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stringclasses
3 values
sexReason
stringlengths
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199
offensiveYN
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4 values
annotatorGender
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6 values
annotatorMinority
stringlengths
0
70
sexPhrase
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265
speakerMinorityYN
stringclasses
4 values
WorkerId
stringlengths
16
20
HITId
stringlengths
30
30
annotatorPolitics
stringclasses
8 values
annotatorRace
stringclasses
8 values
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post
stringlengths
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1.22k
targetMinority
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0
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targetCategory
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8 values
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dataSource
stringclasses
12 values
0.0
0.66
0.0
1.0
woman
-8935932304856669427
363A7XIFV4G2799C5V96YERJA9AVAM
liberal
white
45.0
RT @_LexC__: I'm convinced that some of y'all bitches get pregnant purposely because "birth control & plan b pills" are effective 😕🐸☕️
t/davidson
0.0
0.66
0.0
0.5
man
6347880360297734464
363A7XIFV4G2799C5V96YERJA9AVAM
mod-liberal
white
35.0
RT @_LexC__: I'm convinced that some of y'all bitches get pregnant purposely because "birth control & plan b pills" are effective 😕🐸☕️
t/davidson
0.0
0.33
0.0
0.5
man
-7452610791699819066
363A7XIFV4G2799C5V96YERJA9AVAM
liberal
asian
23.0
RT @_LexC__: I'm convinced that some of y'all bitches get pregnant purposely because "birth control & plan b pills" are effective 😕🐸☕️
t/davidson
1.0
1.0
0.0
1.0
man
0.0
-500114755446676507
3JTPR5MTZS6RLS3JBV4IOU0G2X35K5
liberal
white
25.0
RT @iBeZo: Stupid fucking nigger LeBron. You flopping stupid jungle bunny monkey faggot.
black folks
race
all stupid
t/davidson
1.0
1.0
0.0
1.0
man
0.0
-500114755446676507
3JTPR5MTZS6RLS3JBV4IOU0G2X35K5
liberal
white
25.0
RT @iBeZo: Stupid fucking nigger LeBron. You flopping stupid jungle bunny monkey faggot.
black folks
race
are not people but apes.
t/davidson
1.0
1.0
0.0
1.0
woman
0.0
7912096326098817047
3JTPR5MTZS6RLS3JBV4IOU0G2X35K5
liberal
white
32.0
RT @iBeZo: Stupid fucking nigger LeBron. You flopping stupid jungle bunny monkey faggot.
black folks
race
black people are monkeys
t/davidson
1.0
1.0
0.0
1.0
woman
0.0
7912096326098817047
3JTPR5MTZS6RLS3JBV4IOU0G2X35K5
liberal
white
32.0
RT @iBeZo: Stupid fucking nigger LeBron. You flopping stupid jungle bunny monkey faggot.
black folks
race
black people are from the jungles
t/davidson
1.0
1.0
0.0
1.0
woman
0.0
7912096326098817047
3JTPR5MTZS6RLS3JBV4IOU0G2X35K5
liberal
white
32.0
RT @iBeZo: Stupid fucking nigger LeBron. You flopping stupid jungle bunny monkey faggot.
black folks
race
using racial slurs is okay
t/davidson
1.0
1.0
0.0
1.0
woman
gender, sexuality, mental illness
0.0
118631008378348260
3JTPR5MTZS6RLS3JBV4IOU0G2X35K5
liberal
white
28.0
RT @iBeZo: Stupid fucking nigger LeBron. You flopping stupid jungle bunny monkey faggot.
black folks
race
black folks are animals
t/davidson
1.0
1.0
0.0
1.0
woman
gender, sexuality, mental illness
0.0
118631008378348260
3JTPR5MTZS6RLS3JBV4IOU0G2X35K5
liberal
white
28.0
RT @iBeZo: Stupid fucking nigger LeBron. You flopping stupid jungle bunny monkey faggot.
