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"์žํ•œ๋‹นํ‹€๋”ฑ๋“ค.. ์•…ํ”Œ์งˆ ๊ณ ๋งŒํ•ด๋ผ."
[ 2, 4 ]
์ •์น˜์ ์œผ๋กœ ํŽธํ–ฅ๋œ ํ‰๋ก ํ•œ์€ ๋ถ„์€ ๋ณ„๋กœ...
[ 8 ]
์ ๋‹นํžˆ์ข€ ์ณ๋จน์ง€.๊ทธ๋žฌ๋ƒ??? ์•ˆ๊ทธ๋ž˜๋„ ๋ฌธ์žฌ์ธ ๋•Œ๋ฌธ์— ๋‚˜๋ผ ์—‰๋ง์ง„์ฐฝ์ธ๋ฐ...
[ 2 ]
"์•ˆ์„œ๋Š” ์•„์žฌ๋“ค ํ’€๋ฐœ๊ธฐ ใ…‹ใ„ฒใ…‹"
[ 4 ]
์šฐ์™€ ใ…‹ ๋Šฅ๋ ฅ์ž
[ 8 ]
๋ง›๋…€์„ ์ฝฉํŠธ๋ณด๋‹ค ์•ฝํ–ˆ์Œ๋ง›๋…€์„ ์• ์ฒญ์ž๋กœ์จ 70%์‹ค๋ ฅ๋ฐœํœ˜
[ 8 ]
์ฃผ์˜ํ›ˆ ์†”์งํžˆ ํ˜ธ๊ฐ์ž„ ์ž‰๊ผฌ๋ถ€๋ถ€๋กœ ์†Œ๋ฌธ๋‚ฌ์ž–์•„
[ 8 ]
์ด๊ฒŒ์ฃผ๊ฐ„์•„์ด๋Œ์ด๋ž‘๋จธ๊ฐ€๋‹ฌ๋ผ...
[ 8 ]
์•„์˜ค ์Šˆ๋ฐ• ํšŒ์‚ฌ์ƒํ™œ๋„ ์ก‘๊นฅ๊ณ  ๋ˆ๋ฒŒ๊ธฐ ํž˜๋“ค์–ด ์ฃฝ๊ฒ ๊ตฌ๋งŒ ๋ญ” ์ €๋”ด๊ฒƒ๋“ค ์ž๊พธ tv๋‚˜์™€์„œ ์‚ฌ๋žŒ ์งœ์ฆ๋‚˜๊ฒŒํ•˜๋ƒ ์™ธ๊ตญ์„œ ํŽธํžˆ์‚ด๋ ค๋ฉด ์•„๋‹ฅํ•˜๊ณ  ์‚ด์•„๋ผ ๋Œ€ํ•œ๋ฏผ๊ตญ์„œ ์ทจ๋ฏธ๋กœ ๋ˆ๋ฒŒ์–ด๊ฐ€์ง€๋ง๊ณ  ์ข€ ๋„์ง€๋ผ๊ณ !
[ 3 ]
"๋ฌธ์žฌ์ธ ํ•˜๋Š”๊ฒŒ ๋ญ ๋ณ„๊ฑฐ์žˆ๋ƒ?ใ…‚ใ……ใ……ใ…‹๊ฐ€ ํ•˜๋Š”์ง“์ธ๋ฐ ์–ด๋ จํ•˜๊ฒ ์–ด.ใ…‹ใ…‹ใ…‹"
[ 2, 3 ]
๊ณตํ†ต์ ์€ ํ•œ๊ตญ๋‚จ์ž?
[ 8 ]
์ด์žฌ์ง„์€ ๊ทธ๊ฒŒ๋ฌธ์ œ๊ฐ€์•„๋‹ˆ์ž๋‚˜ ์ง€๊ธˆ ใ…‹ใ…‹ใ…‹ใ…‹
[ 8 ]
"์•ผ์ด OOO ์ƒ‰ํ–. ์ฃผ์ฒด์‚ฌ์ƒ ํ™ฉ์žฅ์—ฝ ๊ตญ๋ฆฝ๋ฌ˜์ง€์— ์‹ญ์–ด๋„ฃ์€ ์ด๋ช…๋ฐ•์€ ์–ด์ฉ”๋ž˜ ๋“œ์œผ์‘OOO์•„."
[ 3 ]
์†Œ์ง€์„ญ ๊ฑธ์–ด๋‹ค๋‹ˆ๋Š” ์ธ๊ฐ„ ์ˆ˜๋ฉด์ œ๋‚˜ํ”ผ๋””๊ฐ€ ํŽธ์ง‘์„ ์ž˜ํ•˜๊ฒ ์ง€๋งŒ๊ฑฑ์ •๋œ๋‹ค.ใ… .ใ… 
[ 8 ]
์›์ค‘์”จ........... ์ž˜๊ฐ€์š”........ ์ž˜์‚ด์•„์š”..........
[ 8 ]
50์–ด ์• ๋‚˜์„œ ์–ธ์ œ ๊ธธ๋Ÿฌ์š”?
