
cap u e.
4. Discussion
Findings o his s udy a e o ganized a ound he h ee esea ch ques ions. We i s epo
esou ce‐speci ic unc ions, hen examine wi hin-pos in e ac ions and hei
aes he ic/na a ological e ec s, and inally d aw ou heo e ical and me hodological implica ions.
Illus a i e exce p s a e anonymized and ligh ly pa aph ased o p o ec use iden i y while
p ese ing meaning.
4.1 Emojis, Hash ags, and Code-Swi ching
4.1.1 Emojis: s ance, one, and alignmen
Ac oss opics and pla o ms, emojis p ima ily indexed s ance and onal con ou ing (e.g., empa hy,
i ony, so ening). In #S uden Li e, acial and swea - ace emojis equen ly mi iga ed complain
sequences, con e ing po en ial ace- h ea s in o a ilia i e bids. In #Wo kLi eBalance,
b ie case/clock combina ions in ensi ied ime-p essu e claims while main aining a collegial
egis e . I ony ended o be ma ked by smiling-wi h-swea o winking aces adjacen o o he wise
nega i e p oposi ional con en . These uses align wi h a “ one-as-me ada a” unc ion: emojis do no
eplace wo ds so much as laye e alua i e cues on hem.
4.1.2 Hash ags: pa icipa ion ames and hema ic sca olding
Hash ags se ed wo ecu en unc ions. Fi s , hey indexed opical alignmen and disco e abili y
(e.g., #MeToo, #S uden Li e), si ua ing pos s wi hin issue publics and ou inizing up ake h ough
sea chable labels. Second, use s deployed seconda y o c ea i e ags (e.g., #No AllHe oes,
#DeadlineMode) as me ap agma ic glosses, comp essing s ance in o compac e ains ha eade s
could echo. In mul i- ag pos s, one “ancho ” hash ag ypically es ablished opic, while sho e ,
in en i e ags signaled e alua ion o communi y no ms.
4.1.3 Code-swi ching: audience design and iden i y wo k
In asen en ial swi ching (e.g., English–U du; English–Spanish) signaled audience calib a ion and
iden i y display. Tag-le el swi ching (e.g., #Khai Chalo alongside English body ex ) ecu en ly
lagged cul u al in imacy o inside s a us. In #MeToo pos s, sho swi ches (add ess e ms, idioms)
in ensi ied alignmen wi hou displacing he global “ancho ” language o he h ead. Code choice
was hus a esou ce o who is being add essed and how solida i y o expe ise is being claimed.
4.1.4 Join unc ions
When co-p esen , he h ee esou ces ended o bundle in o s able con igu a ions: (a) Mi iga ed
complain (emoji-as-so ene + opical hash ag + b ie ag-le el swi ch); (b) Call o wi ness (emoji-
as-emphasis + ancho /issue hash ag + c ea i e s ance hash ag); and (c) Inside aside (emoji-as-
wink + ag-le el swi ch + minimal opical agging).
Emojis, Hash ags, and Code-Swi ching. . . LinFo
www.linguis ic o um.com 46 Linguis ic Fo um 7(1), 2025
4.2 Aes he ic and Na a ological E ec s o Resou ce In e ac ion
4.2.1 Voice ( onal ex u ing)
Emoji placemen —especially adjacency o e alua i e lexemes—p oduced a ecognizable oice: a
pos could sound w y, ea nes , o conspi a o ial wi hou lexical change. In #S uden Li e, sequences
like “ inals week… again” yielded a sel -dep eca ing pe sona ha in i ed a ilia i e eplies.
Hash ags occasionally ex ended his oice ac oss pos s; ecu ing c ea i e ags unc ioned as
au ho ial idiosync asies (mic o-s yle ma ke s).
4.2.2 Re ain (mo i ia hash ags)
Ancho hash ags ope a ed as e ains a wo scales. Wi hin pos s, hey punc ua ed clauses ( opic →
e alua ion → opic), c ea ing hy hmic ecu ence. Ac oss pos s, hei epe i ion h eaded dispe sed
u e ances in o a se ial composi ion, allowing eade s o scan a eed as a cho us wi h a ia ions.
C ea i e s ance ags (e.g., #S illHe e, #DoBe e ) ac ed as mo i ic cells, acc uing meaning h ough
euse ac oss con ex s.
4.2.3 Polyphony (mul i oicedness ia code-swi ching)
Swi ching ec ui ed addi ional “ oices”: a quo ed p o e b o add ess e m inse ed an al e na e
pe spec i e wi hou o e epo ing clauses. In #MeToo, sho swi ches in oked amilial o
communal ames, jux aposed agains a public, global ag—p oducing laye ed oicing in a compac
o m. Polyphony in ensi ied when swi ches co-occu ed wi h echo-hash ags (e.g., #Belie eHe /
#HumBhi): he pos simul aneously inhabi ed global and local discu si e wo lds.
4.2.4 In e ac ional cho eog aphy (sequence ma e s)
Func ion and o m we e sensi i e o o de . Emoji-be o e-p oposi ion p imed he eading (e.g., wink
→ i ony), emoji-a e so ened closu e; ancho -hash ag- i s amed up ake, while c ea i e ags in
pos - inal posi ion ac ed as punchlines. Tag-le el swi ching placed a he end ead as an aside;
embedded swi ches ead as iden i y claims in eg al o he p oposi ion. These pa e ned placemen s
cons i u e a aci composi ional g amma .
4.3 Rep esen a i e Con igu a ions
Example 1 — Mi iga ed complain (#S uden Li e)
“Fou deadlines his week… #S uden Li e #Co eeIsMyPlan”
Reading: emoji mi iga es complain ; ancho hash ag si ua es audience; c ea i e ag c ys allizes mo i
( a igue-as- uel).
Payo : Voice (sel -dep eca ing), Re ain (ancho ag), no swi ching needed.
Example B — Call o wi ness (#MeToo)