black folks
race
black folks are stupid
t/davidson
1.0
1.0
0.0
1.0
man
1.0
2430897476320006833
3OKP4QVBP2RJPCJHG0AKYDO9NUNAGD
cons
white
32.0
I need a girl from Jamaica I can't fuck with these basic black bitches
black folks
race
black women are basic
t/davidson
1.0
1.0
0.0
1.0
man
1.0
2430897476320006833
3OKP4QVBP2RJPCJHG0AKYDO9NUNAGD
cons
white
32.0
I need a girl from Jamaica I can't fuck with these basic black bitches
black folks
race
t/davidson
1.0
1.0
0.0
1.0
man
white, male
0.0
1389628790122253253
3WGCNLZJKF2KJ4WQIRGJHRS9681D1I
cons
white
31.0
Molly make bitches crazy
women
gender
women can't handle drugs
t/davidson
1.0
1.0
0.0
1.0
man
white, male
0.0
1389628790122253253
3WGCNLZJKF2KJ4WQIRGJHRS9681D1I
cons
white
31.0
Molly make bitches crazy
women
gender
women are crazy
t/davidson
0.0
0.0
0.0
woman
6927542478311092738
3HRWUH63QUWSTYOM88WYGMHKWS85NA
liberal
white
24.0
RT @rihanna: I ain't got no type...bad bitches is the only thing that I like
t/davidson
0.33
0.0
0.0
woman
women
-6837958490067487319
3HRWUH63QUWSTYOM88WYGMHKWS85NA
liberal
white
34.0
RT @rihanna: I ain't got no type...bad bitches is the only thing that I like
t/davidson
0.0
0.0
0.0
woman
none
-4903257119943710595
3HRWUH63QUWSTYOM88WYGMHKWS85NA
liberal
white
32.0
RT @rihanna: I ain't got no type...bad bitches is the only thing that I like
t/davidson
1.0
1.0
0.0
1.0
woman
0.5
8789702980570783632
3P888QFVX3OZ457WTFKETMCGIYZQOM
other
hisp
27.0
This niggga said I be branding bitches 😹😂😂😂😂😏
black folks
race
black folks brand women
t/davidson
1.0
1.0
0.0
1.0
woman
0.5
8789702980570783632
3P888QFVX3OZ457WTFKETMCGIYZQOM
other
hisp
27.0
This niggga said I be branding bitches 😹😂😂😂😂😏
black folks
race
black folks think they own women
t/davidson
1.0
0.66
0.0
0.5
man
0.0
4554125108380068779
3P888QFVX3OZ457WTFKETMCGIYZQOM
mod-liberal
white
27.0
This niggga said I be branding bitches 😹😂😂😂😂😏
women
gender
women are property to some men
t/davidson
1.0
0.66
0.0
0.5
man
0.0
4554125108380068779
3P888QFVX3OZ457WTFKETMCGIYZQOM
mod-liberal
white
27.0
This niggga said I be branding bitches 😹😂😂😂😂😏
women
gender
women get "branded" by a man
t/davidson
1.0
1.0
0.0
1.0
woman
0.0
-8810733183869606469
35O6H0UNLSAWA8X1TA46NF03N835J6
liberal
white
34.0
It's funny when u get a black female mad they start acting ghetto then a bitch
black folks
race
black women are trashy.
t/davidson
1.0
1.0
0.0
1.0
woman
0.0
-8810733183869606469
35O6H0UNLSAWA8X1TA46NF03N835J6
liberal
white
34.0
It's funny when u get a black female mad they start acting ghetto then a bitch
black folks
race
black women are easily angered.
