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whoTarget (string)intentYN (string)sexYN (string)sexReason (string)offensiveYN (string)annotatorGender (string)annotatorMinority (string)sexPhrase (string)speakerMinorityYN (string)WorkerId (string)HITId (string)annotatorPolitics (string)annotatorRace (string)annotatorAge (string)post (string)targetMinority (string)targetCategory (string)targetStereotype (string)dataSource (string)
0.0
0.66
0.0
1.0
woman
women
-7090297418013232722
3DA79LNS59PNAW62RLUHAOPS7E2T38
other
white
38.0
Fuc u say ? “@AyyyeThatsChubb: All these hoes look good on IG” http://t.co/PlsFL84cDp
t/davidson
0.0
0.0
0.0
man
3541686222721437796
3DA79LNS59PNAW62RLUHAOPS7E2T38
liberal
white
37.0
Fuc u say ? “@AyyyeThatsChubb: All these hoes look good on IG” http://t.co/PlsFL84cDp
t/davidson
0.0
0.66
0.0
0.5
man
-7353159578349448466
3DA79LNS59PNAW62RLUHAOPS7E2T38
mod-liberal
white
32.0
Fuc u say ? “@AyyyeThatsChubb: All these hoes look good on IG” http://t.co/PlsFL84cDp
t/davidson
0.0
1.0
1.0
calls a woman a slut
1.0
man
Yo hoe is like a tick so she hunting for a blood
2430897476320006833
3OQQD2WO8I0X1IQRX9VDWHNJWXN3I2
cons
white
32.0
Yo hoe is like a tick so she hunting for a blood
t/davidson
0.0
0.66
0.0
0.5
woman
-6352039173957003457
3OQQD2WO8I0X1IQRX9VDWHNJWXN3I2
mod-cons
white
36.0
Yo hoe is like a tick so she hunting for a blood
t/davidson
0.0
0.66
0.0
1.0
woman
women
-7090297418013232722
3OQQD2WO8I0X1IQRX9VDWHNJWXN3I2
other
white
38.0
Yo hoe is like a tick so she hunting for a blood
t/davidson
1.0
1.0
0.0
1.0
man
0.0
4554125108380068779
32PT7WK7DM9GT7A55VU25K764SF3D0
mod-liberal
white
27.0
When u hitting it from the back u gotta call her "bitch" they love that but don't u dare call em "slut"
women
gender
sexually promiscuous women are called sluts
t/davidson
1.0
1.0
0.0
1.0
man
0.0
4554125108380068779
32PT7WK7DM9GT7A55VU25K764SF3D0
mod-liberal
white
27.0
When u hitting it from the back u gotta call her "bitch" they love that but don't u dare call em "slut"
women
gender
women are sex objects
t/davidson
0.0
0.66
0.0
1.0
man
2385617559681638227
3ZFRE2BDQ98VC6MFH2QN3SFDZ5PXZS
liberal
white
26.0
Studying for this bio test like a hoe 😪
t/davidson
0.0
0.5
refers to self as a ho
0.0
man
Studying for this bio test like a hoe
2430897476320006833
3ZFRE2BDQ98VC6MFH2QN3SFDZ5PXZS
cons
white
32.0
Studying for this bio test like a hoe 😪
t/davidson
0.0
0.0
0.0
woman
955703508749055793
3ZFRE2BDQ98VC6MFH2QN3SFDZ5PXZS
liberal
asian
23.0
Studying for this bio test like a hoe 😪
t/davidson
1.0
1.0
1.0
it's a euphemism for having sex with a woman
1.0
man
get some pussy
0.5
-2394418997796114243
38F60IALAGBRT4758YCY8QRL42IT0D
mod-liberal
white
30.0
You niggas talking to these females trying to get some pussy but dont realize you're only boosting their ego and getting curved.
women
gender
women are self-centered/egotistical
t/davidson
1.0
1.0
1.0
it's a euphemism for having sex with a woman
1.0
man
get some pussy
0.5
-2394418997796114243
38F60IALAGBRT4758YCY8QRL42IT0D
mod-liberal
white
30.0
You niggas talking to these females trying to get some pussy but dont realize you're only boosting their ego and getting curved.
women
gender
women string men along
t/davidson
1.0
0.66
0.0
0.5
woman
gender, sexuality, mental illness
0.0
118631008378348260
32K26U12DNI05GCOP508OFZPVC7VDO
liberal
white
28.0
Fuck you bitches who send dudes to child support court just because you're petty
women
gender
women are petty
t/davidson
1.0
0.66
0.0
0.5
woman
gender, sexuality, mental illness
0.0
118631008378348260
32K26U12DNI05GCOP508OFZPVC7VDO
liberal
white
28.0