[ 8 ]
์ •์‹ ๋‚˜๊ฐ„๋…„๋“ค ๋‚˜๋ผ ๋Œ์•„๊ฐ€๋Š”๊ฑด 1๋„ ๋ชจ๋ฅด์ง€
[ 3 ]
"์ค‘๊ตญ์€ ๋ฌด์Šจ ์•”๋ฉ์–ด๋ฆฌ ๊ฐ™์Œ ์–ด๋–ป๊ฒŒ ์ €๋ ‡๊ฒŒ ์„ธ๊ณ„๊ฐ์ง€์— ๊ณจ๊ณ ๋ฃจ ๋ฏผํ๋งŒ ๋ผ์น˜๋ƒ"
[ 0 ]
์•Œ๋ฐ”ํ’€์—ˆ๋‚˜ ๋งŽ์ด๋ณธ๋‰ด์Šค ์ดํ”„๋กœ๊ทธ๋žจ๊ธฐ์‚ฌ ๋Œ“๊ธ€ํ•˜๊ณ  ๋ฐ˜์‘์ด 180๋„ ๋‹ค๋ฅด๋„ค ใ…‹ใ…‹ใ…‹ ์˜๋ฏธ์—†๋Š” ์ฐฌ์–‘์งˆ ์™œ์ผ€๋งŽ๋ƒ ์—ฌ๊ธด
[ 8 ]
ํƒ์ด ์•„๋ถ€์ง€ ์นด๋ฆฌ์Šค๋งˆ ๋ฟœ๋ฟœ
[ 8 ]
๊ธฐ์‚ฌ์— ๋‚š์ด๋Š” ใ…‚ใ……๊ฐ™์€ ๋Œ“๊ธ€๋Ÿฌ๋“ค
[ 3 ]
๊น€ํƒœ๋ฆฌ.. ๋‚˜ 3๋Œ€์งธ ๋ผ์ง€๋†์žฅ์ฃผ ๊ณ ์„ํ™˜์˜ ์‹ ๋ถ€ํ›„๋ณด 16์œ„.. ํ•˜์ง€๋งŒ ์˜ค๋Š˜ 9์œ„๋กœ ์˜ฌ๋ผ๊ฐ”๋‹ค. ์ข€๋” ๋ถ„๋ฐœํ•ด๋ผ
[ 8 ]
์ง€๊ฐ€ ๊ดœ์ฐฎ๋‹ค๋Š”๋ฐ ๋‹ˆ๋“ค์€ ์ข€ ๋‹ฅโ‚ฉ/โ‚ฉ์น˜๊ณ ์žˆ์–ด๋ผ ๋Œ€๋ฆฌ๋„ ๋ชป๋‹จ ์ƒˆ!.!3!;๋ผ๋“ค์ด ๋ง์€ ์กด/&๋‚˜๊ฒŒ ๋งŽ๋„ค ใ…‹ใ…‹
[ 3 ]
๋Œ€์ฒด ๋ชจ์ž๋ž€๊ฒŒ ๋ญ์ง€ ์™„๋ฒฝํ•˜๋„ค
[ 8 ]
๋‹ค๋“ค ์˜ค์ง€๋ž– ์ข€...๋˜๊ฒŒ ํ•  ์ง“ ์—†์–ด๋ณด์—ฌ์š”
[ 8 ]
"์ฐŒ์งˆํ•œ ์ƒˆ๋ผ๋“ค.... ์™œ? ๋™์–‘ ๋‚จ์ž๋Š” ๋ฌด์„ญ๋ƒ?"
[ 3 ]
๊ฐœ๋‚˜์†Œ๋‚˜ ํŠธ๋กœํŠธ ๊ฐ€์ˆ˜๋ž˜ ๋…ธ๋ž˜ ์กด๋‚˜ ๋ชป๋ถ€๋ฅด๋„ค
[ 3 ]
์ ˆ์„ธ๋ฏธ๋…€๋ผ ์ข‹์•„ํ–ˆ์—ˆ๋Š”๋ฐ ํ†ต์ˆ˜์นœ๊ฑฐ๋ณด๊ณ  ์ •๋š๋–จ. ์—ฌ์šฐ์ง“ ใ…ˆใ„ด์‹ซ์Œใ…‹ใ…‹ใ…‹ใ…‹ใ…‹ ๋ฌดํŠผ ํ–‰์‡ผ~
[ 3 ]
์ง€์ธ์ด๋ž€๋„˜?์ง€์ธ ๋งž์–ด ใ…‹ใ…‹
[ 8 ]
๋ชฌ์ƒ๊ธด ์—ฌ์ž์ธ๊ฐ„๋“ค์€ ์ฐŒ๊ทธ๋Ÿฌ์ ธ์žˆ์–ด๋ผ ํ™•๊ทธ๋ƒฅ ๋ง‰๊ทธ๋ƒฅ
[ 1, 5 ]
์†ก์ค‘๊ธฐ๋Š” ์‚ฌ์ง„์—๋„ ์—†๊ตฌ๋งŒ..ใ…‹ใ…‹์†กํ˜œ๊ตโ™ก๊ณ ์–‘์ด.ใ…‹ใ…‹ใ…‹
[ 8 ]