t/davidson
1.0
1.0
0.0
1.0
woman
female
0.0
5330619308534303933
35O6H0UNLSAWA8X1TA46NF03N835J6
mod-cons
white
35.0
It's funny when u get a black female mad they start acting ghetto then a bitch
black women
gender
are loud and mean
t/davidson
1.0
1.0
0.0
1.0
woman
female
0.0
5330619308534303933
35O6H0UNLSAWA8X1TA46NF03N835J6
mod-cons
white
35.0
It's funny when u get a black female mad they start acting ghetto then a bitch
black women
gender
are all from the ghetto
t/davidson
0.0
0.66
0.0
1.0
man
-7452610791699819066
36FQTHX3Z3LNEACLRZ3WAUJ61733BT
liberal
asian
23.0
RT @_danieebro: all these bitches fake if you ask me
t/davidson
0.0
1.0
0.0
1.0
man
-2877140746380073212
36FQTHX3Z3LNEACLRZ3WAUJ61733BT
cons
white
42.0
RT @_danieebro: all these bitches fake if you ask me
t/davidson
0.0
0.0
0.0
man
3541686222721437796
36FQTHX3Z3LNEACLRZ3WAUJ61733BT
liberal
white
37.0
RT @_danieebro: all these bitches fake if you ask me
t/davidson
0.0
1.0
0.0
1.0
woman
-5471608738051449110
3L21G7IH47QNHFRHEN3XUC2AOW6Y1A
other
white
35.0
RT @__nijel: Niggas be out here loving ALL they hoes...that's a no no.
t/davidson
0.0
1.0
0.0
1.0
woman
2428559182657536645
3L21G7IH47QNHFRHEN3XUC2AOW6Y1A
liberal
white
29.0
RT @__nijel: Niggas be out here loving ALL they hoes...that's a no no.
t/davidson
0.33
0.0
0.0
woman
women
-1563458821535984332
3L21G7IH47QNHFRHEN3XUC2AOW6Y1A
liberal
white
22.0
RT @__nijel: Niggas be out here loving ALL they hoes...that's a no no.
t/davidson
1.0
0.66
0.0
1.0
woman
0.5
980292540985390308
3BO3NEOQM0BXL3PC6OANJP1P27FAI5
liberal
white
51.0
A guy claiming to be Republican called C-Span news and said "Republicans hate that nigger Obama" https://t.co/XZe0u7guZM
conservatives
social
conservatives hate black people.
t/davidson
1.0
0.66
0.0
1.0
woman
0.5
980292540985390308
3BO3NEOQM0BXL3PC6OANJP1P27FAI5
liberal
white
51.0
A guy claiming to be Republican called C-Span news and said "Republicans hate that nigger Obama" https://t.co/XZe0u7guZM
conservatives
social
all conservatives hate obama because he is black.
t/davidson
1.0
1.0
0.0
1.0
woman
Woman, African American
0.0
7067515179960755087
3BO3NEOQM0BXL3PC6OANJP1P27FAI5
mod-liberal
black
30.0
A guy claiming to be Republican called C-Span news and said "Republicans hate that nigger Obama" https://t.co/XZe0u7guZM
black folks
race
nigger is a derogatory term for black people
t/davidson
1.0
1.0
0.0
1.0
woman
Woman, African American
0.0
7067515179960755087
3BO3NEOQM0BXL3PC6OANJP1P27FAI5
mod-liberal
black
30.0
A guy claiming to be Republican called C-Span news and said "Republicans hate that nigger Obama" https://t.co/XZe0u7guZM
black folks
race
black people were called niggers
t/davidson
1.0
1.0
0.0
1.0
man
white, male
0.0
1389628790122253253
3BO3NEOQM0BXL3PC6OANJP1P27FAI5
cons
white
31.0
A guy claiming to be Republican called C-Span news and said "Republicans hate that nigger Obama" https://t.co/XZe0u7guZM
black folks
race
said niggas
t/davidson
1.0
1.0
0.0
1.0
man
white, male
0.0
1389628790122253253
3BO3NEOQM0BXL3PC6OANJP1P27FAI5
cons
white
31.0
A guy claiming to be Republican called C-Span news and said "Republicans hate that nigger Obama" https://t.co/XZe0u7guZM
black folks
race
republicans don't like blacks
t/davidson
1.0
1.0
0.0
1.0
man
white, male
0.0
1389628790122253253
3BO3NEOQM0BXL3PC6OANJP1P27FAI5
cons
white
31.0
A guy claiming to be Republican called C-Span news and said "Republicans hate that nigger Obama" https://t.co/XZe0u7guZM
republicans
social
all republicans don't like black people
t/davidson
1.0
1.0
0.0
1.0
man
white, male
0.0
1389628790122253253
3BO3NEOQM0BXL3PC6OANJP1P27FAI5
cons
white
31.0
A guy claiming to be Republican called C-Span news and said "Republicans hate that nigger Obama" https://t.co/XZe0u7guZM
republicans
social
republicans are racist.