Fuck you bitches who send dudes to child support court just because you're petty
women
gender
women only care about money
t/davidson
1.0
1.0
0.0
1.0
man
0.0
6347880360297734464
3J94SKDEKIJJ7MX9JEIVKH5QIO6D50
mod-liberal
white
35.0
A Nigguh spitting dat crack to your bitch
black folks
race
black people are crackheads
t/davidson
1.0
1.0
0.0
1.0
man
0.0
6347880360297734464
3J94SKDEKIJJ7MX9JEIVKH5QIO6D50
mod-liberal
white
35.0
A Nigguh spitting dat crack to your bitch
black folks
race
black people are criminals
t/davidson
0.33
0.0
0.0
woman
8805298672758419198
3W31J70BASQCKDCYQ5RQ206QUEPKCS
liberal
white
49.0
RT @YoungAmari_: Dizzy bitch 😒😴
t/davidson
0.0
0.66
0.0
1.0
woman
women
-7090297418013232722
3W31J70BASQCKDCYQ5RQ206QUEPKCS
other
white
38.0
RT @YoungAmari_: Dizzy bitch 😒😴
t/davidson
0.0
0.66
0.0
0.5
woman
gender, sexuality, mental illness
118631008378348260
3W31J70BASQCKDCYQ5RQ206QUEPKCS
liberal
white
28.0
RT @YoungAmari_: Dizzy bitch 😒😴
t/davidson
1.0
0.66
1.0
whore
1.0
woman
disabled, bisexual
hoe
0.0
5225876289250202382
36MUZ9VAE6WJ35Q2UFBYOBB3VH5DE1
liberal
white
32.0
RT @lesxo_: cant turn a hoe into a husband either
women, trans men
gender
women who have sex before marriage are ruined.
t/davidson
1.0
0.66
1.0
whore
1.0
woman
disabled, bisexual
hoe
0.0
5225876289250202382
36MUZ9VAE6WJ35Q2UFBYOBB3VH5DE1
liberal
white
32.0
RT @lesxo_: cant turn a hoe into a husband either
women, trans men
gender
trans men are not really part of their gender.
t/davidson
1.0
0.66
1.0
oral sex
1.0
woman
suck dick
0.0
-8935932304856669427
3L2OEKSTW945SFB2D8CC9MPI22CY8U
liberal
white
45.0
"@ReachtheeCLIMAX: bitches can't cook but know how to suck dick .." s/o to the ones that can do both!
women
gender
women are used for sex
t/davidson
1.0
0.66
1.0
oral sex
1.0
woman
suck dick
0.0
-8935932304856669427
3L2OEKSTW945SFB2D8CC9MPI22CY8U
liberal
white
45.0
"@ReachtheeCLIMAX: bitches can't cook but know how to suck dick .." s/o to the ones that can do both!
women
gender
women are used for cooking
t/davidson
0.0
0.0
0.0
man
-7353159578349448466
37SQU136V7IQR9GE2YWOGPR0HBY11G
mod-liberal
white
32.0
Like the hoes when they lay w/ me.. Puppy eyes when I go, Sayin stay wit me
t/davidson
0.33
0.0
0.0
man
-7452610791699819066
37SQU136V7IQR9GE2YWOGPR0HBY11G
liberal
asian
23.0
Like the hoes when they lay w/ me.. Puppy eyes when I go, Sayin stay wit me
t/davidson
0.0
1.0
have sex with me
0.0
woman
lay with me
6927542478311092738
37SQU136V7IQR9GE2YWOGPR0HBY11G
liberal
white
24.0
Like the hoes when they lay w/ me.. Puppy eyes when I go, Sayin stay wit me
t/davidson
0.33
0.0
woman
-3559048982681398316
3XUY87HIVP8KJNOSO6YKK936DXOMMH
liberal
white
31.0
RT @blackrepublican: Spike Lee is one of those [wealthy] people he bitches about that's causing gentrification.
t/davidson
0.0
1.0
0.0
1.0
woman
446815182533233009
3XUY87HIVP8KJNOSO6YKK936DXOMMH
other
white
35.0
RT @blackrepublican: Spike Lee is one of those [wealthy] people he bitches about that's causing gentrification.
t/davidson
0.33
0.0
0.0
woman
1970909591341798263
3XUY87HIVP8KJNOSO6YKK936DXOMMH
na
white
44.0
RT @blackrepublican: Spike Lee is one of those [wealthy] people he bitches about that's causing gentrification.
t/davidson
1.0
1.0
0.0
1.0
woman
Woman, African American
1.0
7067515179960755087
3JYPJ2TAYI2KYGTOUZMZ6NDSL1NFPZ
mod-liberal
black
30.0
Dear Utah Polynesians: Again, stop acting black and speaking ghetto. Speak FOB, you ain't no ghetto niggaz.
black folks
race
nigga is a slang term for black people
t/davidson
1.0
1.0
0.0
1.0
woman
Woman, African American
1.0
7067515179960755087
3JYPJ2TAYI2KYGTOUZMZ6NDSL1NFPZ