๋„๋Œ€์ฒด ใ…‹ใ…‹ใ…‹ใ…‹์–ด๋””์„œ ๋ฐฉ์†ก์‚ฌ ๊ฐ‘์งˆ์ด ํŠ€์–ด๋‚˜์˜จ๊ฑฐ?? ์ง€๊ธˆ๊นŒ์ง€ ๋ฐํ˜€์ง„ ์‚ฌ์‹ค์€ ์ „๋ถ€ ๊ณ ํ˜„์ • ๊ฐ‘์งˆ์ด๋˜๋ฐ???ใ…‹ใ…‹ใ…‹ใ…‹ใ…‹ใ…‹์ดฌ์˜๊ฑฐ๋ถ€, ๊ฐ๋…์ง€์‹œ๊ฑฐ๋ถ€, ์ดฌ์˜์ดํƒˆ, ๋ฐ€์น˜๊ณ ์š•ํ•˜๋Š”ํญ๋ ฅํ–‰์‚ฌ ใ…‹ใ…‹ใ…‹ใ…‹ใ…‹ใ…‹ใ…‹ใ…‹ใ…‹ ์™œ๊ฐ‘์ž๊ธฐ ๊ณ ํ˜„์ •์„ ๋ถˆ์˜์— ๋งž์„œ์‹ธ๋ˆ„๋Š” ์ž”๋‹ค๋ฅดํฌํ–‰์ธ์ง€ ใ…‹ใ…‹ใ…‹ใ…‹ใ…‹์—ฌ์ž๋“ค ์ง„์งœ ๋ฌด๋…ผ๋ฆฌ
[ 5 ]
์œ ๋ถ€๋‚จ์ด ์ฒ˜๋…€์—๊ฒŒ ๋„ˆ๋ž‘ ๊ฒฐํ˜ผํ•˜๊ณ  ์‹ถ๋‹ค๋ผ๊ณ  ํ–ˆ์œผ๋ฉด ๊ฐœ์š•์ฒ˜๋จน์—ˆ๊ฒ ์ง€ ใ…‹ใ…‹ใ…‹ใ…‹ ๋ฏธ์นœํ—ฌ์กฐ์„ ์—์„œ ํƒœ์–ด๋‚˜ ์˜ค๋Š˜๋„ ๊ฐ์‚ฌํ•ฉ๋‹ˆ๋‹ค
[ 0 ]
์—ฐ๊ธฐ๋ชปํ•˜๋Š” ๋ฐฉ๊ตฌ์Ÿ์ด๋…„
[ 3 ]
"์งฑ๊นจ ์™ค์ผ€ ๊ผด๋ณด๊ธฐ ์‹ซ๋ƒ. ์ค‘๊ตญ๋‚ด์—์„œ ๋ฐ˜๋ž€ ์ผ์–ด๋‚˜์„œ ๋ฟ”๋ฟ”์ด ๋œฏ์–ด์กŒ์Œ ์ข‹๊ฒ ๋‹ค."
[ 0 ]
๊ทผ๋ฐ ๋”๋Ÿฌ์šด๊ฑด ์ง€๋””์ธ๋ฐ ์™œ ์—ฌ์ž์š•๋งŒํ•จ ใ…‹ใ…‹
[ 8 ]
"์ฟต์พ…์ด๋“ค ์ขŒํ‘œ์ฐ๊ณ  ํ™”๋ ฅ ์˜ค์ง€๋„ค ใ…‹ใ…‹"
[ 1, 5 ]
"ํ•˜๋‚˜๋‹˜์€ ๋ณ€ํ•จ์ด ์—†๋Š”๋ฐ ๊ฐœ๋ชฉ์‚ฌ๋“ค์ด ํ•˜๋‚˜๋‹˜์„ ํŒ”์•„์„œ ์ง€๋ฐฐ๋งŒ ๋ถˆ๋ฆฌ์ง€"
[ 0 ]
๊ฐ€์ข‡์˜ํ™”๋ฅผ ํ‚น์Šค๋งจ ๋”ฐ์œ„๋ฅผ ๋น„๋น„๋ƒ ใ…‹ใ…‹ใ…‹ใ…‹
[ 3 ]
"์งฑ๊นจ๋“ฃ์ด์—ฌ ์งฑ๊นจ๋Š” ๋Œ€ํ•œ๋ฏผ๊ตญ์— ์ „ํ˜€ ๋„์›€์ด ์•ˆ๋˜๋Š” ์ธ๊ฐ„๋“ค์ด์•ผ ์ถ”๋ฐฉ์ด ๋‹ต์ด๋‹ค."
[ 0 ]
"๋ชฉ์‚ฌ ์ข‹์•„ํ•˜๋„ค~~ ์ข…๊ตํŒ”์ด ํ•˜์ง€๋งˆ๋ผ ! ๊ฐœ๋ชฉ์‚ฌ ์ฃผ์ œ์—.."
[ 7 ]
"์ด์—ฌ์ธ ๊ด€์ƒ์ž์ฒด๊ฐ€.."
[ 8 ]
์ตœ์ข…๋ชธํ†ต์ด ๋ฌธ์ฃ„์ธ์ด๋ž‘ ๊ด€๋ จ์žˆ๋Š”๊ฑฐ ๊ฐ™๋‹ค์‚ด์•„์žˆ๋ฏ„ ๊ถŒ๋ ฅ์ด๋‹ˆ ์•ˆ์žก๊ณ  ์‰ฌ์‰ฌํ•˜๋Š”๊ฑฐ์ง€
[ 2 ]