t/davidson
0.0
0.66
0.0
1.0
man
-7452610791699819066
3VW0145YLY6UIFI7L9LS4HBP93TMJD
liberal
asian
23.0
I don't love you hoes.
t/davidson
0.0
0.66
0.0
0.5
man
-7790343187498138255
3VW0145YLY6UIFI7L9LS4HBP93TMJD
cons
white
49.0
I don't love you hoes.
t/davidson
0.0
0.66
0.0
0.5
woman
1869048359460155317
3VW0145YLY6UIFI7L9LS4HBP93TMJD
mod-cons
white
22.0
I don't love you hoes.
t/davidson
0.0
0.66
0.0
1.0
man
-2877140746380073212
3L55D8AUFAR74I1FUKSHB8L1STYCYF
cons
white
42.0
RT @t_grun: Yeah I checked my connection, I have fucking wifi. Did you check if you were being a lil bitch nigga? http://t.co/0kvF0REwjd
t/davidson
0.0
1.0
0.0
1.0
woman
1869048359460155317
3L55D8AUFAR74I1FUKSHB8L1STYCYF
mod-cons
white
22.0
RT @t_grun: Yeah I checked my connection, I have fucking wifi. Did you check if you were being a lil bitch nigga? http://t.co/0kvF0REwjd
t/davidson
0.0
0.66
0.0
1.0
man
-7713443391178782699
3L55D8AUFAR74I1FUKSHB8L1STYCYF
mod-liberal
white
27.0
RT @t_grun: Yeah I checked my connection, I have fucking wifi. Did you check if you were being a lil bitch nigga? http://t.co/0kvF0REwjd
t/davidson
1.0
1.0
0.0
1.0
man
0.0
-3579354685786976851
3SA4EMRVJVWT1CEYRH3E3GQOFZY0PG
mod-cons
white
35.0
@JustDoItSlow on sale ass hoes
women
gender
women are sex objects
t/davidson
1.0
1.0
0.0
1.0
man
0.0
-3579354685786976851
3SA4EMRVJVWT1CEYRH3E3GQOFZY0PG
mod-cons
white
35.0
@JustDoItSlow on sale ass hoes
women
gender
women are refereed to as a derogatory word
t/davidson
0.0
0.0
0.0
woman
none
-4903257119943710595
37MQ8Z1JQEQU609DXR3BZVCFG2KY2R
liberal
white
32.0
Up too early the damn birds are still chirping smh.
t/davidson
0.0
0.33
0.0
1.0
woman
-4671295340405687436
37MQ8Z1JQEQU609DXR3BZVCFG2KY2R
other
white
31.0
Up too early the damn birds are still chirping smh.
t/davidson
0.0
0.0
0.0
woman
2428559182657536645
37MQ8Z1JQEQU609DXR3BZVCFG2KY2R
liberal
white
29.0
Up too early the damn birds are still chirping smh.