mod-liberal
black
30.0
Dear Utah Polynesians: Again, stop acting black and speaking ghetto. Speak FOB, you ain't no ghetto niggaz.
black folks
race
black people are ghetto
t/davidson
0.0
0.0
0.0
woman
8805298672758419198
3K1H3NEY7LTHNJMT0A1MOEWF8XKDGI
liberal
white
49.0
i ran out 😩 RT @sadonna_rae: This hoe just texted me asking to borrow a roll of tissue until tomorrow 😫😫😭😭😭
t/davidson
0.0
0.66
0.0
0.5
man
-7790343187498138255
3K1H3NEY7LTHNJMT0A1MOEWF8XKDGI
cons
white
49.0
i ran out 😩 RT @sadonna_rae: This hoe just texted me asking to borrow a roll of tissue until tomorrow 😫😫😭😭😭
t/davidson
0.0
0.0
0.0
man
-5870881806826799680
3K1H3NEY7LTHNJMT0A1MOEWF8XKDGI
liberal
white
38.0
i ran out 😩 RT @sadonna_rae: This hoe just texted me asking to borrow a roll of tissue until tomorrow 😫😫😭😭😭
t/davidson
0.33
1.0
sex
0.0
woman
none
fucking raw
-4903257119943710595
311HQEI8RSA1XRGOZPMP9T2PXCBZ70
liberal
white
32.0
Niggas swear they don't trust these hoes but be fucking raw 😐😂smh
t/davidson
0.0
0.66
1.0
Sex without protection
0.5
woman
women
fucking raw
7162172225567546910
311HQEI8RSA1XRGOZPMP9T2PXCBZ70
liberal
white
27.0
Niggas swear they don't trust these hoes but be fucking raw 😐😂smh
t/davidson
0.33
1.0
unprotected sex
0.0
man
fucking raw
-7353159578349448466
311HQEI8RSA1XRGOZPMP9T2PXCBZ70
mod-liberal
white
32.0
Niggas swear they don't trust these hoes but be fucking raw 😐😂smh
t/davidson
1.0
1.0
0.0
1.0
woman
0.0
-8810733183869606469
30P8I9JKOIF513XVAZIPQ3BXIVA5VR
liberal
white
34.0
@YourDudeFerg gay fags
lesbian women, gay men, trans women, trans men, bisexual women, bisexual men
gender
gay folks are "fags"
t/davidson
1.0
1.0
0.0
1.0
woman
0.0
-8810733183869606469
30P8I9JKOIF513XVAZIPQ3BXIVA5VR
liberal
white
34.0
@YourDudeFerg gay fags
lesbian women, gay men, trans women, trans men, bisexual women, bisexual men
gender
gay folks are inferior.
t/davidson
1.0
0.66
0.0
0.5
woman
0.0
-6352039173957003457
30P8I9JKOIF513XVAZIPQ3BXIVA5VR
mod-cons
white
36.0
@YourDudeFerg gay fags
gay men
gender
is promiscuous
t/davidson
1.0
0.66
0.0
0.5
woman
0.0
-6352039173957003457
30P8I9JKOIF513XVAZIPQ3BXIVA5VR
mod-cons
white
36.0
@YourDudeFerg gay fags
gay men
gender
is a disappointment
t/davidson
1.0
1.0
0.0
1.0
woman
0.0
-7598494085232335834
30P8I9JKOIF513XVAZIPQ3BXIVA5VR
liberal
white
41.0
@YourDudeFerg gay fags
lesbian women, gay men, trans women, trans men, bisexual women, bisexual men
gender
gay people are called fags
t/davidson
1.0
1.0
0.0
1.0
woman
0.0
-7598494085232335834
30P8I9JKOIF513XVAZIPQ3BXIVA5VR
liberal
white
41.0
@YourDudeFerg gay fags
lesbian women, gay men, trans women, trans men, bisexual women, bisexual men
gender
fags is a bad term
t/davidson
0.0
0.0
0.0
woman
-5471608738051449110
3KVQ0UJWPXFIGSX009NLC5UBP4C5W1
other
white
35.0
RT @iLikeWGW: retweet for big booty bitches
t/davidson
0.0
1.0
0.0
1.0
woman
-8810733183869606469
3KVQ0UJWPXFIGSX009NLC5UBP4C5W1
liberal
white
34.0
RT @iLikeWGW: retweet for big booty bitches
t/davidson
0.0
0.0
0.0
woman
female
5330619308534303933
3KVQ0UJWPXFIGSX009NLC5UBP4C5W1
mod-cons
white
35.0
RT @iLikeWGW: retweet for big booty bitches
t/davidson
0.0
0.0
0.0
man
-7353159578349448466
30EV7DWJTVPIO1SEKTFEP8TRW7MY68
mod-liberal
white
32.0
“@TheRealMFast: I hate this shit like why y'all bitches think that's cute? http://t.co/UXSzeXGUjU”<total clothing violation
t/davidson
0.0
0.66
0.0
0.5
woman
female
5330619308534303933
30EV7DWJTVPIO1SEKTFEP8TRW7MY68
mod-cons
white
35.0
“@TheRealMFast: I hate this shit like why y'all bitches think that's cute? http://t.co/UXSzeXGUjU”<total clothing violation
t/davidson
0.0
0.0
0.0
woman