์กฐ์šฉํ•„ ์ด์„ ํฌ์”จ ๊ฑฐ๊ธฐ ๊ฐ€์„œ ๋…ธ๋ž˜๋ถ€๋ฅด๋Š” ์ง“ ํ•˜์ง€ ๋งˆ์„ธ์š” ๊ทธ๋ƒฅ ๊ฑฐ์ ˆํ•˜์‹œ๊ธธ
[ 8 ]
์™€..์ง„์งœ ์˜ˆ์˜๋‹ค..
[ 8 ]
๊ฐ€์‹œ๋‚˜์„ธ์ƒ์ข‹์•„ํ–ˆ๋Š”๋ฐ. . ๋‚œ์„ ๋ฏธ๋„˜์ข‹์•„~.
[ 8 ]
"๊ตํ™œํ•œ ๊ฐœ๋…์˜ ์ „ํ˜•. ๋…์‹คํ•œ ๊ฐœ๋… ํ•˜๋‚˜ ๋” ์ฅ๋ฐ”๊ธฐ"
[ 2, 7 ]
๊ณ ์ƒ ํ•˜์…จ์Šต๋‹ˆ๋‹ค. ์ข€๋” ๋ง˜ํŽธํ•œ ์‚ถ์„ ํ–‰๋ณตํ•œ์‚ถ์„ ์‚ด์ˆ˜ ์žˆ๊ธธ ๋ฐ”๋ž๋‹ˆ๋‹ค.
[ 8 ]
๋‚จ์€ ์ธ์ƒ๋‹ฌ๋‹ฌํ•˜๊ฒŒ ํ–‰๋ณตํ•˜๊ฒŒ ์‚ฌ์„ธ์š”์ถ•ํ•˜ํ•ฉ๋‹ˆ๋‹ค
[ 8 ]
์ง€์šฐ๋ผ๋ฉด ์ง€์›Œ ๋ง ์กด๋‚˜๊ฒŒ๋งŽ๋„ค ์‹ธOOO๋“ค
[ 3 ]
ํ•œ์‹ฌํ•œ์ƒˆ๋ผ๋“คใ…‰
[ 3 ]
๋‚˜๋„ ๋ˆ๋งŽ์€ ์•„์คŒ๋งˆ๋ž‘ ๊ฒฐํ˜ผํ•˜๊ณ ์‹ถ๋‹ค
[ 8 ]
๋šฑ๋ณด๋ผ๋ฆฐ ๋ญ”๊ฐ€ ์ž˜๋งž๋Š”๊ฒŒ ์žˆ๋‚˜๋ณด๋„ค
[ 1 ]
๊ด‘๋Œ€๊ฐ€ ์ œ์ผ ์•„์‰ฝ ใ… ใ… ใ… ใ… 
[ 8 ]
"๊ด€์ƒ์€ ๋ฌด์‹œ ๋ชปํ•œ๋‹ค.. ๋ชป๋˜๊ฒŒ ์ƒ๊ธด๊ฑฐ ๋ด๋ผ.."
[ 1 ]
์˜›๋‚  ๋ณด๋ฆฌ๊ณ ๊ฐœ ์‹œ์ ˆ์—๋„ 7๋‚จ๋งค๋Š” ํ”ํ•  ์ •๋„๋กœ ๋‹ค์‚ฐํ–ˆ์—ˆ๊ณ  ๊ฐ€๋‚œํ–ˆ์–ด๋„ ์• ๋“ค ๋‹ค ์ž˜ํ‚ค์› ์—ˆ๋‹ค, ๊ทธ๋ฆฌ๊ณ  ๊ทธ ์‹œ์ ˆ์—” ์‚ฐ๋ชจ๊ฐ€ ๋ฐญ์—์„œ ์ผํ•˜๋‹ค๊ฐ€ ์• ๋‚ณ๋Š” ๊ฒฝ์šฐ๋„ ํ”ํ–ˆ๊ณ  ์‚ฐํ›„์กฐ๋ฆฌ๋Š” ๊ฟˆ๋„ ๋ชป๊พธ๊ณ  ์• ๋‚ณ๊ณ  ์ดํ‹€์ •๋„๋งŒ ์‰ฌ๊ณ  ๋‹ค์‹œ ์ผํ•˜๋Ÿฌ ๊ฐ”์—ˆ๋‹ค. ์ด๋Ÿฐ ํ—๊ทธ๋ฆฌ ์ •์‹ ์ด ์˜ค๋Š˜๋‚ ์˜ ๋Œ€ํ•œ๋ฏผ๊ตญ์„ ๋งŒ๋“ค์—ˆ๋Š”๋ฐ ์š”์ฆ˜ ์ Š์€ ๊ฒƒ๋“ค์€ ๊ทธ ๋•Œ์— ๋น„ํ•˜๋ฉด ์ฒœ๊ตญ๊ฐ™์ด ์‚ด๊ธฐ ์ข‹์€ ์„ธ์ƒ์ธ๋ฐ๋„ ํ—ฌ์กฐ์„  ์šด์šดํ•˜๋ฉฐ ์• ๋“ค์„ ์•ˆ ๋‚ณ๋Š”๋ฐ ํ•œ ๋งˆ๋””๋กœ ๋ฐฐ์ง€๋•Œ๊ธฐ๊ฐ€ ์ฒ˜ ๋ถˆ๋Ÿฌ์„œ ์ •์‹ ์„ ๋ชป์ฐจ๋ฆฌ๋Š”๊ฑฐ๋‹ค
[ 4 ]