t/davidson
1.0
1.0
0.0
1.0
man
0.0
4554125108380068779
3RIHDBQ1NESYR9NHX9I7S4VAWBPHM3
mod-liberal
white
27.0
The emoji I use when I'm flirting wit bitches ---> 😈
women
gender
degrades women by calling them names
t/davidson
1.0
1.0
0.0
1.0
man
0.0
4554125108380068779
3RIHDBQ1NESYR9NHX9I7S4VAWBPHM3
mod-liberal
white
27.0
The emoji I use when I'm flirting wit bitches ---> 😈
women
gender
women are called bitches
t/davidson
0.33
0.0
0.0
woman
women
-7090297418013232722
30QQTY5GMKEKBSMET2NHS1NAVUW7UJ
other
white
38.0
I respect any nigga that tell me he got a bitch , so I fall the fuck back no hating 💯
t/davidson
0.0
0.0
0.0
0.5
woman
1869048359460155317
30QQTY5GMKEKBSMET2NHS1NAVUW7UJ
mod-cons
white
22.0
I respect any nigga that tell me he got a bitch , so I fall the fuck back no hating 💯
t/davidson
0.0
0.0
0.0
man
-7790343187498138255
30QQTY5GMKEKBSMET2NHS1NAVUW7UJ
cons
white
49.0
I respect any nigga that tell me he got a bitch , so I fall the fuck back no hating 💯
t/davidson
0.0
1.0
0.0
1.0
woman
2428559182657536645
36818Z1KV37IV07TLL30FJZ0JPI3AY
liberal
white
29.0
RT @Soy_Brent: steal a nigga's bitch, don't steal a nigga's tweets.
t/davidson
0.0
1.0
0.0
1.0
woman
6927542478311092738
36818Z1KV37IV07TLL30FJZ0JPI3AY
liberal
white
24.0
RT @Soy_Brent: steal a nigga's bitch, don't steal a nigga's tweets.
t/davidson
0.0
0.0
0.0
woman
none
-4903257119943710595
36818Z1KV37IV07TLL30FJZ0JPI3AY
liberal
white
32.0
RT @Soy_Brent: steal a nigga's bitch, don't steal a nigga's tweets.
t/davidson
0.0
0.66
0.0
0.5
man
-7790343187498138255
371Q3BEXDH3NGP0GNSK04KYLKQNSZF
cons
white
49.0
RT @caseytu88662466: Y these niggas on this hoe shit
t/davidson
0.0
0.66
0.0
0.5
woman
1869048359460155317
371Q3BEXDH3NGP0GNSK04KYLKQNSZF
mod-cons
white
22.0
RT @caseytu88662466: Y these niggas on this hoe shit
t/davidson
0.0
1.0
1.0
refers to lots of sexual activity
1.0
woman
women
hoe shit
7162172225567546910
371Q3BEXDH3NGP0GNSK04KYLKQNSZF
liberal
white
27.0
RT @caseytu88662466: Y these niggas on this hoe shit
t/davidson
0.0
1.0
women wanting sex
0.0
woman
hoes bent over
6927542478311092738
3VGET1QSZ0TX3WBJO22IAP87GA77WI
liberal
white
24.0
🙌 RT @iDO_me2: this vodka be having all the hoes bent over😩💃
t/davidson
0.0
1.0
1.0
refers to someone that's sexually promiscuous
1.0
woman
Woman, African American
hoes
7067515179960755087
3VGET1QSZ0TX3WBJO22IAP87GA77WI
mod-liberal
black
30.0
🙌 RT @iDO_me2: this vodka be having all the hoes bent over😩💃
t/davidson
0.0
0.66
0.0
0.5
man
-7790343187498138255
3VGET1QSZ0TX3WBJO22IAP87GA77WI
cons
white
49.0
🙌 RT @iDO_me2: this vodka be having all the hoes bent over😩💃
t/davidson
1.0
1.0
0.0
1.0
man
0.0
2385617559681638227
3P7RGTLO6E7ORYF06RVZW5G2UB4AKG
liberal
white
26.0
RT @Tahrell: Now, bitches gonna think just because they're married, they're better than every female that's not. Smh
women
gender
women are bitches
