51 research outputs found

    Analytical Tools and Databases for Metagenomics in the Next-Generation Sequencing Era

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    Metagenomics has become one of the indispensable tools in microbial ecology for the last few decades, and a new revolution in metagenomic studies is now about to begin, with the help of recent advances of sequencing techniques. The massive data production and substantial cost reduction in next-generation sequencing have led to the rapid growth of metagenomic research both quantitatively and qualitatively. It is evident that metagenomics will be a standard tool for studying the diversity and function of microbes in the near future, as fingerprinting methods did previously. As the speed of data accumulation is accelerating, bioinformatic tools and associated databases for handling those datasets have become more urgent and necessary. To facilitate the bioinformatics analysis of metagenomic data, we review some recent tools and databases that are used widely in this field and give insights into the current challenges and future of metagenomics from a bioinformatics perspective.

    ๊ฐ•ํ™” ๊ฐฏ๋ฒŒ๊ณผ ๋‚จ๊ทน๋ฐ˜๋„ ๋ถ์„œ์ง€์—ญ์—์„œ ๋ถ„๋ฆฌ๋œ ํ˜ธ๊ธฐ์„ฑ ์„ธ๊ท ์˜ ๋ถ„๋ฅ˜

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ƒ๋ช…๊ณผํ•™๋ถ€,2007.Docto

    Comparison of Health Behaviors, Disease prevalence between Middle aged One-person households and Multiple households in Korea

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋ณด๊ฑด๋Œ€ํ•™์› ๋ณด๊ฑดํ•™๊ณผ(๋ณด๊ฑดํ•™์ „๊ณต), 2018. 8. ์กฐ์˜ํƒœ.์—ฐ๊ตฌ๋ชฉ์ : ๋ณธ ์—ฐ๊ตฌ๋Š” ์งˆ๋ณ‘๊ด€๋ฆฌ๋ณธ๋ถ€์—์„œ ์ œ๊ณตํ•˜๋Š” 2008๋…„, 2012๋…„, 2016๋…„ ์ง€์—ญ์‚ฌํšŒ๊ฑด๊ฐ•์กฐ์‚ฌ(Korean Community Health Survey)๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ค‘๋…„ 1์ธ๊ฐ€๊ตฌ์™€ ๋‹ค์ธ๊ฐ€๊ตฌ ์ค‘๋…„์˜ ๊ฑด๊ฐ•ํ–‰ํƒœ ๋ฐ ์งˆ๋ณ‘ ์ดํ™˜์„ ๋น„๊ตํ•˜๋Š” ์—ฐ๊ตฌ์ด๋‹ค. ์—ฐ๊ตฌ๋ฐฉ๋ฒ•: ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” 2008๋…„, 2012๋…„, 2016๋…„ ์ง€์—ญ์‚ฌํšŒ๊ฑด๊ฐ•์กฐ์‚ฌ์ž๋ฃŒ์˜ 40~50๋Œ€ ์„ฑ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์ด์ฐจ์ž๋ฃŒ ๋ถ„์„์—ฐ๊ตฌ์ด๋‹ค. ์šฐ๋ฆฌ๋‚˜๋ผ 40~50๋Œ€ ์ค‘๋…„์„ ๋Œ€์ƒ์œผ๋กœ 1์ธ๊ฐ€๊ตฌ์™€ ๋‹ค์ธ๊ฐ€๊ตฌ์˜ ์ธ๊ตฌ์‚ฌํšŒํ•™์  ํŠน์„ฑ, ๊ฑด๊ฐ•ํ–‰ํƒœ ๋ฐ ์งˆ๋ณ‘ ์ดํ™˜ ๊ด€๋ จ ํŠน์„ฑ์„ ์—ฐ๋„๋ณ„๋กœ ์นด์ด์ œ๊ณฑ๊ฒ€์ •์„ ํ†ตํ•ด ๋น„๊ตํ•˜์˜€์œผ๋ฉฐ ์ค‘๋…„ 1์ธ๊ฐ€๊ตฌ์˜ ๊ฑด๊ฐ•ํ–‰ํƒœ์™€ ์งˆํ™˜์˜ ์˜ค์ฆˆ๋น„๋ฅผ ๊ตฌํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ ๋ณ€์ˆ˜๋ณ„๋กœ ๊ฐ€์ค‘์น˜๋ฅผ ๊ณ ๋ คํ•œ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ํŠนํžˆ ์ค‘๋…„ 1์ธ๊ฐ€๊ตฌ์™€ ๋‹ค์ธ๊ฐ€๊ตฌ ์ค‘๋…„์˜ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ƒํƒœ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์„ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ƒํƒœ๋ฅผ ์ข…์†๋ณ€์ˆ˜๋กœ ์„ค์ •ํ•˜์—ฌ ๊ฐ€์ค‘์น˜๋ฅผ ๊ณ ๋ คํ•œ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์— ์‚ฌ์šฉ๋œ ๋ชจ๋“  ๋ถ„์„์€ SAS ํ”„๋กœ๊ทธ๋žจ Ver 9.4๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ: ์šฐ๋ฆฌ๋‚˜๋ผ 40-50๋Œ€ ์ค‘๋…„ 1์ธ๊ฐ€๊ตฌ์˜ ๊ฑด๊ฐ•ํ–‰ํƒœ ๋ฐ ์งˆ๋ณ‘ ์ดํ™˜์„ ๋‹ค์ธ๊ฐ€๊ตฌ ์ค‘๋…„๊ณผ ๋น„๊ตํ•  ๊ฒฝ์šฐ ์˜ค์ฆˆ๋น„๋Š” ํก์—ฐ์˜ ๊ฒฝ์šฐ 2008๋…„ 1.44(1.19-1.74), 2012๋…„ 1.33(1.11-1.59), 2016๋…„ 1.40(1.22-1.59)๋กœ ๋ชจ๋“  ์—ฐ๋„์—์„œ ์œ ์˜ํ•˜๊ฒŒ ๋†’์•˜๋‹ค. ์Œ์ฃผ์˜ ๊ฒฝ์šฐ ์˜ค์ฆˆ๋น„๋Š” 2008๋…„ 1.43(1.27-1.62), 2012๋…„ 1.32(1.17-1.50), 2016๋…„ 1.41(1.24-1.60)์œผ๋กœ ๋ชจ๋“  ์—ฐ๋„์—์„œ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ๋†’์•˜๋‹ค. ํ•œํŽธ, ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ƒํƒœ์™€ ์ŠคํŠธ๋ ˆ์Šค์—์„œ๋Š” ๋‹ค์ธ๊ฐ€๊ตฌ ์ค‘๋…„์— ๋น„ํ•ด ์ค‘๋…„ 1์ธ๊ฐ€๊ตฌ์—์„œ ์˜ค์ฆˆ๋น„๊ฐ€ ๋‚ฎ์€ ๊ฒฝํ–ฅ์„ ๋ณด์˜€๋‹ค. ๊ฑด๊ฐ•ํ–‰ํƒœ ๋ฐ ์งˆ๋ณ‘ ์ดํ™˜์€ ์—ฐ๋„๋ณ„, ๋‚จ๋…€๋ณ„๋กœ ๋‹ค๋ฅธ ํŒจํ„ด์„ ๋ณด์˜€๋‹ค. ๊ฒฐ๋ก : ํก์—ฐ, ์Œ์ฃผ์™€ ๊ฐ™์€ ๊ฐ๊ด€์  ๊ฑด๊ฐ•ํ–‰์œ„๋Š” ์ค‘๋…„ 1์ธ๊ฐ€๊ตฌ๊ฐ€ ์ทจ์•ฝํ•˜์ง€๋งŒ, ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ƒํƒœ์™€ ์ŠคํŠธ๋ ˆ์Šค ์ธ์ง€๋Š” ๋‹ค์ธ๊ฐ€๊ตฌ ์ค‘๋…„์—์„œ ์ทจ์•ฝํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํŠนํžˆ ์ •์‹ ๊ฑด๊ฐ• ์˜์—ญ์—์„œ ์—ฌ์„ฑ ์ค‘๋…„ 1์ธ๊ฐ€๊ตฌ๋ณด๋‹ค ๋‚จ์„ฑ ์ค‘๋…„ 1์ธ๊ฐ€๊ตฌ์—์„œ ์ทจ์•ฝํ–ˆ๋‹ค. ์ตœ๊ทผ ์šฐ๋ฆฌ๋‚˜๋ผ์— ์ฆ๊ฐ€ํ•˜๋Š” ์ค‘๋…„ 1์ธ๊ฐ€๊ตฌ์˜ ๊ฑด๊ฐ•ํ–‰ํƒœ์™€ ์งˆ๋ณ‘์„ ์ง€์†์ ์œผ๋กœ ๋ชจ๋‹ˆํ„ฐํ•˜์—ฌ ๊ฑด๊ฐ•๊ด€๋ จ ํ”„๋กœ๊ทธ๋žจ ๋ฐ ์ •์ฑ…์— ํ™œ์šฉํ•  ํ•„์š”์„ฑ์ด ์žˆ๋‹ค.์š”์•ฝ 1 1. ์„œ๋ก  8 1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 8 1.2 ์—ฐ๊ตฌ๋ชฉ์  13 2. ๋ฌธํ—Œ๊ณ ์ฐฐ ๋ฐ ๊ฐ€์„ค 14 2.1 ๋ฌธํ—Œ๊ณ ์ฐฐ 14 2.1.1 ์ค‘๋…„ 1์ธ๊ฐ€๊ตฌ์˜ ์ •์˜ 14 2.1.2 ์ค‘๋…„ 1์ธ๊ฐ€๊ตฌ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  15 2.2 ์—ฐ๊ตฌ๊ฐ€์„ค 17 3. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 18 3.1 ์—ฐ๊ตฌ์„ค๊ณ„ 18 3.2 ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ์ž๋ฃŒ์ˆ˜์ง‘ 20 3.3 ๋ณ€์ˆ˜์˜ ์ •์˜ 22 3.3.1 ์ธ๊ตฌ์‚ฌํšŒํ•™์  ํŠน์„ฑ ๊ด€๋ จ ๋ณ€์ˆ˜ 22 3.3.2 ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ƒํƒœ ๋ฐ ์ •์‹ ๊ฑด๊ฐ• ํŠน์„ฑ ๊ด€๋ จ ๋ณ€์ˆ˜ 23 3.3.3 ๊ฑด๊ฐ•ํ–‰ํƒœ ํŠน์„ฑ ๊ด€๋ จ ๋ณ€์ˆ˜ 23 3.3.4 ์งˆ๋ณ‘ ์ดํ™˜ ํŠน์„ฑ ๊ด€๋ จ ๋ณ€์ˆ˜ 24 3.4 ๋ถ„์„ ๋ฐฉ๋ฒ• 25 4. ์—ฐ๊ตฌ๊ฒฐ๊ณผ 26 4.1 ๊ธฐ์ดˆ๋ถ„์„ 26 4.1.1 ์ธ๊ตฌ์‚ฌํšŒํ•™์  ํŠน์„ฑ 