ONE WOMAN’S ‘LOL’ IS ANOTHER’S ‘HAHA’: DOING GENDER WITH FEATURES OF THE DIGITAL SUPERVERNACULARL A U R E N S Q U I R E ST H E O H I O S T A T E U N I V E R S I T Y
s q u i r e s . 4 1 @ o s u . e d u
@ p r o f _ s q u i r e s
“ W H A T ’ S U P S W I T Z E R L A N D ” W O R K S H O P
U N I V E R S I T Y O F Z U R I C H
1 1 J U N E 2 0 1 6
BACKGROUND FRAMINGq Style and social identity in digital communication
qWhich features in digital language convey social meanings (indexicalities)?
qWhere do these social meanings come from?qHow are they related to metalinguistic discourse, language ideologies,
spoken language, etc.?
qHow are single features used as part of feature composites to form sociolinguistic styles?
qWhat are the relevant contributions of traditional demographic categories (like gender, race, age) and other social personae dimensions (like “hippie”, “prep”, “burnout”)?
BACKGROUND FRAMINGq A digital ‘supervernacular’?
qSome features seem to recur across global digital contexts, though they may not be unique to those contexts (see Shortis 2007, Squires 2010)
q‘Supervernaculars’: “semiotic forms that circulate in networks driven, largely, by new technologies such as the Internet and mobile communication devices. Attention to these new forms can highlight, because of their extreme clarity, some fundamental aspects of sociolinguistic processes, notably those aspects we capture under terms such as ‘standardization’” (Blommaert 2011)
qWhat about sociolinguistic processes related to style/stance/etc.?qHow do these features come to carry social meanings at both global
and local levels?
SUPERVERNACULAR FEATURES AND GENDER
q How are ’supervernacular’ features used to perform different aspects of social identity?
qSupervernacular features index global-and-local meanings (Blommaert2011; Heyd, fc) through the sociolinguistic ordering of semiotic resourcesqincluding indexical ordering (Spitzmüller)
qHow does the ordering of these resources pertain to gender?
SUPERVERNACULAR FEATURES AND GENDER
q Gender and language use on Twitter (Bamman et al. 2014)
qFeatures more frequent among female Twitter usersqemoticons qemotion termsqellipses qexpressive lengtheningqserial punctuation / exclamation and question marksqbackchannel markersq”CMC words” (abbreviations like ‘lol’ and ‘omg’)
qGendered features and social identity qClusters of authors grouped by lexical similarity are heavily gender-
skewedqbut some gender-skewed clusters defy the average in their usages
q Homophily effect: individuals with stronger same-gender networks use more gender-marked features
CASE STUDY: STYLISTIC UNIFORMITY AND VARIATION IN A COMMUNITY OF PRACTICE
q Project: Tweets of the Real Housewives
qTakes a distinctive group of known speakers, explores how CMC practice reflects (or doesn’t) practice in other realms—offline, and in other media
qGoal: Identify features meaningfully associated with different sociolinguistic styles on Twitter for this group of speakers
qSpecifically, features doing work related to gender and social class (status)
THE REAL HOUSEWIVES• The Real Housewives of…• Reality TV franchise on Bravo network (in the US)• Each show follows group of women: shown shopping, vacationing,
partying, working at home or at work, being with family, going to charity events• Aged late 20s to early 50s• Many are divorced, some never-married; many businesswomen or
public figures
• RH is overtly about elite (Jaworski & Thurlow, 2009), moneyed lifestyles lived by women presenting a mainstream femininity• Main topics of conversation include family, friendships, material
possessions, beauty, lifestyle, sex, conflict
REAL HOUSEWIVES AROUND THE USA
New Jersey
Orange County, California
Atlanta, Georgia
New York City
Beverly Hills, California
REAL HOUSEWIVES AROUND THE USA
Miami
Washington, DC
Potomac, Maryland
Dallas
REAL HOUSEWIVES AROUND THE WORLD
THE ‘HOUSEWIFE’ PERSONA
• A ‘good’ ‘Housewife’ is…• Feminine• Heterosexual• Middle-aged• Fun• Values traditional female roles• Values female empowerment • Wealthy and elite (or at least trying to be)• “Classy” - conforms to set of normatively acceptable behaviors; regulates
others’ behavior (Squires 2014)
REAL HOUSEWIVES AROUND THE USA
New Jersey
Orange County, California
Atlanta, Georgia
New York City
Beverly Hills, California
REAL HOUSEWIVES AROUND THE USA
Miami
Washington, DC
Potomac, Maryland
Dallas
DIFFERENCES BETWEEN HOUSEWIVES CASTS• Regional differences• Each cast takes place in a regionally distinctive area, highlighted through
landscapes, reference points, self-characterizations, and language
• Social differences: racial/ethnic, linguistic, cultural, stylistic
• Orange County: some outwardly Christian; mostly blonde; lots of plastic surgery
• Atlanta: most African American; most Southern; most speak African American English
• New Jersey: Italian-American; several blood relatives; use Northeast dialect features
CAST AS SALIENT SOCIAL DIFFERENCE OPENING TAGLINES
“In Atlanta, money and class do give you power.”