t/davidson
1.0
1.0
0.0
1.0
man
0.0
2385617559681638227
3P7RGTLO6E7ORYF06RVZW5G2UB4AKG
liberal
white
26.0
RT @Tahrell: Now, bitches gonna think just because they're married, they're better than every female that's not. Smh
women
gender
women only want to get married
t/davidson
1.0
1.0
0.0
1.0
woman
women
0.0
2659038022143379063
37OPIVELUUXXP8ES06S2JD0A2U0AHC
mod-cons
white
47.0
RT @TheBloodShow: Ugly hoes be the loudest .. u need to duck off & hide yourself frfr
women
gender
women are loud if they are not good looking
t/davidson
1.0
1.0
0.0
1.0
woman
women
0.0
2659038022143379063
37OPIVELUUXXP8ES06S2JD0A2U0AHC
mod-cons
white
47.0
RT @TheBloodShow: Ugly hoes be the loudest .. u need to duck off & hide yourself frfr
women
gender
women need to hide if they unattractive
t/davidson
1.0
0.66
0.0
1.0
man
0.0
4554125108380068779
37OPIVELUUXXP8ES06S2JD0A2U0AHC
mod-liberal
white
27.0
RT @TheBloodShow: Ugly hoes be the loudest .. u need to duck off & hide yourself frfr
ugly folks
body
ugly women are unwanted
t/davidson
1.0
0.66
0.0
1.0
man
0.0
4554125108380068779
37OPIVELUUXXP8ES06S2JD0A2U0AHC
mod-liberal
white
27.0
RT @TheBloodShow: Ugly hoes be the loudest .. u need to duck off & hide yourself frfr
ugly folks
body
ugly women should disappear
t/davidson
1.0
1.0
0.0
1.0
woman
0.5
-4671295340405687436
3PGQRAZX02ENBZQBR6IHQDPJEWJSYO
other
white
31.0
Fuck all these dirty hoes these bitches basic
women
gender
women are "hoes"
t/davidson
1.0
1.0
0.0
1.0
woman
0.5
-4671295340405687436
3PGQRAZX02ENBZQBR6IHQDPJEWJSYO
other
white
31.0
Fuck all these dirty hoes these bitches basic
women
gender
women are "bitches"
t/davidson
1.0
1.0
1.0
alludes to rape
1.0
woman
none
molly'd
0.5
-3114316428557054065
33Q5P9PUSPG798BKQ6U0PFU3YN2CZ6
mod-cons
white
33.0
bitch probably molly'd the 1st nigga that said good mornin' back to her
women
gender
women are defined by who has sex with them
t/davidson
1.0
1.0
1.0
alludes to rape
1.0
woman
none
molly'd
0.5
-3114316428557054065
33Q5P9PUSPG798BKQ6U0PFU3YN2CZ6
mod-cons
white
33.0
bitch probably molly'd the 1st nigga that said good mornin' back to her
women
gender
women are sex objects
t/davidson
1.0
1.0
1.0
alludes to rape
1.0
woman
none
molly'd
0.5
-3114316428557054065
33Q5P9PUSPG798BKQ6U0PFU3YN2CZ6
mod-cons
white
33.0
bitch probably molly'd the 1st nigga that said good mornin' back to her
black folks
race
black people are niggers
t/davidson
1.0
1.0
1.0
alludes to rape
1.0
woman
none
molly'd
0.5
-3114316428557054065
33Q5P9PUSPG798BKQ6U0PFU3YN2CZ6
mod-cons
white
33.0
bitch probably molly'd the 1st nigga that said good mornin' back to her
black folks
race
black people only think about sex
t/davidson
1.0
0.33
0.0
1.0
woman
women
1.0
7162172225567546910
3H6W48L9F4JM92FJKOWZABZSG4DWPE
liberal
white
27.0
@G_Anderson4 lol it's gonna be so fun got two niglets on the way
black folks
race
black people are niggers.