-5471608738051449110
30EV7DWJTVPIO1SEKTFEP8TRW7MY68
other
white
35.0
“@TheRealMFast: I hate this shit like why y'all bitches think that's cute? http://t.co/UXSzeXGUjU”<total clothing violation
t/davidson
1.0
1.0
0.0
1.0
woman
none
0.5
-3114316428557054065
37S0QRNUFBAQD9KK04EIA91G9NA88R
mod-cons
white
33.0
bitches dumb
women
gender
women are stupid
t/davidson
1.0
1.0
0.0
1.0
woman
none
0.5
-3114316428557054065
37S0QRNUFBAQD9KK04EIA91G9NA88R
mod-cons
white
33.0
bitches dumb
women
gender
women are bitches
t/davidson
0.0
0.66
0.0
1.0
woman
racial minority, gender
4224836053735053558
3Q7TKIAPOT4X9B0UB1Y2161N4E6DLO
liberal
asian
25.0
Y'all lonely ass bitches sure do give out a lot of relationship advice....
t/davidson
0.0
0.0
0.0
man
-7353159578349448466
3Q7TKIAPOT4X9B0UB1Y2161N4E6DLO
mod-liberal
white
32.0
Y'all lonely ass bitches sure do give out a lot of relationship advice....
t/davidson
0.0
0.66
0.0
1.0
woman
7912096326098817047
3Q7TKIAPOT4X9B0UB1Y2161N4E6DLO
liberal
white
32.0
Y'all lonely ass bitches sure do give out a lot of relationship advice....
t/davidson
1.0
1.0
0.0
1.0
man
white, male
0.0
1389628790122253253
3VDI8GSXAFN56OQ2J2Q62MIEX6N8G7
cons
white
31.0
"@ARIZZLEINDACUT: Females think dating a pussy is cute now? http://t.co/VxBJg26Gsz" how does doing this stuff make him a pussy?
women, men
gender
women don't want real men anymore
t/davidson
1.0
1.0
0.0
1.0
man
white, male
0.0
1389628790122253253
3VDI8GSXAFN56OQ2J2Q62MIEX6N8G7
cons
white
31.0
"@ARIZZLEINDACUT: Females think dating a pussy is cute now? http://t.co/VxBJg26Gsz" how does doing this stuff make him a pussy?
women, men
gender
women want to date weak men
t/davidson
1.0
0.66
0.0
0.5
man
0.0
4554125108380068779
3VDI8GSXAFN56OQ2J2Q62MIEX6N8G7
mod-liberal
white
27.0
"@ARIZZLEINDACUT: Females think dating a pussy is cute now? http://t.co/VxBJg26Gsz" how does doing this stuff make him a pussy?
women
gender
women date "inferior" men
t/davidson
1.0
0.66
0.0
0.5
man
0.0
4554125108380068779
3VDI8GSXAFN56OQ2J2Q62MIEX6N8G7
mod-liberal
white
27.0
"@ARIZZLEINDACUT: Females think dating a pussy is cute now? http://t.co/VxBJg26Gsz" how does doing this stuff make him a pussy?
women
gender
women sometimes just referred to as "females"
t/davidson
1.0
1.0
0.0
1.0
man
0.5
-2877140746380073212
3VDI8GSXAFN56OQ2J2Q62MIEX6N8G7
cons
white
42.0
"@ARIZZLEINDACUT: Females think dating a pussy is cute now? http://t.co/VxBJg26Gsz" how does doing this stuff make him a pussy?
men
gender
must be masculine
t/davidson
1.0
1.0
0.0
1.0
man
0.5
-2877140746380073212
3VDI8GSXAFN56OQ2J2Q62MIEX6N8G7
cons
white
42.0
"@ARIZZLEINDACUT: Females think dating a pussy is cute now? http://t.co/VxBJg26Gsz" how does doing this stuff make him a pussy?
men
gender
should not show weakness
t/davidson
1.0
0.33
0.0
1.0
man
0.0
-7353159578349448466
3PCPFX4U40K5YSU3UC1O6S32QX9FQQ
mod-liberal
white
32.0
@StephyRae7 hii fag. :)
gay men
gender
gay men should be called fags
t/davidson
1.0
0.33
0.0
1.0
man
0.0
-7353159578349448466
3PCPFX4U40K5YSU3UC1O6S32QX9FQQ
mod-liberal
white
32.0
@StephyRae7 hii fag. :)
gay men
gender
gay men should be the butt of jokes
t/davidson
0.0
1.0
0.0
1.0
man
3541686222721437796
30U1YOGZGAQKDOVKVAV3DSFIW8EDS2
liberal
white
37.0
That bitch got 5 million boyfriends
t/davidson
0.0
0.66
0.0
0.5
woman
6927542478311092738
30U1YOGZGAQKDOVKVAV3DSFIW8EDS2
liberal
white
24.0
That bitch got 5 million boyfriends
t/davidson
0.0
1.0
0.0
1.0
woman
female
5330619308534303933
30U1YOGZGAQKDOVKVAV3DSFIW8EDS2
mod-cons
white
35.0
That bitch got 5 million boyfriends
t/davidson
0.0
0.66
0.0
0.5
woman
0.0
3401190781414184435