–Kim Zolciak
“In Beverly Hills, it's who you know, and I know everyone.”
–Lisa Vanderpump
“People make fun of Jersey girls, but I think they're just jealous.”
–Teresa Giudice
‘HOUSEWIFE’ AS CLASS AND GENDER PERFORMANCE
q How do the Housewives perform their social personae in CMC?
qThey use social media a lot – blogs, Twitter, Instagram, Snapchat
qText-based Twitter posts are void of the kind of physical symbols integral to the women’s class/gender performances on TV
qHow is the ‘Housewife’ persona presented, or reinforced, via textual features?
qHow does each Housewife present an identity that is at onceqglobally recognizable as feminine, andqregionally-inflected and socially-differentiated
Housewives Twitter Corpus§ Tweets collected from Sept 2012 to July 2013§ Tweets from 6 casts airing at the time, from current cast members at the time
TWEETS OF THE (MOSTLY) RICH & (SEMI-)FAMOUS
Total speakers Total words
Atlanta 7 178,748Beverly Hills 8 264,686
Miami 8 188,845New Jersey 5 155,246
New York City 6 239,935Orange County 6 127,063
TOTAL 40 1,154,523
Beverly Hills
New Jersey
Orange County
Atlanta
Miami
NYC
Real Housewives casts included in analysis
FEATURES ANALYZED
• Substitutions (u, r, b, 4)• Initialisms (wtf, omg)• Abbreviations (abt, ppl, hav, thru, nite)• lol+• haha+• hehe+• Emoticons• xx+• xo+• Prosodic lengthening (lll, ooo, aaa, sss)• Serial punctuation (?!, !?, !!, ??)• Ellipses
• Tabulate number of occurrences• Normalize frequency per 10,000 words
• Which features are used differentially across the women / across the casts?
FEATURE USAGE ACROSS SPEAKERS/CASTS• Casts’ stylistic differences are different ways of doing ‘Housewife’ – e.g. gender + class• Principal Components Analysis determines multivariate dimensions that explain the
structure of the data – shows similarities and differences based on frequency of use of multiple features
-4 -2 0 2 4 6
-6-4
-20
24
Individuals factor map (PCA)
Dim 1 (25.17%)
Dim
2 (1
7.02
%)
Cynthia
Kandi
Kenya
KimZ
NeNe
Phaedra
Porsha
Adrienne
BrandiCamille
KimR
Kyle
LisaV
TaylorYolanda
Aviva
Carole
HeatherT
LuannRamona
Sonja
Alexis
Gretchen
HeatherD
LydiaTamra
Vicki
Caroline
Jacqueline KathyMelissa
TeresaAdriana
AlexiaAna JoannaKarent
Lea
LisaHMarysol
Atlanta
BeverlyHills
MiamiNewJersey
NewYorkCityOrangeCounty
AtlantaBeverlyHillsMiamiNewJerseyNewYorkCityOrangeCounty
-8 -6 -4 -2 0 2 4 6
-6-4
-20
24
Individuals factor map (PCA)
Dim 2 (17.02%)
Dim
3 (1
3.08
%)
CynthiaKandi
Kenya
KimZ
NeNe
Phaedra
Porsha
Adrienne
Brandi
Camille
KimR
Kyle
LisaV
TaylorYolanda
AvivaCarole
HeatherT
Luann
Ramona Sonja
Alexis
Gretchen HeatherD
LydiaTamra
Vicki
CarolineJacquelineKathy Melissa
Teresa
Adriana
Alexia
AnaJoannaKarent
Lea
LisaH
MarysolAtlanta
BeverlyHillsMiami
NewJersey
NewYorkCityOrangeCounty
AtlantaBeverlyHillsMiamiNewJerseyNewYorkCityOrangeCounty
Principal Components 1 and 2 Principal Components 2 and 3
FEATURES UNDERLYING COMPONENT 1
-4 -2 0 2 4 6
-6-4
-20
24
Individuals factor map (PCA)
Dim 1 (25.17%)D
im 2
(17.