t/davidson
1.0
0.33
0.0
1.0
woman
women
1.0
7162172225567546910
3H6W48L9F4JM92FJKOWZABZSG4DWPE
liberal
white
27.0
@G_Anderson4 lol it's gonna be so fun got two niglets on the way
black folks
race
black babies are niglets.
t/davidson
1.0
1.0
0.0
1.0
man
0.0
6347880360297734464
3H6W48L9F4JM92FJKOWZABZSG4DWPE
mod-liberal
white
35.0
@G_Anderson4 lol it's gonna be so fun got two niglets on the way
black folks
race
black people are a waste
t/davidson
1.0
1.0
0.0
1.0
man
0.0
6347880360297734464
3H6W48L9F4JM92FJKOWZABZSG4DWPE
mod-liberal
white
35.0
@G_Anderson4 lol it's gonna be so fun got two niglets on the way
black folks
race
black people are a waste
t/davidson
1.0
0.33
0.0
1.0
woman
0.5
1869048359460155317
3H6W48L9F4JM92FJKOWZABZSG4DWPE
mod-cons
white
22.0
@G_Anderson4 lol it's gonna be so fun got two niglets on the way
black folks
race
black folks are n*ggers
t/davidson
1.0
0.33
0.0
1.0
woman
0.5
1869048359460155317
3H6W48L9F4JM92FJKOWZABZSG4DWPE
mod-cons
white
22.0
@G_Anderson4 lol it's gonna be so fun got two niglets on the way
black folks
race
black folks are inferior to whites
t/davidson
1.0
1.0
0.0
1.0
woman
0.5
-4671295340405687436
322ZSN9Z5GE8SSP6RZZ0KTHYLEYT4T
other
white
31.0
U wondering why I'm not answering I'm wondering where my money at niggas act like bitches these days
black folks
race
black people can be called the n word.
t/davidson
1.0
1.0
0.0
1.0
woman
0.5
-4671295340405687436
322ZSN9Z5GE8SSP6RZZ0KTHYLEYT4T
other
white
31.0
U wondering why I'm not answering I'm wondering where my money at niggas act like bitches these days
black folks
race
black people were slaves.
t/davidson
1.0
0.66
0.0
1.0
man
1.0
1558992385113521549
322ZSN9Z5GE8SSP6RZZ0KTHYLEYT4T
libert
white
39.0
U wondering why I'm not answering I'm wondering where my money at niggas act like bitches these days
black folks
race
blacks are in debt
t/davidson
1.0
0.66
0.0
1.0
man
1.0
1558992385113521549
322ZSN9Z5GE8SSP6RZZ0KTHYLEYT4T
libert
white
39.0
U wondering why I'm not answering I'm wondering where my money at niggas act like bitches these days
black folks
race
blacks act like women
t/davidson
0.0
0.66
0.0
0.5
man
-7790343187498138255
33NKDW9FFXCI4DB5PO6FSUESQN4CX9
cons
white
49.0
RT @MsKeShera_: these bitches be sending shots but be missing me ian been @'d yet you cant be dissing me!
t/davidson
0.0
0.66
0.0
1.0
woman
racial minority, gender
4224836053735053558
33NKDW9FFXCI4DB5PO6FSUESQN4CX9
liberal
asian
25.0
RT @MsKeShera_: these bitches be sending shots but be missing me ian been @'d yet you cant be dissing me!
t/davidson
0.0
1.0
0.0
1.0
man
0.0
-5870881806826799680
33NKDW9FFXCI4DB5PO6FSUESQN4CX9
liberal
white
38.0
RT @MsKeShera_: these bitches be sending shots but be missing me ian been @'d yet you cant be dissing me!
t/davidson
1.0
0.66
1.0
they're talking about having sex with minorities
1.0
woman
fuck exotic bitches!
0.0
6650634677954316496
3UUSLRKAULXYUQBS77XSI1JM9U1D7F
libert
white
25.0
I just wanna get lifted & fuck exotic bitches!
women
gender
women are only good for sex
t/davidson
1.0
0.66
1.0
they're talking about having sex with minorities
1.0
woman
fuck exotic bitches!