3R0WOCG21M30S1NOKFQ3STX6H5ZUDZ
mod-liberal
white
46.0
Just straight ignorant bitches #WhereTheyHeadsAt
t/davidson
0.0
1.0
0.0
1.0
woman
1869048359460155317
3R0WOCG21M30S1NOKFQ3STX6H5ZUDZ
mod-cons
white
22.0
Just straight ignorant bitches #WhereTheyHeadsAt
t/davidson
0.0
1.0
0.0
1.0
man
2430897476320006833
3R0WOCG21M30S1NOKFQ3STX6H5ZUDZ
cons
white
32.0
Just straight ignorant bitches #WhereTheyHeadsAt
t/davidson
1.0
1.0
0.0
1.0
woman
disabled, bisexual
0.0
5225876289250202382
32K26U12DNI05GCOP508OFZPVC8DV7
liberal
white
32.0
Why do so many filthy wetback half-breed spic savages live in #LosAngeles? None of them have any right at all to be here.
latino/latina folks
race
latinx people are worthless.
t/davidson
1.0
1.0
0.0
1.0
woman
disabled, bisexual
0.0
5225876289250202382
32K26U12DNI05GCOP508OFZPVC8DV7
liberal
white
32.0
Why do so many filthy wetback half-breed spic savages live in #LosAngeles? None of them have any right at all to be here.
latino/latina folks
race
latinx people don't deserve to live here.
t/davidson
1.0
1.0
0.0
1.0
woman
disabled, bisexual
0.0
5225876289250202382
32K26U12DNI05GCOP508OFZPVC8DV7
liberal
white
32.0
Why do so many filthy wetback half-breed spic savages live in #LosAngeles? None of them have any right at all to be here.
latino/latina folks
race
latinx people are savages.
t/davidson
1.0
1.0
0.0
1.0
woman
Hispanic, female, bisexual
0.0
-1074131302321565002
32K26U12DNI05GCOP508OFZPVC8DV7
mod-liberal
hisp
29.0
Why do so many filthy wetback half-breed spic savages live in #LosAngeles? None of them have any right at all to be here.
latino/latina folks
race
doesnt deserve to live in the us
t/davidson
1.0
1.0
0.0
1.0
woman
Hispanic, female, bisexual
0.0
-1074131302321565002
32K26U12DNI05GCOP508OFZPVC8DV7
mod-liberal
hisp
29.0
Why do so many filthy wetback half-breed spic savages live in #LosAngeles? None of them have any right at all to be here.
latino/latina folks
race
dirty
t/davidson
1.0
1.0
0.0
1.0
man
0.0
7891575995618716029
32K26U12DNI05GCOP508OFZPVC8DV7
mod-liberal
white
40.0
Why do so many filthy wetback half-breed spic savages live in #LosAngeles? None of them have any right at all to be here.
latino/latina folks
race
latino people are savages
t/davidson
1.0
1.0
0.0
1.0
man
0.0
7891575995618716029
32K26U12DNI05GCOP508OFZPVC8DV7
mod-liberal
white
40.0
Why do so many filthy wetback half-breed spic savages live in #LosAngeles? None of them have any right at all to be here.
latino/latina folks
race
latino people don't belong in the us
t/davidson
0.0
0.0
0.0
woman
-5471608738051449110
34KYK9TV2R2RYRX0HE4XI2RITN2SBA
other
white
35.0
It's scheduled up already, tho #bitches> #BryanCranston> One tweet and he's got #JoePesci out there making fun of #stolenbases> #BASEBALL
t/davidson
0.0
0.33
0.0
0.5
man
7891575995618716029
34KYK9TV2R2RYRX0HE4XI2RITN2SBA
mod-liberal
white
40.0
It's scheduled up already, tho #bitches> #BryanCranston> One tweet and he's got #JoePesci out there making fun of #stolenbases> #BASEBALL
t/davidson
0.33
0.0
0.0
man
white, male
1389628790122253253
34KYK9TV2R2RYRX0HE4XI2RITN2SBA
cons
white
31.0
It's scheduled up already, tho #bitches> #BryanCranston> One tweet and he's got #JoePesci out there making fun of #stolenbases> #BASEBALL
t/davidson
1.0
0.33
0.0
0.0
woman
-4671295340405687436
3Q9SPIIRWJGFMY9HV5E2KGAXHIDAWC
other
white
31.0
Knowing what you want doesn't make you a bitch. It makes you a woman who won't settle for anything less than what she deserves.
t/davidson
0.0
0.0
0.0
man
-7452610791699819066
3Q9SPIIRWJGFMY9HV5E2KGAXHIDAWC
liberal
asian
23.0