"๋ณด๋ฆ„์ด ์›๋ž˜ ์›ƒ์Œ์ƒ์ด๋ผ ์˜คํ•ด๋ฐ›๋Š”๊ฑด๋ฐ 40๋Œ€ ์ƒˆ๋ผ๋“ค ์ง„์งœ ํ† ๋‚˜์˜จ๋‹ค"
[ 3, 4 ]
์™ธ๋ชจ๋งŒ๋ณด๋ฏ„ํ•œ๊ตญํ† ์ข…์‹œ๊ณจ์•„์คŒ๋งˆ์ฃ ์ข€๋ชป์ƒ๊ธด์คŒ๋งˆ. ๋…ธ๋žœ์ž˜ํ•˜์ฃ 
[ 1, 5 ]
๋…ธ๋ž˜ ๋ช‡๋ฒˆ ๋งํ•˜๋”๋‹ˆ ์ด์ œ ํ—๊ทธ๋ฆฌ ์ •์‹ ์ด ์ƒˆ๋กœ ์ƒ๊ธด๋“ฏ....์•„์•„ ์„ธ์›”์ด ์•ผ์†ํ•˜๋„ค. ์˜›๋‚ ์— ์ „์„ฑ๊ธฐ ์‹œ์ ˆ์—” ์ง€๊ธˆ ์ˆ˜์ง€ ์•„์ด์œ  ํ•ฉ์ณ๋†“์€ ๊ธ‰์ด์—ˆ๋Š”๋ฐ.
[ 8 ]
์—ฌ๊ธฐ ์•„์คŒ๋งˆ๋“ค ์‹œ์ƒ˜์ด ์žฅ๋‚œ์•„๋‹ˆ๋„ค์š” ์—ดํญํ• ์‹œ๊ฐ„์— ์• ๊ธฐ๋‚˜๋ณด์„ธ์š”^^
[ 5 ]
์–ด๋ฆฐ ์—ฌ์ž ๋งŒ๋‚˜์•ผ ํ•˜๋Š”๊ฑด๊ฐ€..
[ 8 ]
์™€!! ๋ชป์ƒ๊ฒจ๋”ฐ
[ 1 ]
๋ฌธ์žฌ์•™ ๋ฎ์œผ๋ ค๊ณ  ์—ฐ์˜ˆ์ธ๋“ค์ด ๊ณ ์ƒํ•˜๋„ค
[ 2 ]
๋‚จ์ž๋„ ์„ฑํ˜•์ด ๋งŽ๋‹ค๋‹ˆ..์„ฑํ˜•๊ณตํ™”๊ตญ๋‹ต๋‹ค
[ 8 ]
๋ˆ„๋‚˜ ํ•จ๋ถ€๋กœ ์“ฐ์ง€๋งˆ๋ผ. ๊ณง ํ™˜๊ฐ‘์ธ๋ฐ. ์•„์คŒ๋งˆ๋‚˜ ํ• ๋จธ๋‹ˆ๋‹ค.
[ 8 ]
๋ง๋”ธ์ดํ•ด๊ฐ€์•ˆ๊ฐ€๊ณ  ๊ฐ€์žฅ์ด๊ธฐ์ ์ธ๋‡ฌ ์•„๋ฒ„์ง€๊ฐ€ํ™ฉํ˜ผ์—์„œ ์ฒซ์‚ฌ๋ž‘๋งŒ๋‚˜ ํ–‰๋ณตํ•˜๊ณ ์‹ถ๋‹ค๋Š”๋ฐ ํ›ผ๋ฐฉ๋†“๋‹ค๋‹ˆ ๋ฌด์ž์‹์ด์ƒํŒ”์ž๋‹ค
[ 3 ]
ํ•ฉ์ฐฝ ๋ฌผํƒ€๊ธฐ ๊ผผ์ˆ˜๊ฐ€ ๋ฐฉ์†ก์„ ํ•ต๋…ธ์žผ ๋งŒ๋“ฆ
[ 8 ]
์˜์žํŽธ๋งŒ๋ณด๊ณ  ์ž˜๋ ค๊ณ  ํ–ˆ๋”๋‹ˆ ๋’ค๋กœ ๋ฐ€์–ด๋†จ๋„ค ์•„๋†”~์œ ๋ณ‘์žฌ๊บผ ๋ณด๊ธฐ์‹ซ์—ˆ๋Š”๋ฐ.
[ 8 ]
"์–ด์„œ๋“ค ํ”ผ๊ฒ€์‚ฌ ๋ฐ›์œผ์‹œ๊ธธ~~"
[ 8 ]
์•„์šฐ~~ ์ง„์งœ ๋ณผ๊นจ๋ฌผ์–ด์ฃผ๊ฒŒ ๊ท€์—ฝ๋„น~~ ์‚ผ๋‘ฅ์ด ์•„ํ”„์ง€๋ง๊ณ  ๊ฑด๊ฐ•ํ•˜๊ฒŒ ์ž๋ผ๊ธธ ๋ฐ”๋ž„๊ฒŒ~~
[ 8 ]
๋ˆ„๊ตฌ์‹ ์ง€. . .์กด์žฌ๊ฐ ์—†๋Š”
[ 8 ]
