A"Twi&er)Based"Study"of""Newly"Formed"Clippings""in"American"English"!

!

Sravana!Reddy,!Joy!Zhong,!James!Stanford!Dartmouth!College!

Previous!Work!

Baclawski!(2012)!A!study!of!Fs!(‘adorbs’)!in!40!TwiLer!users!

Research!QuesNons!•  Are!these!clippings!just!cyclical!“slang”?!(Eble!1996,!2004)!!

•  Are!they!an!increasingly!producNve!process!with!new!social!meanings?!!

•  Is!this!type!of!clipping!more!producNve!than!past!generaNons?!!

•  What!is!the!role!of!the!–s!suffix!(adorbs,(awks,(totes)?!!

•  Which!speakers!use!it!the!most?!Age,!gender,!ethnicity?!

This!Study!

Hypothesis:!!Women!are!leading!in!the!usage!of!these!new!clippings,!and!it!is!more!

urban/suburban"than!rural!

Labov!(1990,!2001),!Trudgill!(1972),!Coates!&!Pichler!(2011),!Holmes!&!Meyerhoff!(2003),!Wolfram!&!SchillingFEstes!(2006:155F6)!

Why!use!TwiLer!for!!American!Dialect!Research?!

Each!era!applied!contemporary!technology…!–  Kurath!(1939)!–  Hanley’s!recordings!(1931F1937)!(Purnell!2012)!–  Chambers!&!Trudgill!(1998)!–  Labov,!Ash!&!Boberg!(2006)!–  Kretzschmar!(2009)!and!many!more !!

Now:!Social!Media!analysis,!computaNonal!modeling,!!

Mechanical!Turk!

TwiLer!for!SociolinguisNcs!– Eisenstein,!O’Connor,!Smith!&!Xing!(2010)!US!regional!variaNon!in!lexical!items!– Bamman,!Eisenstein!&!Schnoebelen!(2012)!Gendered!language!and!networks!– Maybaum!(2012)!!TwiLer!terms!– Zappavigna!(2013)!!TwiLer!discourse!and!variaNon!– Doyle!(2014)!Geographic!distribuNon!of!“needs!done”!

Methodology!•  Collected!185!million!geoFtagged!tweets(originaNng!in!the!US!(JulFNov!2013)!!by!893,024!users!

•  AutomaNcally!extracted!a!list!of!clippings!!•  For!each!word,!created!demographic!profile!of!users!!– Gender!– PopulaNon,!median!age,!and!ethnic!distribuNon!at!the!user’s!locaNon!

•  Compared!demographic!features!of!clipping!and!its!original!form!

ExtracNng!Clippings!

•  Rather!than!manually!compiling!list!of!clippings,!automaNcally!learn!from!TwiLer!data!

•  A!clipping!and!its!original!form!will!be!used!in!roughly!similar!contexts!

ExtracNng!Clippings!

•  Represent!every!word!type!as!vector!of!its!leo!and!right!context!

•  Rank!every!word!pair!by!context!vector!similarity!!

•  Extract!top!ranked!pairs!where!first!three!characters!match!

totes!leo:!am,!are,!was,!were,!is..!right:!okay,!ok,!adorbs,!fine…!!adorable!leo:!is,!so,!these,!looks…!right:!omg,!with,!dork,…!!totally!leo:!am,!are,!was,!were,!is…!right:!okay,!fine,!insane,!not…!!!

•  Survey!on!Mechanical!Turk!!•  Demographic!quesNons:!age,!gender,!locaNon!!•  Rate!familiarity!with!each!clipping!

o Unfamiliar!o Familiar,!but!I!do!not!use!it!o I!use!it!in!speech!only!o I!use!it!in!wriNng!only!o I!use!it!in!speech!and!wriNng!

•  Same!survey!also!conducted!with!Dartmouth!undergraduate!students!

Old!vs.!New!Clippings!

Old!vs.!New!Clippings!•  Split!survey!respondents!into!ages!18F29!and!30+!•  For!each!clipping,!compute!average!familiarity!score!within!the!two!age!groups!

!0!=!Unfamiliar!3!=!Familiar,!but!I!do!not!use!it!4!=!I!use!it!in!speech!only!5!=!I!use!it!in!wriNng!only!6!=!I!use!it!in!speech!and!wriNng!

1.13 0.89 0.76

1.941.52

4.253.75 3.6

4.724.14

sesh obvi perf probs totes

Top858New8Words

Avg8Fam

iliarity8Score

Over830

Under830

Old!vs.!New!Clippings!

1.13 0.89 0.76

1.941.52

4.253.75 3.6

4.724.14

sesh obvi perf probs totes

Top858New8Words

Avg8Fam

iliarity8Score

Over830

Under830

•  Newness!score!for!clipping!!=!below!30!familiarity!–!above!30!familiarity!

!

Old!vs.!New!Clippings!

•  Newness!score!for!clipping!!=!below!30!familiarity!–!above!30!familiarity!

•  Threshold!at!1.0!newness!score!

sesh!

-2 -1 0 1 2 3

perf!adorbs!ridic!vom!alc!vid!frat!perv!choc!cig!