Knowing what you want doesn't make you a bitch. It makes you a woman who won't settle for anything less than what she deserves.
t/davidson
0.0
0.33
0.0
1.0
man
1558992385113521549
3Q9SPIIRWJGFMY9HV5E2KGAXHIDAWC
libert
white
39.0
Knowing what you want doesn't make you a bitch. It makes you a woman who won't settle for anything less than what she deserves.
t/davidson
1.0
0.66
0.0
0.5
woman
0.0
-8935932304856669427
306W7JMRYYS9F7IR3GYS1BRHHTVB8T
liberal
white
45.0
If a bitch is bad n single she single fo a reason....hoe prolly cray or a runna
women
gender
single women are crazy
t/davidson
1.0
0.66
0.0
0.5
woman
0.0
-8935932304856669427
306W7JMRYYS9F7IR3GYS1BRHHTVB8T
liberal
white
45.0
If a bitch is bad n single she single fo a reason....hoe prolly cray or a runna
women
gender
single women are runners
t/davidson
1.0
1.0
0.0
1.0
man
0.0
3541686222721437796
37SDSEDIN9W8290Z1J53VZE8VH4180
liberal
white
37.0
RT @UNCLERUCKUS_BD: Being a nigger isn't a color it's a state of mind
black folks
race
are niggers
t/davidson
1.0
1.0
0.0
1.0
man
0.0
3541686222721437796
37SDSEDIN9W8290Z1J53VZE8VH4180
liberal
white
37.0
RT @UNCLERUCKUS_BD: Being a nigger isn't a color it's a state of mind
black folks
race
are subhuman
t/davidson
1.0
1.0
0.0
1.0
woman
0.0
2428559182657536645
37SDSEDIN9W8290Z1J53VZE8VH4180
liberal
white
29.0
RT @UNCLERUCKUS_BD: Being a nigger isn't a color it's a state of mind
black folks
race
racial slur
t/davidson
1.0
1.0
0.0
1.0
woman
0.0
2428559182657536645
37SDSEDIN9W8290Z1J53VZE8VH4180
liberal
white
29.0
RT @UNCLERUCKUS_BD: Being a nigger isn't a color it's a state of mind
black folks
race
t/davidson
1.0
1.0
0.0
1.0
man
0.0
3014462452121555470
37SDSEDIN9W8290Z1J53VZE8VH4180
mod-liberal
white
38.0
RT @UNCLERUCKUS_BD: Being a nigger isn't a color it's a state of mind
black folks
race
blacks are worthless
t/davidson
1.0
1.0
0.0
1.0
man
0.0
3014462452121555470
37SDSEDIN9W8290Z1J53VZE8VH4180
mod-liberal
white
38.0
RT @UNCLERUCKUS_BD: Being a nigger isn't a color it's a state of mind
black folks
race
blacks are criminals
t/davidson
0.0
0.0
0.0
woman
955703508749055793
3VDVA3ILID9NVO6HKHHRNCV6J0H1GG
liberal
asian
23.0
We live ina generation where we praise these hoes.
t/davidson
0.33
0.0
0.0
woman
female
5330619308534303933
3VDVA3ILID9NVO6HKHHRNCV6J0H1GG
mod-cons
white
35.0
We live ina generation where we praise these hoes.
t/davidson
0.0
0.0
0.0
man
3541686222721437796
3VDVA3ILID9NVO6HKHHRNCV6J0H1GG
liberal
white
37.0
We live ina generation where we praise these hoes.
t/davidson
0.0
0.66
0.0
0.5
man
-7790343187498138255
3AQN9REUTFAH9A7TUGNSVE3JDTADYG
cons
white
49.0
Let a bitch be a bitch n a hoe be a hoe!
t/davidson
0.0
1.0
0.0
1.0
woman
-5471608738051449110
3AQN9REUTFAH9A7TUGNSVE3JDTADYG
other
white
35.0
Let a bitch be a bitch n a hoe be a hoe!
t/davidson
0.0
0.33
0.0
1.0
man
1558992385113521549
3AQN9REUTFAH9A7TUGNSVE3JDTADYG
libert
white
39.0
Let a bitch be a bitch n a hoe be a hoe!
t/davidson
0.0
1.0
0.0
1.0
woman
7912096326098817047
3OB6JN3A9QJBG31KTEU168XGJKNMR5
liberal
white
32.0
RT @AaronTheGoat: This bitch can't be serious... http://t.co/dVZegKeBGv
t/davidson
0.0
0.66
0.0
1.0
woman
8789702980570783632
3OB6JN3A9QJBG31KTEU168XGJKNMR5
other
hisp
27.0
RT @AaronTheGoat: This bitch can't be serious... http://t.co/dVZegKeBGv
t/davidson
0.0
0.33
0.0
0.5
woman
3169503690680674782
3OB6JN3A9QJBG31KTEU168XGJKNMR5
liberal
white
29.0
RT @AaronTheGoat: This bitch can't be serious... http://t.co/dVZegKeBGv
t/davidson
0.0
1.0
0.0
1.0
man
-2877140746380073212
3UEBBGULPFIXQL1KBLLTGT0RTIFUFA
cons
white
42.0
Awwwwww they no Kaep ain't rape no bitch!!!!
t/davidson
End of preview (truncated to 100 rows)

Dataset Card for "social_bias_frames"

Dataset Summary

Warning: this document and dataset contain content that may be offensive or upsetting.

Social Bias Frames is a new way of representing the biases and offensiveness that are implied in language. For example, these frames are meant to distill the implication that "women (candidates) are less qualified" behind the statement "we shouldn’t lower our standards to hire more women." The Social Bias Inference Corpus (SBIC) supports large-scale learning and evaluation of social implications with over 150k structured annotations of social media posts, spanning over 34k implications about a thousand demographic groups.

Supported Tasks and Leaderboards

This dataset supports both classification and generation. Sap et al. developed several models using the SBIC. They report an F1 score of 78.8 in predicting whether the posts in the test set were offensive, an F1 score of 78.6 in predicting whether the posts were intending to be offensive, an F1 score of 80.7 in predicting whether the posts were lewd, and an F1 score of 69.9 in predicting whether the posts were targeting a specific group.

Another of Sap et al.’s models performed better in the generation task. They report a BLUE score of 77.9, a Rouge-L score of 68.7, and a WMD score of 0.74 in generating a description of the targeted group given a post as well as a BLUE score of 52.6, a Rouge-L score of 44.9, and a WMD score of 2.79 in generating a description of the implied offensive statement given a post. See the paper for further details.

Languages

The language in SBIC is predominantly white-aligned English (78%, using a lexical dialect detector, Blodgett et al., 2016). The curators find less than 10% of posts in SBIC are detected to have the AAE dialect category. The BCP-47 language tag is, presumably, en-US.

Dataset Structure

Data Instances

Each instance contains a post that may contain an offensive statement and annotated information concerning the nature of the offensive implication as well as the demographics of the annotator and origin of the post. See the Social Bias Frames dataset viewer to explore more examples.

default

  • Size of downloaded dataset files: 6.03 MB
  • Size of the generated dataset: 42.41 MB
  • Total amount of disk used: 48.45 MB

An example of 'validation' looks as follows.

{
    "HITId": "3Z8UJEJOCZ74QHXOTFP0JOOGD7039J",
    "WorkerId": "7162172225567546910",
    "annotatorAge": "27.0",
    "annotatorGender": "woman",
    "annotatorMinority": "women",
    "annotatorPolitics": "liberal",
    "annotatorRace": "white",
    "dataSource": "t/davidson",
    "intentYN": "1.0",
    "offensiveYN": "1.0",
    "post": "Every half-breed cop in America is trying to rape white women.",
    "sexPhrase": "rape white women",
    "sexReason": "rape",
    "sexYN": "1.0",
    "speakerMinorityYN": "0.0",
    "targetCategory": "race",
    "targetMinority": "mixed folks",
    "targetStereotype": "mixed folks are rapists.",
    "whoTarget": "1.0"
}

Data Fields

The data fields are the same among all splits.

default

  • whoTarget: a string, ‘0.0’ if the target is a group, ‘1.0’ if the target is an individual, and blank if the post is not offensive
  • intentYN: a string indicating if the intent behind the statement was to offend. This is a categorical variable with four possible answers, ‘1.0’ if yes, ‘0.66’ if probably, ‘0.33’ if probably not, and ‘0.0’ if no.
  • sexYN: a string indicating whether the post contains a sexual or lewd reference. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no.
  • sexReason: a string containing a free text explanation of what is sexual if indicated so, blank otherwise
  • offensiveYN: a string indicating if the post could be offensive to anyone. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no.
  • annotatorGender: a string indicating the gender of the MTurk worker
  • annotatorMinority: a string indicating whether the MTurk worker identifies as a minority
  • sexPhrase: a string indicating which part of the post references something sexual, blank otherwise
  • speakerMinorityYN: a string indicating whether the speaker was part of the same minority group that's being targeted. This is a categorical variable with three possible answers, ‘1.0’ if yes, ‘0.5’ if maybe, ‘0.0’ if no.
  • WorkerId: a string hashed version of the MTurk workerId
  • HITId: a string id that uniquely identifies each post
  • annotatorPolitics: a string indicating the political leaning of the MTurk worker
  • annotatorRace: a string indicating the race of the MTurk worker
  • annotatorAge: a string indicating the age of the MTurk worker
  • post: a string containing the text of the post that was annotated
  • targetMinority: a string indicating the demographic group targeted
  • targetCategory: a string indicating the high-level category of the demographic group(s) targeted
  • targetStereotype: a string containing the implied statement
  • dataSource: a string indicating the source of the post (t/...: means Twitter, r/...: means a subreddit)

Data Splits

To ensure that no post appeared in multiple splits, the curators defined a training instance as the post and its three sets of annotations. They then split the dataset into train, validation, and test sets (75%/12.5%/12.5%).

name train validation test
default 112900 16738 17501

Dataset Creation

Curation Rationale

The main aim for this dataset is to cover a wide variety of social biases that are implied in text, both subtle and overt, and make the biases representative of real world discrimination that people experience RWJF 2017. The curators also included some innocuous statements, to balance out biases, offensive, or harmful content.

Source Data

The curators included online posts from the following sources sometime between 2014-2019:

Initial Data Collection and Normalization

The curators wanted posts to be as self-contained as possible, therefore, they applied some filtering to prevent posts from being highly context-dependent. For Twitter data, they filtered out @-replies, retweets, and links, and subsample posts such that there is a smaller correlation between AAE and offensiveness (to avoid racial bias; Sap et al., 2019). For Reddit, Gab, and Stormfront, they only selected posts that were one sentence long, don't contain links, and are between 10 and 80 words. Furthemore, for Reddit, they automatically removed posts that target automated moderation.

Who are the source language producers?

Due to the nature of this corpus, there is no way to know who the speakers are. But, the speakers of the Reddit, Gab, and Stormfront posts are likely white men (see Gender by subreddit, Gab users, Stormfront description).

Annotations

Annotation process

For each post, Amazon Mechanical Turk workers indicate whether the post is offensive, whether the intent was to offend, and whether it contains lewd or sexual content. Only if annotators indicate potential offensiveness do they answer the group implication question. If the post targets or references a group or demographic, workers select or write which one(s); per selected group, they then write two to four stereotypes. Finally, workers are asked whether they think the speaker is part of one of the minority groups referenced by the post. The curators collected three annotations per post, and restricted the worker pool to the U.S. and Canada. The annotations in SBIC showed 82.4% pairwise agreement and Krippendorf’s α=0.45 on average.

Recent work has highlighted various negative side effects caused by annotating potentially abusive or harmful content (e.g., acute stress; Roberts, 2016). The curators mitigated these by limiting the number of posts that one worker could annotate in one day, paying workers above minimum wage ($7–12), and providing crisis management resources to the annotators.

Who are the annotators?

The annotators are Amazon Mechanical Turk workers aged 36±10 years old. The annotators consisted of 55% women, 42% men, and <1% non-binary and 82% identified as White, 4% Asian, 4% Hispanic, 4% Black. Information on their first language(s) and professional backgrounds was not collected.

Personal and Sensitive Information

Usernames are not included with the data, but the site where the post was collected is, so the user could potentially be recovered.

Considerations for Using the Data

Social Impact of Dataset

The curators recognize that studying Social Bias Frames necessarily requires confronting online content that may be offensive or disturbing but argue that deliberate avoidance does not eliminate such problems. By assessing social media content through the lens of Social Bias Frames, automatic flagging or AI-augmented writing interfaces may be analyzed for potentially harmful online content with detailed explanations for users or moderators to consider and verify. In addition, the collective analysis over large corpora can also be insightful for educating people on reducing unconscious biases in their language by encouraging empathy towards a targeted group.

Discussion of Biases

Because this is a corpus of social biases, a lot of posts contain implied or overt biases against the following groups (in decreasing order of prevalence):

  • gender/sexuality
  • race/ethnicity
  • religion/culture
  • social/political
  • disability body/age
  • victims

The curators warn that technology trained on this dataset could have side effects such as censorship and dialect-based racial bias.

Other Known Limitations

Because the curators found that the dataset is predominantly written in White-aligned English, they caution researchers to consider the potential for dialect or identity-based biases in labelling (Davidson et al.,2019; Sap et al., 2019a) before deploying technology based on SBIC.

Additional Information

Dataset Curators

This dataset was developed by Maarten Sap of the Paul G. Allen School of Computer Science & Engineering at the University of Washington, Saadia Gabriel, Lianhui Qin, Noah A Smith, and Yejin Choi of the Paul G. Allen School of Computer Science & Engineering and the Allen Institute for Artificial Intelligence, and Dan Jurafsky of the Linguistics & Computer Science Departments of Stanford University.

Licensing Information

The SBIC is licensed under the Creative Commons 4.0 License

Citation Information

@inproceedings{sap-etal-2020-social,
    title = "Social Bias Frames: Reasoning about Social and Power Implications of Language",
    author = "Sap, Maarten  and
      Gabriel, Saadia  and
      Qin, Lianhui  and
      Jurafsky, Dan  and
      Smith, Noah A.  and
      Choi, Yejin",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.486",
    doi = "10.18653/v1/2020.acl-main.486",
    pages = "5477--5490",
    abstract = "Warning: this paper contains content that may be offensive or upsetting. Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge is that it is rarely what is stated explicitly, but rather the implied meanings, that frame people{'}s judgments about others. For example, given a statement that {``}we shouldn{'}t lower our standards to hire more women,{''} most listeners will infer the implicature intended by the speaker - that {``}women (candidates) are less qualified.{''} Most semantic formalisms, to date, do not capture such pragmatic implications in which people express social biases and power differentials in language. We introduce Social Bias Frames, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes onto others. In addition, we introduce the Social Bias Inference Corpus to support large-scale modelling and evaluation with 150k structured annotations of social media posts, covering over 34k implications about a thousand demographic groups. We then establish baseline approaches that learn to recover Social Bias Frames from unstructured text. We find that while state-of-the-art neural models are effective at high-level categorization of whether a given statement projects unwanted social bias (80{\%} F1), they are not effective at spelling out more detailed explanations in terms of Social Bias Frames. Our study motivates future work that combines structured pragmatic inference with commonsense reasoning on social implications.",
}

Contributions

Thanks to @thomwolf, @lewtun, @otakumesi, @mariamabarham, @lhoestq for adding this dataset.

Update on GitHub