์—ฌ์ž๊ฐ€ ๋‚จ์ž๊ถ๋Ž…์ด๋งŒ์ง€๋ฉด ๊ฒฐ๋ก ์€ ๊ทธ๋Ÿด์ˆ˜๋„์žˆ์ง€ ๋‚จ์ž๊ฐ€ ์ชผ์ž”ํ•˜๊ฒŒ ๋”ฐ์ง€๋ƒ ์—ฌ์ž๊ฐ€ ๋งŒ์ ธ์ฃผ๋ฉด ์ข‹์ง€๋ญ ์–ด๋•Œ?? ๋ฐ˜๋ฉด ์—ฌ์ž๊ถ๋Ž…์ด ๋งŒ์ง€๋ฉด 30๋…„์ „์ผ์ด๋ผ๋„ ์ง€๊ธˆ ๊ธฐ์–ต๋‚˜๋ฉด ์•„ ์š”์ฆ˜ ์งœ์ฆ๋‚˜๋Š”๋ฐ๋ˆ„๊ตฌ ๋ฏธํˆฌํ• ๋‚จ์ž์—†๋‚˜ ์ฐพ์•„์„œ ์•„์‹ธ ๋ฏธํˆฌ ๋‹ˆ์ธ์ƒ ์กฐ์ ธ ^-----^
[ 5 ]
์™€ ์ด๊ฑฐ ์™„์ „ ์žฌ์•™์ด๋„ค. ๊นจ๋—ํ•œ ๋‚˜๋ผ ๋งŒ๋“ ๋‹ค๋ฉด์„œ ์™œ ์ด๋Ÿฐ ์‚ฌ๊ฑด์ด ํ„ฐ์ง€๋ƒ??
[ 2 ]
์ข€๋” ์ฐ์–ด๋ด์š”
[ 8 ]
์•”๋งŒ ์—ฐ์˜ˆ์ธ์ด๋ผ๋„ ๋‚จ์ž”๋ฐ ๋ฌด์Šจ ํ™”์žฅ์„ ์ €๋ฆฌ๋„ ๋–ก์น ์„ ํ–ˆ๋ƒ? ๋ฌด์Šจ ๊ฒŒ์ด์ƒค๋ƒ?
[ 5 ]
์–ด์ด๊ตฌ ๋Œ๋ฌธ์žฌ์•™
[ 2 ]
์˜ค๋Š˜ ์ฒซ ๋ผ์—ฌ~
[ 8 ]
๋‹ค๋“ค ์กด๋‚˜ ๊ผฌ์˜€๊ตฌ๋‚˜
[ 3 ]
"์ค‘๊ตญ์ค‘๊ตญ์ค‘๊ตญ์ค‘๊ตญํ•˜๊ณ  ์ „์Ÿ์ด๋‚˜ ์‹œ์›ํ•˜๊ฒŒ ํ•˜์ž"
[ 8 ]
์šด๋™์ด ์•„๋‹ˆ๋ผ ํ˜น์‚ฌ์ง€....๊ฑ ๋ฐฅ์ž˜๋จน๊ณ  ์ž˜์ž๊ณ  ์กฐ๊น…๋งŒํ•ด๋„ ๋œ๋‹ค.
[ 8 ]
ํ† ํฌ๋ชฌ ๊ฐ•ํ˜ธ๋™ ๊น€ํฌ์„ ๋•œ์‹œ ๋ณธ๋‹ค^^
[ 8 ]
์ •๋ง๋ฐ”๋ฅด๊ณ  ๋ด๋„๊ด˜์•ˆ์€๊ฒƒ๊ฐ™์€๋ฐ ๋‘๊ณ ๋ด์•ผ์ง€ ์ง€๊ธˆ์€ ๋ฐ˜๋“ฏํ•œ๊ฑฐ๊ฐ™์€๋ฐ ๊ทธ๋ž˜์„œ์ด๋ฝ€
[ 8 ]
"์—‰๋ฉ์ด๋ฅผ ํ”๋“ค์–ด๋ด~"
[ 8 ]
์Šน๋ฆฌ๊ณ ํ–ฅ ์ „๋ผ๋„๊ด‘์ฃผใ…‹ใ…‹ใ…‹ใ…‹๋” ์ด์ƒ ๋ฌด์Šจ๋ง์ดํ•„์š”?ใ…‹ใ…‡ใ…‹
[ 8 ]
"์ƒ๊ธด๊ฒƒ๋„ ๊ฐœ๋–ก๊ฐ™์€๊ฒŒ ๊ฐœ๋–ก์น˜๋Š” ์†Œ๋ฆฌํ•˜๋„ค. 18๋‡ฌ!"
[ 1, 3 ]
"๋‹ˆ๋„ค ๊ผด๋ณด๊ธฐ ์‹œ๋Ÿฌ ๋‚˜๋„ ๋™์ฐธํ–ˆ๋‹ค ์ •๋ง ๋Š๊ทธ ์กฐ๊ตญ ์ชฝ๋ฐ”๋ฆฌํ•œํ…Œ ๊ฐ€๋ผ"
[ 0 ]
"๋ฌด์‹ํ•œ ํ• ๋ฐฐ๋“ค์˜ ์•ˆ๋ณดํŒ”์ด ์‹์ƒํƒ€. ์ด์   ์ Š์€์ด๋“ค์ด ์‚ด ์„ธ์ƒ์ด๋‹ค"
[ 4 ]
์• ์ดˆ์— ์นด์ง€๋…ธ๋ฅผ ์™œ ์ง“๋ƒ
[ 8 ]