27 4.1.2 ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ƒํƒœ ๋ฐ ์ •์‹ ๊ฑด๊ฐ• ํŠน์„ฑ 40 4.1.3 ๊ฑด๊ฐ•ํ–‰ํƒœ ํŠน์„ฑ 40 4.1.4 ์งˆ๋ณ‘ ์ดํ™˜ ํŠน์„ฑ 41 4.2 ๊ฑด๊ฐ•ํ–‰ํƒœ, ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ƒํƒœ ๋ฐ ์ •์‹ ๊ฑด๊ฐ•, ์งˆ๋ณ‘ ์ดํ™˜ ๋น„๊ต 44 4.3 ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ƒํƒœ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ 53 5. ๊ณ ์ฐฐ 66 5.1 ๋…ผ์˜ 66 5.2 ํ•œ๊ณ„์  70 6. ๊ฒฐ๋ก  71 7. ์ฐธ๊ณ ๋ฌธํ—Œ 76 Abstract 79Maste

    Risk Factors assoiated with the readmission of the spinal surgery patients

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    ๋ณ‘์›๊ฒฝ์˜์ „๊ณต/์„์‚ฌ๋ณธ ์—ฐ๊ตฌ๋Š” ์ฒ™์ถ” ์ˆ˜์ˆ  ํ›„ ํ‡ด์›ํ•œ ํ™˜์ž๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์—ฌ ์ธ๊ตฌ์‚ฌํšŒํ•™์  ํŠน์„ฑ, ์ž„์ƒํ•™์  ํŠน์„ฑ, ์ง„๋ฃŒ๊ณผ์ • ํŠน์„ฑ๊ณผ ์ฒ™์ถ” ์ˆ˜์ˆ  ํ›„ ์žฌ์ž…์›๊ณผ์˜ ๊ด€๋ จ์„ฑ์„ ๋น„๊ตํ•˜๋Š” ํ›„ํ–ฅ์  ํ™˜์ž-๋Œ€์กฐ๊ตฐ ์—ฐ๊ตฌ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” 2005๋…„ 11์›”๋ถ€ํ„ฐ 2012๋…„ 10์›”๊นŒ์ง€ ์„œ์šธ ์†Œ์žฌ 2000๋ณ‘์ƒ ๊ทœ๋ชจ์˜ 1๊ฐœ ๋Œ€ํ•™๋ณ‘์› ์‹ ๊ฒฝ์™ธ๊ณผ์—์„œ ๊ณ„ํš๋œ ์ฒ™์ถ” ์งˆํ™˜ ์ˆ˜์ˆ ์„ ๋ฐ›์€ 9587๋ช… ์ค‘, ์ˆ˜์ˆ  ํ›„ ํ‡ด์›ํ•˜์—ฌ 30์ผ ์ด๋‚ด ์žฌ์ž…์›ํ•œ ํ™˜์ž๋“ค 102๋ช…๊ณผ ์žฌ์ž…์›ํ•˜์ง€ ์•Š์€ ํ™˜์ž๋“ค ์ค‘ ์—ฐ๋ น๊ณผ ์„ฑ๋ณ„์— ๋งž์ถ˜ ๋ฌด์ž‘์œ„ ์ถ”์ถœ์„ ํ†ตํ•˜์—ฌ ์„ ์ •ํ•œ ํ™˜์ž๋“ค 487๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๋‹ค. ์žฌ์ž…์› ํŠน์„ฑ๊ณผ ๊ด€๋ จํ•œ ๋ณ€์ˆ˜๋“ค์€ ํ™˜์ž์˜ ์˜๋ฌด๊ธฐ๋ก์„ ํ† ๋Œ€๋กœ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์ˆ˜์ง‘ํ•œ ๋ณ€์ˆ˜๋“ค์€ Student t-test, Chi-square test, Multiple logistic regression analysis๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ, ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค.1. ์ฒ™์ถ” ์ˆ˜์ˆ  ํ›„ ์žฌ์ž…์›๊ตฐ ์ž์ฒด๊ฐ€ ์œ ํ•ฉ์ˆ ์„ ํ•œ ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์œผ๋ฉฐ, ์„œ์šธ ๋ฐ ์ˆ˜๋„๊ถŒ์— ์‚ด๊ณ  ์žˆ๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•˜๋‹ค. ์ฒ™์ถ” ์ˆ˜์ˆ  ๋น„๊ด€๋ จ ์ฆ์ƒ์œผ๋กœ ์ž…์›ํ•œ ๊ตฐ์ด ์žฌ์›์ผ ์ˆ˜, ์ง„๋ฃŒ๋น„, ์ค‘ํ™˜์ž์‹ค ์ž…์‹ค, ๋™๋ฐ˜ ์ƒ๋ณ‘์ˆ˜์˜ ๊ฐœ์ˆ˜๊ฐ€ ๋” ๋งŽ์•˜๋‹ค. ์ฒ™์ถ” ์ˆ˜์ˆ  ๊ด€๋ จ ์ฆ์ƒ์œผ๋กœ ์žฌ์ž…์›ํ•œ ๊ตฐ์€ ์žฌ์ˆ˜์ˆ  ๊ณผ๊ฑฐ๋ ฅ์ด ์žˆ๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์œผ๋ฉฐ ํ‰๊ท  ์—ฐ๋ น์ด ๋†’์•˜๋‹ค.2. ์—ฐ๊ตฌ ๋Œ€์ƒ์ž์˜ ํŠน์„ฑ๊ณผ ์žฌ์ž…์›๊ณผ์˜ ๊ด€๋ จ์„ฑ์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์—ฐ๋ น, ์ง„๋ฃŒ๋น„, ์žฌ์›์ผ์ˆ˜, ์ค‘ํ™˜์ž์‹ค ์ž…์‹ค, ์œ ํ•ฉ์ˆ  ์—ฌ๋ถ€, ์œ ํ•ฉ์ˆ  ๋ฒ”์œ„, ์žฌ์ˆ˜์ˆ  ๊ณผ๊ฑฐ๋ ฅ, ๋‹น๋‡จ, ์ •์‹ ์งˆํ™˜, ๋™๋ฐ˜์ƒ๋ณ‘ ์ˆ˜, ์ˆ˜์ˆ ์‹œ ์ถœํ˜ˆ๋Ÿ‰, ์ˆ˜์ˆ  ์‹œ๊ฐ„์—์„œ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ๋‹ค.3. ์ฒ™์ถ” ์ˆ˜์ˆ  ํ›„ ์žฌ์ž…์›์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์œผ๋กœ๋Š” ์žฌ์›์ผ์ˆ˜, ์ง„๋ฃŒ๋น„ ์ด์•ก, ์ˆ˜์ˆ  ์‹œ๊ฐ„, ์žฌ์ˆ˜์ˆ  ๊ณผ๊ฑฐ๋ ฅ, ์ค‘ํ™˜์ž์‹ค ์ž…์‹ค๋กœ ๋ฐํ˜€์กŒ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋ฐํ˜€์ง„ ๋Œ€๋กœ ์žฌ์›์ผ ์ˆ˜ ๋ฐ ์ˆ˜์ˆ  ์‹œ๊ฐ„์ด ์งง์„ ๊ฒฝ์šฐ, ์žฌ์ˆ˜์ˆ  ๊ณผ๊ฑฐ๋ ฅ ๋ฐ ์ค‘ํ™˜์ž์‹ค ์ž…์‹ค์„ ํ•œ ๊ฒฝ์šฐ๋Š” ํ–ฅํ›„ ์žฌ์ž…์› ์œ„ํ—˜๊ตฐ์ž„์„ ๊ณ ๋ คํ•˜์—ฌ ์žฌ์›๊ธฐ๊ฐ„ ๋™์•ˆ ๊ด€๋ฆฌ๊ฐ€ ์ž˜ ์ด๋ฃจ์–ด์ ธ์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋ฐํ˜€์ง„ ์žฌ์ž…์› ๊ด€๋ จ ์š”์ธ์„ ์ œ์–ดํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ๊ฐ•๊ตฌํ•œ๋‹ค๋ฉด ์žฌ์ž…์›์„ ์–ต์ œํ•˜์—ฌ ์˜๋ฃŒ๋น„์šฉ์„ ๊ฐ์†Œ์‹œํ‚ค๊ณ , ์น˜๋ฃŒ ์„ฑ์ ์„ ๋†’์ด๊ณ , ํ™˜์ž์˜ ๋งŒ์กฑ๋„๋ฅผ ๋†’์ผ ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.ope

    1997 financial supervisory reform in Korea : a study on policy preferences of bureaucrats

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ •์น˜ํ•™๊ณผ,2009.8.Maste

    Spatial cluster detection for ordinal data using stochastic ordering

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    ์˜ํ•™์ „์‚ฐํ†ต๊ณ„ํ•™ ํ˜‘๋™๊ณผ์ •/์„์‚ฌ๊ณต๊ฐ„ ๊ฒ€์ƒ‰ ํ†ต๊ณ„๋Ÿ‰์€ ์ง€๋ฆฌ์  ์งˆ๋ณ‘ ๊ฐ์‹œ ์—ฐ๊ตฌ์—์„œ ๋‹ค๋ฅธ ์ง€์—ญ์— ๋น„ํ•˜์—ฌ ์œ ๋ณ‘๋ฅ , ๋ฐœ๋ณ‘๋ฅ  ๋˜๋Š” ์‚ฌ๋ง๋ฅ ์ด ๋†’๊ฑฐ๋‚˜ ๋‚ฎ์€ ์–ด๋–ค ํŠน์ •ํ•œ ์ง€์—ญ์„ ๋ฐœ๊ฒฌํ•˜๊ณ , ์ด์— ๋Œ€ํ•œ ํ†ต๊ณ„ํ•™์  ์œ ์˜์„ฑ์„ ํ‰๊ฐ€ ํ•˜๋Š”๋ฐ ์‚ฌ์šฉ๋œ๋‹ค. ์งˆ๋ณ‘์˜ ์ง„ํ–‰๋‹จ๊ณ„์™€ ๊ฐ™์€ ์ˆœ์„œํ˜• ์ž๋ฃŒ์—์„œ์˜ ๊ณต๊ฐ„ ๊ฒ€์ƒ‰ ํ†ต๊ณ„๋Ÿ‰(Jung et al., 2007)์€ ์งˆ๋ณ‘์˜ ์ง„ํ–‰์ •๋„๊ฐ€ ์‹ฌํ•ด์งˆ์ˆ˜๋ก ๊ด€์ธก๋œ ๋น„์œจ์ด ๋†’์•„์ง€๋Š” ๊ณต๊ฐ„ ๊ตฐ์ง‘์„ ์ฐพ๊ธฐ ์œ„ํ•œ ํ†ต๊ณ„๋Ÿ‰์œผ๋กœ, ์ด๋Ÿฌํ•œ ๊ณต๊ฐ„ ๊ตฐ์ง‘์„ ์ฐพ๊ธฐ ์œ„ํ•ด likelihood ratio ordering์˜ ์—„๊ฒฉํ•œ ์ œ์•ฝ์กฐ๊ฑด์„ ๊ฐ–๋Š”๋‹ค. ์ด๋กœ ์ธํ•˜์—ฌ ์ฐพ์„ ์ˆ˜ ์žˆ๋Š” ๊ตฐ์ง‘์˜ ๋ฒ”์œ„๊ฐ€ ํ•œ์ •๋˜๊ณ , ์ œ์•ฝ์กฐ๊ฑด์„ ๋งŒ์กฑํ•˜๊ธฐ์œ„ํ•ด ๋ฒ”์ฃผ๊ฐ€ ํ•ฉ์ณ์ง์œผ๋กœ์จ ๊ณต๋ณ€๋Ÿ‰ ์ ์šฉ์— ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๊ธฐ์กด ๋ฐฉ๋ฒ•์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ์—„๊ฒฉํ•œ ์ œ์•ฝ์„ ์™„ํ™”ํ•  ์ˆ˜ ์žˆ๋Š” stochastic ordering์„ ์ด์šฉํ•˜์—ฌ ์–ด๋–ป๊ฒŒ ๊ณต๊ฐ„ ๊ตฐ์ง‘์„ ์ฐพ๊ณ , ๊ธฐ์กด ๋ฐฉ๋ฒ•์— ๋น„ํ•˜์—ฌ ์–ด๋– ํ•œ ์žฅ์ ์ด ์žˆ๋Š”์ง€ ๋ณด์ด๊ณ ์ž ํ–ˆ๋‹ค. Robertson et al.