02%
)
Cynthia
Kandi
Kenya
KimZ
NeNe
Phaedra
Porsha
Adrienne
BrandiCamille
KimR
Kyle
LisaV
TaylorYolanda
Aviva
Carole
HeatherT
LuannRamona
Sonja
Alexis
Gretchen
HeatherD
LydiaTamra
Vicki
Caroline
Jacqueline KathyMelissa
TeresaAdriana
AlexiaAna JoannaKarent
Lea
LisaHMarysol
Atlanta
BeverlyHills
MiamiNewJersey
NewYorkCityOrangeCounty
AtlantaBeverlyHillsMiamiNewJerseyNewYorkCityOrangeCounty
4 2 0 -2 -4 -6 -8
-6-4
-20
24
6
Comp.1
Comp.2
Cynthia
KandiKenya
KimZ
NeNe
Phaedra
Porsha
Adrienne
BrandiCamille
KimR
Kyle
LisaV
TaylorYolanda
Aviva
Carole
HeatherT
LuannRamona
Sonja
Alexis
Gretchen
HeatherD
Lydia
TamraVicki
CarolineJacqueline
Kathy
MelissaTeresa
AdrianaAlexiaAna Joanna
Karent
Lea
LisaHMarysol
0.4 0.2 0.0 -0.2 -0.4 -0.6 -0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
substitution
initialism
lol
haha
hehe
abbreviation
lengthening
serialpunc
ellipsesemoticon
xx
xo
Component 1 = “light” v. “heavy” users of ‘supervernacular’ features (no cast association)
FEATURES UNDERLYING COMPONENTS 2 & 3
-6 -4 -2 0 2 4 6
-6-4
-20
24
6
Comp.2
Comp.3
CynthiaKandi
Kenya
KimZ
NeNe
Phaedra
Porsha
Adrienne
Brandi
Camille
KimR
Kyle
LisaV
TaylorYolanda
AvivaCarole
HeatherT
Luann
RamonaSonja
Alexis
GretchenHeatherDLydiaTamra
Vicki
CarolineJacquelineKathyMelissaTeresa
AdrianaAlexia
AnaJoannaKarent
Lea
LisaH
Marysol
-0.5 0.0 0.5
-0.5
0.0
0.5
substitutioninitialism
lol
haha
heheabbreviation
lengthening
serialpuncellipses
emoticon
xx
xo
-8 -6 -4 -2 0 2 4 6
-6-4
-20
24
Individuals factor map (PCA)
Dim 2 (17.02%)D
im 3
(13.
08%
)
CynthiaKandi
Kenya
KimZ
NeNe
Phaedra
Porsha
Adrienne
Brandi
Camille
KimR
Kyle
LisaV
TaylorYolanda
AvivaCarole
HeatherT
Luann
Ramona Sonja
Alexis
Gretchen HeatherD
LydiaTamra
Vicki
CarolineJacquelineKathy Melissa
Teresa
Adriana
Alexia
AnaJoannaKarent
Lea
LisaH
MarysolAtlanta
BeverlyHillsMiami
NewJersey
NewYorkCityOrangeCounty
AtlantaBeverlyHillsMiamiNewJerseyNewYorkCityOrangeCounty
Component 2 = +ellipses, +haha, +xx, +emoticons, -lol, -xo, -abbreviations = +Beverly Hills
Component 3 = +xo, +emoticons, +haha, -initialism, -substitution, -lengthening = -Atlanta
FEATURES UNDERLYING COMPONENTS
Component 1 Component 2 Component 3
+serial punctuation +haha +xo
+hehe +ellipses +haha
+initialism +xx +emoticon
+emoticon +emoticon -substitution
+lengthening -xo -initialism
+lol -abbreviation -lengthening
+abbreviation -lol
-Atlanta+Beverly Hills
• HAHA/LOL as alternatives*• XO/XX as alternatives
(*Tagliamonte & Denis 2008)
LOL – PROPORTION RELATIVE TO HAHA/HEHE
Cynthia
Kandi
Kenya
KimZ
Nene
Phaedra
Porsha
Adrienne
Brandi
Camille
KimR
Kyle
LisaV
Taylor
Yolanda
Aviva
Carole
HeatherT
Luann
Ramona
Sonja
Alexis
Gretchen
HeatherD
Lydia
Tamra
Vicki
Caroline
Jacqueline
Kathy
Melissa
Teresa
Adriana
Alexia
Ana
Joanna
Karent
Lea
LisaH
Marysol
Proportion LOL v. HAHA/HEHE
0.0
0.2
0.4
0.6
0.8
1.0
Atlanta BeverlyHills
New York City
OrangeCounty
New Jersey
Miami
XO – PROPORTION RELATIVE TO XX
Cynthia
Kandi
Kenya
KimZ
Nene
Phaedra
Porsha
Adrienne
Brandi
Camille
KimR
Kyle
LisaV
Taylor
Yolanda
Aviva
Carole
HeatherT
Luann
Ramona
Sonja
Alexis
Gretchen
HeatherD
Lydia
Tamra
Vicki
Caroline
Jacqueline
Kathy
Melissa
Teresa
Adriana
Alexia
Ana
Joanna
Karent