"์ฃผ์‚ฌํŒŒ, ์‚ฌ๋…ธ๋งน, ๋Œ€๊นจ๋ฌธ, ๋‹ฌX, ํ˜ธ๋‚จ์ธ๋“ค์ด ๋ฐœ๊ด‘ํ•˜๋„ค"
[ 0, 2 ]
"์„ฌ๋‚˜๋ผ๋กœ ๊ฐ€๋ผ..."
[ 8 ]
"์ด๋‡ธ์ด ๋˜ฅ์˜ค์ฆ˜ ๋ชป๊ฐ€๋ฆฌ๋„ค"
[ 3 ]
ํ™”์žฅ์•ˆํ•œ์–ผ๊ตด์ดํ›จ์”ฌ์ด์œ๊ฒƒ๊ฐ™์Œ.์‹ค์ œ๋กœ๋ณด๋‹ˆ ๋ชธ๋งค๋„ ๋‚ ์”ฌํ•œ๊ฒƒ๊ฐ™๋“œ๋งŒ.์นด๋ฉ”๋ผ์ง€๋Œ€์•ˆ๋ฐ›๋Š”ํƒ€์ž…์ธ๋“ฏใ…Žใ…Ž๊ธฐ์ž๋“ค์ด์•ˆํ‹ฐ ใ…Ž์š”์ฆ˜ ๋ˆˆ์—๋›ฐ๋Š”์—ฌ์ž์•„๋‚˜์šด์„œ๋ณ„๋กœ์—†๋˜๋ฐ ์ซŒ๊ธฐ๋Œ€๋จ
[ 8 ]
"์ˆ˜๊ผดํ‹€๋”ฑ์‹œํ‚ค๋“ค์ด ๋‹ค ๋””์ ธ์•ผ ๋‚˜๋ผ๊ฐ€ ๋˜‘๋ฐ”๋กœ ๋ ๊ฒƒ๊ฐ™๋‹ค..๋‹ต์ด ์—†๋Š” ์ข…์ž๋“คใ… "
[ 2, 3, 4 ]
"์ด๋Ÿฐ๊ธฐ์‚ฌ๋Š” ํ•ญ์ƒ ๋Œ“๊ธ€ํ†ต์ œ ์”น์žฌ์•™ ใ…‹ใ…‹ใ…‹ ์ข…๋ถ์ƒˆ๋ผ"
[ 2, 3 ]
๊ธฐ์ง‘๋…„๋“ค์•„ ์†”์งํžˆ ์–˜๊ธฐํ•˜์ž. ๋‹ˆ๋“ค์ด ๋ฐ›๊ณ  ์‹ถ์€๊ฒŒ ์‚ฌ๊ณผ๋ƒ? ์•„๋‹˜ ๋ˆ์ด๋ƒ? ์™œ ๋ˆ๋‹ฌ๋ผ๋Š” ์–˜๊ธฐ๋Š” ๋–ณ๋–ณํ•˜์ง€ ๋ชปํ•œ๊ฑฐ ๊ฐ™์•„์„œ ๋ชปํ•˜๋Š”๊ฑฐ์•ผ? ๊ทธ๋ ‡๊ฒŒ ์‚ฌ๊ณผ๋ฅผํ•ด๋„ ๊ธฐ์ž์ฐพ์•„๋‹ค๋‹ˆ๋ฉฐ ๊ธฐ์‚ฌ์จ๋‹ฌ๋ผ๊ณ  ํ•˜๋Š”๊ฑด ๊ฒฐ๊ตญ์€ ๋‹ˆ๋“ค ๋ˆ๋จน๊ณ  ์‹ถ์–ด์š”~ ํ•˜๋Š” ๊ผฌ๋ผ์ง€์•„๋‹ˆ๋ƒ~๊ฐ€์‹ ์ณ๋– ๋Š” ๋…„๋“ค ๊ทนํ˜โœ”
[ 3, 5 ]
์ด๋…„ ์ด๊ฑฐ ์ •์‹  ๋ชป์ฐจ๋ ธ๋„ค ์ด๋Ÿฐ๋…„๋“ค์€ ์—ฐ์˜ˆ๊ณ„์—์„œ ํ‡ด์ถœ์•ˆ์‹œํ‚ค๋‚˜?
[ 3, 5 ]
๊ฒฐ๊ตญ์—” ์•  ๋ฏธ ์ฐฝ ๋…„์ด๋ž€๊ฑฐ์ž–์•„
[ 3 ]
"๊ทผ๋ฐ ์งฑ๊นจ๊ตญ์—์„œ ์›๋ž˜ ๋งˆ์•ฝํ•˜๋ฉด ์‚ฌํ˜• ์•„๋‹ˆ์˜€์Œ?? ์‹œ์ง„ํ•‘ ์ง‘๊ถŒํ•˜๊ณ  ๋ฐ”๋€Œ์—ˆ๋‚˜?"
[ 0 ]
"๊ทธ๋Ÿผ ์งฑ๊ฐœ๋ฅผ ์‹œ์ผœ์ฒ˜๋จน๋“ ๊ฐ€ ๊ทธ์ง€์ƒˆใ…กํ‚ค๊ฐ€์ง•์ง•๋Œ€๊ธฐ๋Š”"
[ 0, 3 ]
์†ก์ง€ํšจ ๋‚จ์นœ์žˆ์ง€์•œใ…Ž์Œ? ๊ทธ ๋ง์ฃฝ๊ฑฐ๋ฆฌ ๋‚จ์ž ๋งค๋‹ˆ์ €์—ฟ๋˜์• 
[ 8 ]