0.0
6650634677954316496
3UUSLRKAULXYUQBS77XSI1JM9U1D7F
libert
white
25.0
I just wanna get lifted & fuck exotic bitches!
women
gender
women deserve name calling
t/davidson
0.0
0.0
0.0
woman
2428559182657536645
3BAWBGQGYLTKO7BEYSXMONPT9157V8
liberal
white
29.0
people be shooting their guns around here late as fuck got me thinking they tryna shoot my black ass 4 jammin jiggaboo music
t/davidson
0.33
0.0
0.0
woman
women
-1563458821535984332
3BAWBGQGYLTKO7BEYSXMONPT9157V8
liberal
white
22.0
people be shooting their guns around here late as fuck got me thinking they tryna shoot my black ass 4 jammin jiggaboo music
t/davidson
0.0
0.33
0.0
1.0
woman
-8935932304856669427
3BAWBGQGYLTKO7BEYSXMONPT9157V8
liberal
white
45.0
people be shooting their guns around here late as fuck got me thinking they tryna shoot my black ass 4 jammin jiggaboo music
t/davidson
0.33
0.0
0.0
man
6164268409378511170
367O8HRHKG27DSCR2ZUAAOLCJVLS44
libert
white
49.0
I'm flexin on these bitches all gold bottles no Bombay
t/davidson
0.0
1.0
0.0
1.0
woman
2428559182657536645
367O8HRHKG27DSCR2ZUAAOLCJVLS44
liberal
white
29.0
I'm flexin on these bitches all gold bottles no Bombay
t/davidson
0.0
0.33
0.0
0.5
man
-2877140746380073212
367O8HRHKG27DSCR2ZUAAOLCJVLS44
cons
white
42.0
I'm flexin on these bitches all gold bottles no Bombay
t/davidson
1.0
1.0
1.0
Sexual reference
1.0
man
ass sucked
0.0
6164268409378511170
31HLTCK4BLP3H0MFRV1GU02M84NVGY
libert
white
49.0
@Arbogucci guys not supposed to get their ass sucked queer
gay men
gender
gay people are disgusting
t/davidson
1.0
1.0
1.0
Sexual reference
1.0
man
ass sucked
0.0
6164268409378511170
31HLTCK4BLP3H0MFRV1GU02M84NVGY
libert
white
49.0
@Arbogucci guys not supposed to get their ass sucked queer
gay men
gender
gay people are unnatural
t/davidson
1.0
1.0
1.0
reference to oral sex
1.0
man
guys not supposed to get their ass sucked queer
0.0
7891575995618716029
31HLTCK4BLP3H0MFRV1GU02M84NVGY
mod-liberal
white
40.0
@Arbogucci guys not supposed to get their ass sucked queer
gay men
gender
gay men are into weird sex
t/davidson

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鈥檛 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.鈥檚 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: 6.32 MB
  • Size of the generated dataset: 44.47 MB
  • Total amount of disk used: 50.80 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, 鈥0.0鈥 if the target is a group, 鈥1.0鈥 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, 鈥1.0鈥 if yes, 鈥0.66鈥 if probably, 鈥0.33鈥 if probably not, and 鈥0.0鈥 if no.
  • sexYN: a string indicating whether the post contains a sexual or lewd reference. This is a categorical variable with three possible answers, 鈥1.0鈥 if yes, 鈥0.5鈥 if maybe, 鈥0.0鈥 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, 鈥1.0鈥 if yes, 鈥0.5鈥 if maybe, 鈥0.0鈥 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, 鈥1.0鈥 if yes, 鈥0.5鈥 if maybe, 鈥0.0鈥 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鈥檚 伪=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鈥12), and providing crisis management resources to the annotators.

Who are the annotators?

The annotators are Amazon Mechanical Turk workers aged 36卤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.

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