Lea
LisaH
Marysol
Proportion XO v. XX
0.0
0.2
0.4
0.6
0.8
1.0
Atlanta BeverlyHills
New York City
OrangeCounty
New Jersey
Miami
FUNCTIONAL DISTRIBUTION OF FEATURES
• To establish that these feature pairs are really alternative ways of indexing gender, can we establish that they serve as functional equivalents?
• Do LOL and HAHA perform equivalent functions?
• Do XO and XX perform equivalent functions?
All the women are doing feminine, but using different resources:• Prefer either LOL or HAHA • Prefer either XO or XX
• To some extent, the preferences align with cast membership
FUNCTIONS: LOL / HAHA
position LOL HAHA
Tweet-comprehensive @leReelmccoy lol! @1traceyoneill haha!
Tweet-initial @jsplendor lol yes the music started. @darlenemcki haha. Seems like forever ago!
Tweet-final @jaymandel drowsy appearance. Lol @TaraKrn14 spent 15 min on that for keek. Haha
Tweet-medial @songdivaDAJ I'll consider that! lol thanks Hun
@DrEstella are you crazy? 1000 a month??? Haha!! Then she needs to pay rent ;-)
• Both occur primarily in tweets that are responses to other tweets• Most tweets begin with @username
• Both occur in multiple positions within a tweet• Function as backchannels• Function as discourse markers
FUNCTIONS: XO / XX
position XO XX
Tweet-comprehensive @Slditchkus xoxo @KellyGloster Xx
Tweet-initial @dillonisaBOSS xoxo too funny! @JosueSejour1 xx! For sure!!
Tweet-final @JeneLuciani @GoRecessionista She was!! xo @kouskous SO fun! Loved it! XX
Tweet-medial
• Both occur primarily in tweets that are responses to other tweets• Begin with @username
• Both occur primarily tweet-comprehensively or tweet-finally; tweet-initial and tweet–medial are rare• Function as backchannels – phatic acknowledgments• Function as “sign-off”
DISCUSSION
Features most clearly associated with individual and group styles:
qLOL/HAHA/XO/XX have all been shown elsewhere to have higher frequency among female Twitter users than male users
qAll function as backchannels or “acknowledgers” – a function itself indexing a feminine-gendered identity?qLOL/HAHA acknowledge content in addition to presenceqXO/XX acknowledge presence only
q LOL/HAHA have wider functionality as discourse markers with multiple uses, but still seem to work primarily as alternativesqChoice of one versus another must pertain to social meaning—what
the features signify within the relevant sociolinguistic economies
DISCUSSION
q ‘Supervernacular’ forms indexing gendered identitiesqCarry globally recognizable meaning of technological
savviness/literacy—formed by association with digital culturesqCarry locally recognizable meanings of femininity (shared) and style
(different)qMake sense that features serving these particular functions would
be ones showing stylistic stratification: discourse-marking, phatic communication
q Even amongst people who operate largely in the same social milieu, and who want to signal similar personae, there are nuanced differencesqUse of features is meaningful within a composite of sociolinguistic
resources and in contrast to other possible compositesqOne identity category is not all of identity – but also not only about
individual style (styles within styles)