New!clippings!(25)!Old!clippings!(55)!

doc!

Old!vs.!New!Clippings!sesh!perf!adorbs!ridic!vom!alc!vid!frat!perv!choc!cig!

New!clippings!(25)!Old!clippings!(55)!

doc!

obvi!

probs!

totes!

cray!

choreo!

presh!

craycray!

convo!def!

hilar!

gorg!

guac!

vocab!

obv!

fam!

adorb!

calc!

chem!esp!

prez!

merch!

vacay!

roomie!

mins!

diff!

parm!

prac!

pedi!

sched!

prob!

prolly!

sibs!

delish!

defs!

sus!

pic!

intro!

comfy!

prep!

secs!

approx!

fave!

milli!

collab!

combo!

apps!perv!

pregs!

champ!

info!

preggo!

breaky!

liq!

taL!

meds!

undies!

defly!

gents!

lolly!

anon!

fams!

ortho!

preg!num!

McD’s!

chiro!

cig!

Clippings!on!TwiLer!

0!100000!200000!300000!400000!500000!600000!700000!800000!900000!

Clipping! Original!

Old!New!

0!100000!200000!300000!400000!500000!600000!700000!800000!900000!

Clipping! Original!

Number!of!users!

Demographic!Analysis!•  Gender!

(following!Bamman!et!al.)!– Most!TwiLer!users!report!a!name!in!addiNon!to!their!pseudonym!!!!

– Match!first!name!against!the!Social!Security!AdministraNon!list!of!baby!names!born!in!1995!

– About!2/3!of!users!have!names!in!the!SSA!list!and!are!assigned!a!gender!

Demographic!Analysis!•  LocaNon!!

(following!Eisenstein!et!al.)!– Tweets!are!geoFtagged!with!laNtude/longitude!– Map!each!geoFcoordinate!to!one!of!33000!!Zip!Code!TabulaNon!Areas!(ZCTAs)!

–  Ignore!users!that!tweet!from!more!than!one!ZCTA!– Get!demographic!aLributes!of!ZCTAs!from!2010!Census:!PopulaNon,!Median!Age,!White%,!African!American%,!Asian%,!NaNve!American%,!Hispanic%!

– Each!user!is!now!associated!with!a!demographic!profile!of!their!environment!

Demographic!Analysis!

•  LogisNc!regression!– Predicted!variable:!clipping!or!original?!– Features:!demographic!profile!of!users!!

•  Gender!•  PopulaNon!•  Median!Age!•  Ethnicity!

New!and!Old!Clippings!Log!od

ds!

All!factors!shown!are!!significant!(p<0.05)!

F0.15!

F0.1!

F0.05!

0!

0.05!

0.1!

New! Old!

Gender!Female!

0!

0.005!

0.01!

0.015!

0.02!

0.025!

0.03!

0.035!

New! Old!

Log!od

ds!

Log10!PopulaNon!

Log!od

ds!

No!Significant!Effects!

New!and!Old!Clippings!Log!od

ds!

F0.005!

F0.004!

F0.003!

F0.002!

F0.001!

0!

0.001!

0.002!

0.003!

0.004!

0.005!

New! Old!

Log!od

ds!

Median!Age! Ethnicity!

Usage!of!Fs!suffix!in!clippings!

0!

0.1!

0.2!

0.3!

0.4!

0.5!

0.6!

Gender!Female!

Log!od

ds!

F0.016!

F0.014!

F0.012!

F0.01!

F0.008!

F0.006!

F0.004!

F0.002!

0!

Log!od

ds!

Median!Age!

adorbs/adorb,!probs/prob,!fams/fam,!awks/awk,!pregs/preg,!defs/def!!

Conclusion!

Hypothesis(Confirmed:!!Women!are!leading!in!the!usage!of!these!new!clippings,!and!it!is!more!

urban/suburban"than!rural!

Are!clippings!a!TwiLer!arNfact?!They!abound!in!longFform!blog!posts!too…!

…!and!TwiLer!users!ooen!lengthen!words!

Are!clippings!a!TwiLer!arNfact?!Baldwin!et!al.!(2013)!measure!average!word!lengths!in!TwiLer!and!different!corpora!

Are!clippings!a!TwiLer!arNfact?!Eisenstein!et!al.!(2013)!find!shortened!forms!!are!mainly!used!in!tweets!of!length!much!less!than!!140!characters.!Shortening!is!not!used!in!order!to!!fit!length!constraints!!

Are!clippings!a!TwiLer!arNfact?!Our!experiment:!avg!lengths!of!tweets!containing!!clippings!compared!to!tweets!with!original!forms!

77.01!81.64!

0!

20!

40!

60!

80!

100!

120!

140!

Clipping! Original!

Old!New!

65.08! 73.74!0!

20!

40!

60!

80!

100!

120!

140!

Clipping! Original!

Future!Work!

•  Track!spread!of!clippings!in!TwiLer!over!Nme!– Will!these!clippings!spread!throughout!the!populaNon?!

– Geographic/demographic!dimensions!of!spread?!– When!did!these!clippings!originate?!!!

•  MorphoFphonological!study!of!clippings!!