Dataset Card for K-MHaS

Sample Code

base

Dataset Summary

The Korean Multi-label Hate Speech Dataset, K-MHaS, consists of 109,692 utterances from Korean online news comments, labelled with 8 fine-grained hate speech classes (labels: Politics, Origin, Physical, Age, Gender, Religion, Race, Profanity) or Not Hate Speech class. Each utterance provides from a single to four labels that can handles Korean language patterns effectively. For more details, please refer to our paper about K-MHaS, published at COLING 2022.

Supported Tasks and Leaderboards

Hate Speech Detection

  • binary classification (labels: Hate Speech, Not Hate Speech)
  • multi-label classification: (labels: Politics, Origin, Physical, Age, Gender, Religion, Race, Profanity, Not Hate Speech)

For the multi-label classification, a Hate Speech class from the binary classification, is broken down into eight classes, associated with the hate speech category. In order to reflect the social and historical context, we select the eight hate speech classes. For example, the Politics class is chosen, due to a significant influence on the style of Korean hate speech.

Languages

Korean

Dataset Structure

Data Instances

The dataset is provided with train/validation/test set in the txt format. Each instance is a news comment with a corresponding one or more hate speech classes (labels: Politics, Origin, Physical, Age, Gender, Religion, Race, Profanity) or Not Hate Speech class. The label numbers matching in both English and Korean is in the data fields section.

{'text':'์ˆ˜๊ผดํ‹€๋”ฑ์‹œํ‚ค๋“ค์ด ๋‹ค ๋””์ ธ์•ผ ๋‚˜๋ผ๊ฐ€ ๋˜‘๋ฐ”๋กœ ๋ ๊ฒƒ๊ฐ™๋‹ค..๋‹ต์ด ์—†๋Š” ์ข…์ž๋“คใ… '
 'label': [2, 3, 4]
}

Data Fields

  • text: utterance from Korean online news comment.
  • label: the label numbers matching with 8 fine-grained hate speech classes and not hate speech class are follows.
    • 0: Origin(์ถœ์‹ ์ฐจ๋ณ„) hate speech based on place of origin or identity;
    • 1: Physical(์™ธ๋ชจ์ฐจ๋ณ„) hate speech based on physical appearance (e.g. body, face) or disability;
    • 2: Politics(์ •์น˜์„ฑํ–ฅ์ฐจ๋ณ„) hate speech based on political stance;
    • 3: Profanity(ํ˜์˜ค์š•์„ค) hate speech in the form of swearing, cursing, cussing, obscene words, or expletives; or an unspecified hate speech category;
    • 4: Age(์—ฐ๋ น์ฐจ๋ณ„) hate speech based on age;
    • 5: Gender(์„ฑ์ฐจ๋ณ„) hate speech based on gender or sexual orientation (e.g. woman, homosexual);
    • 6: Race(์ธ์ข…์ฐจ๋ณ„) hate speech based on ethnicity;
    • 7: Religion(์ข…๊ต์ฐจ๋ณ„) hate speech based on religion;
    • 8: Not Hate Speech(ํ•ด๋‹น์‚ฌํ•ญ์—†์Œ).

Data Splits

In our repository, we provide splitted datasets that have 78,977(train) / 8,776 (validation) / 21,939 (test) samples, preserving the class proportion.

Dataset Creation

Curation Rationale

We propose K-MHaS, a large size Korean multi-label hate speech detection dataset that represents Korean language patterns effectively. Most datasets in hate speech research are annotated using a single label classification of particular aspects, even though the subjectivity of hate speech cannot be explained with a mutually exclusive annotation scheme. We propose a multi-label hate speech annotation scheme that allows overlapping labels associated with the subjectivity and the intersectionality of hate speech.

Source Data

Initial Data Collection and Normalization

Our dataset is based on the Korean online news comments available on Kaggle and Github. The unlabeled raw data was collected between January 2018 and June 2020. Please see the details in our paper K-MHaS published at COLING2020.

Who are the source language producers?

The language producers are users who left the comments on the Korean online news platform between 2018 and 2020.

Annotations

Annotation process

We begin with the common categories of hate speech found in literature and match the keywords for each category. After the preliminary round, we investigate the results to merge or remove labels in order to provide the most representative subtype labels of hate speech contextual to the cultural background. Our annotation instructions explain a twolayered annotation to (a) distinguish hate and not hate speech, and (b) the categories of hate speech. Annotators are requested to consider given keywords or alternatives of each category within social, cultural, and historical circumstances. For more details, please refer to the paper K-MHaS.

Who are the annotators?

Five native speakers were recruited for manual annotation in both the preliminary and main rounds.

Personal and Sensitive Information

This datasets contains examples of hateful language, however, has no personal information.

Considerations for Using the Data

Social Impact of Dataset

We propose K-MHaS, a new large-sized dataset for Korean hate speech detection with a multi-label annotation scheme. We provided extensive baseline experiment results, presenting the usability of a dataset to detect Korean language patterns in hate speech.

Discussion of Biases

All annotators were recruited from a crowdsourcing platform. They were informed about hate speech before handling the data. Our instructions allowed them to feel free to leave if they were uncomfortable with the content. With respect to the potential risks, we note that the subjectivity of human annotation would impact on the quality of the dataset.

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

This dataset is curated by Taejun Lim, Heejun Lee and Bogeun Jo.

Licensing Information

Creative Commons Attribution-ShareAlike 4.0 International (cc-by-sa-4.0).

Citation Information

@inproceedings{lee-etal-2022-k,
    title = "K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment",
    author = "Lee, Jean  and
      Lim, Taejun  and
      Lee, Heejun  and
      Jo, Bogeun  and
      Kim, Yangsok  and
      Yoon, Heegeun  and
      Han, Soyeon Caren",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.311",
    pages = "3530--3538",
    abstract = "Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effectively handles Korean language patterns. The dataset consists of 109k utterances from news comments and provides a multi-label classification using 1 to 4 labels, and handles subjectivity and intersectionality. We evaluate strong baselines on K-MHaS. KR-BERT with a sub-character tokenizer outperforms others, recognizing decomposed characters in each hate speech class.",
}

Contributions

The contributors of the work are:

  • Jean Lee (The University of Sydney)
  • Taejun Lim (The University of Sydney)
  • Heejun Lee (BigWave AI)
  • Bogeun Jo (BigWave AI)
  • Yangsok Kim (Keimyung University)
  • Heegeun Yoon (National Information Society Agency)
  • Soyeon Caren Han (The University of Western Australia and The University of Sydney)
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