(1988)์ด ์ œ์‹œํ•œ stochastic ordering ํ•˜์—์„œ์˜ ์ตœ๋Œ€์šฐ๋„์ถ”์ •๋Ÿ‰๊ณผ ์šฐ๋„๋น„ ๊ฒ€์ •ํ†ต๊ณ„๋Ÿ‰์„ ์ด์šฉํ•˜์—ฌ stochastic ordering ํ•˜์—์„œ ๊ณต๊ฐ„ ๊ตฐ์ง‘์„ ์ฐพ์„ ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๋ชจ์˜์‹คํ—˜์„ ํ†ตํ•˜์—ฌ likelihood ratio ordering์„ ์œ„๋ฐ˜ํ•˜๊ณ  stochastic ordering์„ ๊ฐ–๋Š” ๊ฒฝ์šฐ์— ๋Œ€ํ•˜์—ฌ ๋‘ ๋ฐฉ๋ฒ•์˜ ๊ฒ€์ •๋ ฅ, ๋ฏผ๊ฐ๋„, ์–‘์„ฑ์˜ˆ์ธก๋„๋ฅผ ๋น„๊ตํ•˜์˜€๋‹ค. ์ด๋ฅผ ๋น„๊ตํ•ด ๋ณธ ๊ฒฐ๊ณผ, ๊ณต๊ฐ„ ๊ตฐ์ง‘์ด ํฌ๊ฑฐ๋‚˜ ์ž‘์€ ๊ฒฝ์šฐ ๋ชจ๋‘ stochastic ordering์„ ์ด์šฉํ•œ ๋ฐฉ๋ฒ•์ด ๋” ๋†’์€ ๊ฒ€์ •๋ ฅ๊ณผ ์ •ํ™•๋„๋ฅผ ๋ณด์˜€๋‹ค. ๋˜ํ•œ ํ…์‚ฌ์Šค ์ฃผ์˜ ์œ ๋ฐฉ์•”์ž๋ฃŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋‘ ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๋ฐœ๊ฒฌํ•œ ๊ณต๊ฐ„ ๊ตฐ์ง‘์ด ์–ด๋– ํ•œ ์ฐจ์ด๊ฐ€ ์žˆ๋Š”์ง€ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.restrictio

    ์šฐ์ˆ˜๊ณผ์ œ๋ณด๊ณ ์„œ-๋ถ€๋ฉ”๋ž‘์— ๋Œ€ํ•œ ๊ณผํ•™์  ๊ณ ์ฐฐ

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    ํ†ตํ•ฉ์  ์ •์น˜์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜ ๋ชจํ˜•์˜ ์ œ์•ˆ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ์–ธ๋ก ์ •๋ณดํ•™๊ณผ, 2020. 8. ์–‘์Šน๋ชฉ.์ด ์—ฐ๊ตฌ๋Š” ์†Œ์…œ ๋ฏธ๋””์–ด์—์„œ ์ •๋ณด ์ด์šฉ์€ ์„ ํƒ์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค๋Š” ์ ์— ์ฃผ๋ชฉํ•˜๊ณ , ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์„ ์„ ํƒ์  ๋…ธ์ถœ๊ณผ ๊ต์ฐจ ๋…ธ์ถœ, ์„ ํƒ์  ํšŒํ”ผ๋กœ ๊ตฌ๋ถ„ํ•œ ๋’ค ์„ธ ๊ฐ€์ง€ ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์˜ ์ •์น˜์  ํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜๊ณ ์ž ํ–ˆ๋‹ค. ํŠนํžˆ ์ด ์—ฐ๊ตฌ๋Š” ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์ด ์–ด๋– ํ•œ ์กฐ๊ฑด์—์„œ ์ด๋ฃจ์–ด์ง€๋Š”์ง€ ์‚ดํŽด๋ณด๊ณ , ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์ด ๊ฐœ์ธ์˜ ์ •์น˜์  ํƒœ๋„์™€ ๋ถ€์ •์  ๊ฐ์ •, ๋‚˜์•„๊ฐ€ ์ •์น˜์  ์ฐธ์—ฌ ํ–‰๋™์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•ด ๊ฒ€ํ† ํ•จ์œผ๋กœ์จ ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์˜ ์›์ธ๊ณผ ๊ฒฐ๊ณผ๋ฅผ ์•„์šฐ๋ฅด๋Š” ํ†ตํ•ฉ์  ์ •์น˜ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜ ๋ชจํ˜•์„ ์ œ์•ˆํ•˜๊ณ ์ž ํ–ˆ๋‹ค. ๋จผ์ € ์†Œ์…œ ๋ฏธ๋””์–ด ํ™˜๊ฒฝ์—์„œ ๊ฐœ์ธ์€ ํƒ€์ธ๊ณผ์˜ ๊ด€๊ณ„ ์†์—์„œ ์ •๋ณด ์ด์šฉ์„ ํ•˜๊ฒŒ ๋œ๋‹ค๋Š” ์ ์—์„œ ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์˜ ์„ ํ–‰ ์š”์ธ์œผ๋กœ ๋„คํŠธ์›Œํฌ ํŠน์„ฑ๊ณผ ์ •๋ณด ๋‹ค์–‘์„ฑ์˜ ์—ญํ• ์— ๋Œ€ํ•ด ์‚ดํŽด๋ณด์•˜๋‹ค. ์†Œ์…œ ๋ฏธ๋””์–ด ์นœ๊ตฌ๋“ค๊ณผ์˜ ์ •์น˜์ ยท์‚ฌํšŒ์  ๋™์งˆ์„ฑ๊ณผ ๋„คํŠธ์›Œํฌ ๊ทœ๋ชจ๋Š” ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์— ์ง์ ‘์ ์œผ๋กœ ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด์šฉ์ž๊ฐ€ ์†Œ์…œ ๋ฏธ๋””์–ด์—์„œ ์šฐ์—ฐํžˆ ์ ‘ํ•  ์ˆ˜ ์žˆ๋Š” ์ •๋ณด์˜ ๋‹ค์–‘์„ฑ์„ ํ†ตํ•ด ๊ฐ„์ ‘์ ์œผ๋กœ ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์— ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ๋‹ค. ์†Œ์…œ ๋ฏธ๋””์–ด ์ด์šฉ์ž๋Š” SNS ๋‰ด์Šคํ”ผ๋“œ๋‚˜ ์นด์นด์˜คํ†ก ์ฑ„ํŒ…๋ฐฉ์„ ํ†ตํ•ด ์†Œ์…œ ๋ฏธ๋””์–ด ์นœ๊ตฌ๋กœ๋ถ€ํ„ฐ ๋‚ด์šฉ์ ์œผ๋กœ ํ’๋ถ€ํ•œ ์ •๋ณด์™€ ํ•จ๊ป˜ ์ด์Šˆ์— ๋Œ€ํ•œ ์ฐฌ์„ฑ๊ณผ ๋ฐ˜๋Œ€ ์˜๊ฒฌ ์ค‘ ์ผ๋ถ€๋ฅผ ์ „๋‹ฌ ๋ฐ›์„ ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋‚ด์šฉ์„ ๋ฐ”ํƒ•์œผ๋กœ ์ด ์—ฐ๊ตฌ๋Š” ์†Œ์…œ ๋ฏธ๋””์–ด ์ด์šฉ์ž์˜ ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์ด ์ด๋ฃจ์–ด์ง€๋Š” ๊ตฌ์กฐ์ ยทํ™˜๊ฒฝ์  ์š”์ธ์œผ๋กœ ๋„คํŠธ์›Œํฌ ํŠน์„ฑ(๋„คํŠธ์›Œํฌ ๋™์งˆ์„ฑ, ๋„คํŠธ์›Œํฌ ๊ทœ๋ชจ)๊ณผ ํ•จ๊ป˜ ์ •๋ณด ํ™˜๊ฒฝ์˜ ๋‹ค์–‘์„ฑ(๋‰ด์Šค ๋‹ค์–‘์„ฑ, ์˜๊ฒฌ ๊ท ํ˜•์„ฑ)์„ ๊ณ ๋ คํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ด ์—ฐ๊ตฌ๋Š” ์„ธ ๊ฐ€์ง€ ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์ด ์†Œ์…œ ๋ฏธ๋””์–ด ์ด์šฉ์ž์˜ ์ •์น˜์  ํƒœ๋„๋‚˜ ๋ถ€์ •์  ๊ฐ์ •, ๋‚˜์•„๊ฐ€ ์ •์น˜์  ์ฐธ์—ฌ ํ–‰๋™์— ๋ชจ๋‘ ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์„ ๊ณ ๋ คํ•˜์˜€๋‹ค. ์„ ํƒ์  ๋…ธ์ถœ์ด๋‚˜ ์„ ํƒ์  ํšŒํ”ผ์™€ ๊ฐ™์€ ํ™•์ฆ ํŽธํ–ฅ์  ์ •๋ณด ์ด์šฉ๊ณผ ํ•จ๊ป˜ ์ด๊ฒฌ์— ๋Œ€ํ•œ ๋…ธ์ถœ์ด ์†Œ์…œ ๋ฏธ๋””์–ด ์ด์šฉ์ž์˜ ํƒœ๋„ ๊ทน๋‹จ์„ฑ๊ณผ ์–ด๋– ํ•œ ๊ด€๋ จ์ด ์žˆ๋Š”์ง€ ์‚ดํŽด๋ณด์•˜๋‹ค. ํ•œํŽธ ์†Œ์…œ ๋ฏธ๋””์–ด์—์„œ ์ •์น˜ ์ •๋ณด์˜ ๊ณต์œ ๋ฅผ ํ†ตํ•ด ๋ถ„๋…ธ์™€ ๊ฐ™์€ ๋ถ€์ •์  ๊ฐ์ •์ด ํ™•์‚ฐ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ์ด ์—ฐ๊ตฌ๋Š” ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์ด ํŠน์ • ์ด์Šˆ์— ๋Œ€ํ•œ ์†Œ์…œ ๋ฏธ๋””์–ด ์ด์šฉ์ž์˜ ๋ถ„๋…ธ์— ์–ด๋– ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€๋„ ํ•จ๊ป˜ ์‚ดํŽด๋ณด์•˜๋‹ค. ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์€ ์ •์น˜์  ํƒœ๋„๋‚˜ ๋ถ„๋…ธ ์ด์™ธ์— ์ •์น˜์  ์ฐธ์—ฌ ํ–‰๋™์— ์ง์ ‘์ ์œผ๋กœ ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์†Œ์…œ ๋ฏธ๋””์–ด ์ด์šฉ์ž์˜ ํƒœ๋„ ๊ทน๋‹จ์„ฑ๊ณผ ๋ถ„๋…ธ๋ฅผ ํ†ตํ•ด ๊ฐ„์ ‘์ ์œผ๋กœ ์ •์น˜์  ์ฐธ์—ฌ ํ–‰๋™์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์ •์น˜์  ์ฐธ์—ฌ ํ–‰๋™์„ ์†Œ์…œ ๋ฏธ๋””์–ด ํ™œ๋™, ์˜จ๋ผ์ธ ์ •์น˜ ์ฐธ์—ฌ, ์˜คํ”„๋ผ์ธ ์ •์น˜ ์ฐธ์—ฌ๋กœ ๊ตฌ๋ถ„ํ•˜๊ณ  ์„ธ ๊ฐ€์ง€ ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์ด ๋ฏธ์น˜๋Š” ์ง์ ‘ ํšจ๊ณผ์™€ ํƒœ๋„์™€ ๋ถ„๋…ธ๋ฅผ ๊ฒฝ์œ ํ•œ ๊ฐ„์ ‘ ํšจ๊ณผ๋ฅผ ์‚ดํŽด๋ณด์•˜๋‹ค. ์ด์™€ ํ•จ๊ป˜ ์†Œ์…œ ๋ฏธ๋””์–ด์—์„œ ์ด๋ฃจ์–ด์ง€๋Š” ์„ ํƒ์  ์ •๋ณด ์ด์šฉ ๊ณผ์ •์ด ์ด์šฉ์ž์˜ ๊ฐœ์ธ๋‚ด์ (intrapersonal) ํŠน์„ฑ์ธ ์ด์Šˆ ๊ด€์—ฌ๋„์™€ ์ด๋… ๊ทน๋‹จ์„ฑ, ๊ด€์šฉ๊ณผ ์ด์šฉ์ž์˜ ์—ฐ๋ น์— ๋”ฐ๋ผ ์ฐจ์ด๊ฐ€ ์žˆ๋Š”์ง€ ์‚ดํŽด๋ณด๊ธฐ ์œ„ํ•ด ์ด๋“ค์˜ ์กฐ์ ˆํšจ๊ณผ์— ๋Œ€ํ•ด ์ถ”๊ฐ€๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ฃผ๋ชฉํ•œ ๊ฐœ์ธ๋‚ด์  ํŠน์„ฑ์€ ํ™•์ฆ ํŽธํ–ฅ์  ์ •๋ณด ์ด์šฉ์ด๋‚˜ ์ด๊ฒฌ์— ๋Œ€ํ•œ ์ˆ˜์šฉ๊ณผ ๊ด€๋ จ์ด ์žˆ๋Š” ๊ฐœ์ธ์˜ ๊ณ ์œ ํ•œ ํŠน์„ฑ์œผ๋กœ ์†Œ์…œ ๋ฏธ๋””์–ด ํ™˜๊ฒฝ์—์„œ ๊ฐœ์ธ์  ํŠน์„ฑ์ด ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์— ์—ฌ์ „ํžˆ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š”์ง€ ๊ฒ€ํ† ํ•ด ๋ณด๊ณ ์ž ํ–ˆ๋‹ค. ์ด์™€ ๊ฐ™์€ ๋‚ด์šฉ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ๋งŒ20์„ธ ์ด์ƒ ์„ฑ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ์˜จ๋ผ์ธ ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ์‹ค์‹œํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์˜ ์ •์น˜์  ํšจ๊ณผ๊ฐ€ ์†Œ์…œ ๋ฏธ๋””์–ด ํ”Œ๋žซํผ์ด๋‚˜ ์ด์Šˆ์— ๋”ฐ๋ผ ๋‹ค๋ฅด๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š”์ง€ ์‚ดํŽด๋ณด๊ธฐ ์œ„ํ•ด SNS์™€ ์นด์นด์˜คํ†ก ์ด์šฉ์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๋‘ ๊ฐ€์ง€ ์ด์Šˆ(์กฐ๊ตญ ์ „ ๋ฒ•๋ฌด๋ถ€ ์žฅ๊ด€ ์ž„๋ช… ์ด์Šˆ, ์ฝ”๋กœ๋‚˜19์— ๋Œ€ํ•œ ์ •๋ถ€์˜ ๋ฐฉ์—ญ ๋Œ€์ฑ… ์ด์Šˆ)์™€ ๊ด€๋ จ๋œ ์ •๋ณด ์ด์šฉ ๊ฒฝํ—˜์ด ์žˆ๋Š”์ง€์— ๋Œ€ํ•ด ์งˆ๋ฌธํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋‘ ๊ฐ€์ง€ ์ข…๋ฅ˜์˜ ์†Œ์…œ ๋ฏธ๋””์–ด ํ”Œ๋žซํผ๊ณผ ์ด์Šˆ๋ฅผ ์ด์šฉํ•ด(2 ๋งค์ฒด X 2 ์ด์Šˆ) ์ด 4๊ฐœ์˜ ์ผ€์ด์Šค๋ฅผ ๊ตฌ์„ฑํ•œ ๋’ค, ๊ฐ ์ผ€์ด์Šค์˜ ๋ถ„์„ ๊ฒฐ๊ณผ๋“ค์„ ๋น„๊ตํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋ฌธ์ œ์™€ ์—ฐ๊ตฌ๊ฐ€์„ค์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ตฌ์กฐ๋ฐฉ์ •์‹ ๋ชจํ˜• ๋ถ„์„๊ณผ ๋‹ค์ค‘ ๋งค๊ฐœํšจ๊ณผ ๋ถ„์„, ๋‹ค์ง‘๋‹จ ๋ถ„์„, ์ดํ•ญ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ๋จผ์ € ๋ชจ๋“  ์ผ€์ด์Šค์—์„œ ๋„คํŠธ์›Œํฌ ํŠน์„ฑ ์ค‘ ๋„คํŠธ์›Œํฌ ๋™์งˆ์„ฑ์€ ๋‰ด์Šค ๋‹ค์–‘์„ฑ์„ ๋งค๊ฐœํ•ด ์„ ํƒ์  ๋…ธ์ถœ๊ณผ ๊ต์ฐจ ๋…ธ์ถœ์— ๊ฐ„์ ‘์ ์ธ ์˜ํ–ฅ์„ ์ฃผ์—ˆ๊ณ  ์„ ํƒ์  ํšŒํ”ผ์— ์ง์ ‘์ ์ธ ์˜ํ–ฅ์„ ์ฃผ์—ˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ ํƒœ๋„์— ๋Œ€ํ•œ ์„ ํƒ์  ๋…ธ์ถœ์˜ ๊ธ์ •์ ์ธ ์˜ํ–ฅ๊ณผ ๊ต์ฐจ ๋…ธ์ถœ์˜ ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์ด ์žˆ์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์†Œ์…œ ๋ฏธ๋””์–ด ํ™œ๋™๊ณผ ์˜จ๋ผ์ธ ์ •์น˜ ์ฐธ์—ฌ์— ๋Œ€ํ•œ ์„ ํƒ์  ๋…ธ์ถœ์˜ ๊ธ์ •์ ์ธ ์˜ํ–ฅ๊ณผ ๋ชจ๋“  ์ •์น˜์  ์ฐธ์—ฌ ํ–‰๋™์— ๋Œ€ํ•œ ์„ ํƒ์  ํšŒํ”ผ์˜ ๊ธ์ •์ ์ธ ์˜ํ–ฅ์ด ์žˆ์—ˆ๋‹ค. ํƒœ๋„ ๊ทน๋‹จ์„ฑ๊ณผ ๋ถ„๋…ธ ์ค‘์—์„œ๋Š” ์ด์Šˆ์— ๋Œ€ํ•œ ๋ถ„๋…ธ๋งŒ์ด ์†Œ์…œ ๋ฏธ๋””์–ด ํ™œ๋™์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ์ด์Šˆ์™€ ๋งค์ฒด์— ๋”ฐ๋ฅธ ์ฐจ์ด๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ •ํŒŒ์  ์ด์Šˆ์ธ ์กฐ๊ตญ ์ „ ๋ฒ•๋ฌด๋ถ€ ์žฅ๊ด€ ์ž„๋ช… ์ด์Šˆ์™€ ๊ด€๋ จ ์žˆ๋Š” ์ผ€์ด์Šค์—์„œ ์˜๊ฒฌ ๊ท ํ˜•์„ฑ์€ ๊ต์ฐจ ๋…ธ์ถœ์— ์ •์ ์ธ ์˜ํ–ฅ์„ ์ฃผ์—ˆ๊ณ , ์„ ํƒ์  ๋…ธ์ถœ์€ ๋ถ„๋…ธ์— ์ •์ ์ธ ์˜ํ–ฅ์„ ์ฃผ์—ˆ๋‹ค. ๋ฐ˜๋ฉด ๋น„์ •ํŒŒ์  ์ด์Šˆ์ด์ž ๊ณต์ค‘๋ณด๊ฑด ์ด์Šˆ์ธ ์ฝ”๋กœ๋‚˜19์— ๋Œ€ํ•œ ์ •๋ถ€์˜ ๋ฐฉ์—ญ ๋Œ€์ฑ… ์ด์Šˆ์™€ ๊ด€๋ จ๋œ ์ผ€์ด์Šค์—์„œ ์˜๊ฒฌ ๊ท ํ˜•์„ฑ์€ ์„ ํƒ์  ํšŒํ”ผ์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ์ฃผ๊ณ , ๊ต์ฐจ ๋…ธ์ถœ์€ ๋ถ„๋…ธ์™€ ์˜คํ”„๋ผ์ธ ์ •์น˜ ์ฐธ์—ฌ์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ SNS์— ํ•œํ•˜์—ฌ ์˜๊ฒฌ ๊ท ํ˜•์„ฑ์— ๋Œ€ํ•œ ๋„คํŠธ์›Œํฌ ๋™์งˆ์„ฑ์˜ ๋ถ€์ •์ ์ธ ์˜ํ–ฅ, ์„ ํƒ์  ํšŒํ”ผ์— ๋Œ€ํ•œ ๋„คํŠธ์›Œํฌ ๊ทœ๋ชจ์˜ ๊ธ์ •์ ์ธ ์˜ํ–ฅ์ด ๋ฐœ๊ฒฌ๋˜์—ˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ ์กฐ์ ˆ ํšจ๊ณผ์— ๋Œ€ํ•ด ๋ณด๋ฉด, ์ด์Šˆ ๊ด€์—ฌ๋„๋Š” ์กฐ๊ตญ ์ „ ๋ฒ•๋ฌด๋ถ€ ์žฅ๊ด€ ์ž„๋ช… ์ด์Šˆ ๊ด€๋ จ ์ผ€์ด์Šค์—์„œ ๋„คํŠธ์›Œํฌ ๋™์งˆ์„ฑ๊ณผ ๊ต์ฐจ ๋…ธ์ถœ์˜ ๊ด€๊ณ„๋ฅผ ์กฐ์ ˆํ•˜์˜€๊ณ , ์นด์นด์˜คํ†ก ๊ด€๋ จ ์ผ€์ด์Šค์—์„œ ์„ ํƒ์  ํšŒํ”ผ์™€ ์˜จ๋ผ์ธ/์˜คํ”„๋ผ์ธ ์ •์น˜ ์ฐธ์—ฌ์˜ ๊ด€๊ณ„๋ฅผ ์กฐ์ ˆํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋… ๊ทน๋‹จ์„ฑ์€ ์ •ํŒŒ์  ์ด์Šˆ ๊ด€๋ จ ์ผ€์ด์Šค์—์„œ ๊ต์ฐจ ๋…ธ์ถœ๊ณผ ํƒœ๋„์˜ ๊ด€๊ณ„๋ฅผ ์กฐ์ ˆํ•˜์˜€๊ณ , ๊ด€์šฉ์€ SNS ๊ด€๋ จ ์ผ€์ด์Šค์—์„œ ๋‰ด์Šค ๋‹ค์–‘์„ฑ๊ณผ ์„ ํƒ์  ๋…ธ์ถœ์˜ ๊ด€๊ณ„๋ฅผ ์กฐ์ ˆํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์—ฐ๋ น์€ ์กฐ๊ตญ ์ „ ๋ฒ•๋ฌด๋ถ€ ์žฅ๊ด€ ์ž„๋ช… ์ด์Šˆ ๊ด€๋ จ ์ผ€์ด์Šค์—์„œ ์„ ํƒ์  ๋…ธ์ถœ๊ณผ ๊ต์ฐจ ๋…ธ์ถœ์ด ๋ถ„๋…ธ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ์กฐ์ ˆ ํšจ๊ณผ๊ฐ€ ์žˆ์—ˆ์œผ๋ฉฐ, SNS ๊ด€๋ จ ์ผ€์ด์Šค์—์„œ ์„ ํƒ์  ํšŒํ”ผ์™€ ์†Œ์…œ ๋ฏธ๋””์–ด ํ™œ๋™์˜ ๊ด€๊ณ„์— ์กฐ์ ˆ ํšจ๊ณผ๊ฐ€ ์žˆ์—ˆ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ๊ฐœ์ธ์ด ์†ํ•ด ์žˆ๋Š” ์†Œ์…œ ๋ฏธ๋””์–ด์˜ ๋„คํŠธ์›Œํฌ ํŠน์„ฑ๊ณผ ๋„คํŠธ์›Œํฌ๋กœ๋ถ€ํ„ฐ ๋น„๋กฏ๋˜๋Š” ์ •๋ณด ๋‹ค์–‘์„ฑ, ๊ทธ๋ฆฌ๊ณ  ์ •๋ณด ์ด์šฉ ์ดํ›„ ํ–‰๋™๊ณผ ๊ฐ™์€ ๋ฐ˜์‘์ด ์ผ์–ด๋‚˜๊ธฐ ์ด์ „ ๋‹จ๊ณ„์—์„œ ํ˜•์„ฑ๋˜๋Š” ์ด์šฉ์ž ๊ฐœ์ธ์˜ ํƒœ๋„ ๋ฐ ๋ถ„๋…ธ, ๋‚˜์•„๊ฐ€ ํ–‰๋™์œผ๋กœ ๋“œ๋Ÿฌ๋‚˜๋Š” ์ •์น˜์  ์ฐธ์—ฌ์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ์„ ํƒ์  ์ •๋ณด ์ด์šฉ๊ณผ ๊ด€๋ จ๋œ ๋‹ค์–‘ํ•œ ๋ณ€์ธ ๊ฐ„ ๊ด€๊ณ„๋ฅผ ํ†ตํ•ฉ์ ์œผ๋กœ ์‚ดํŽด๋ณด์•˜๋‹ค๋Š” ์ ์—์„œ ์˜๋ฏธ๊ฐ€ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์†Œ์…œ ๋ฏธ๋””์–ด์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜์ง€๋งŒ ๊ทธ๋™์•ˆ์˜ ์„ ํƒ์  ์ •๋ณด ์ด์šฉ ์—ฐ๊ตฌ์—์„œ ์ƒ๋Œ€์ ์œผ๋กœ ๋œ ๊ฐ•์กฐ๋˜์—ˆ๋˜ ์ด์šฉ์ž์˜ ๋„คํŠธ์›Œํฌ ํŠน์„ฑ์ด ์ง€๋‹Œ ์˜๋ฏธ์— ๋Œ€ํ•ด ํ™•์ธํ•˜์˜€๋‹ค. ๋„คํŠธ์›Œํฌ ๋™์งˆ์„ฑ์€ ์–ธ์ œ๋‚˜ ๋‰ด์Šค ๋‹ค์–‘์„ฑ์„ ๋งค๊ฐœํ•ด ์„ ํƒ์  ๋…ธ์ถœ๊ณผ ๊ต์ฐจ ๋…ธ์ถœ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ค‘์š”ํ•œ ๋ณ€์ธ์ด์—ˆ๋‹ค. ๋˜ํ•œ ์ •๋ณด ๋‹ค์–‘์„ฑ ์—ญ์‹œ ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์— ์˜๋ฏธ ์žˆ๋Š” ์—ญํ• ์„ ํ•˜๋ฉฐ, ํŠนํžˆ ๊ต์ฐจ ๋…ธ์ถœ์— ์žˆ์–ด ์˜๊ฒฌ ๊ท ํ˜•์„ฑ์˜ ์ค‘์š”์„ฑ์— ๋Œ€ํ•ด ํ™•์ธํ–ˆ๋‹ค. ์ •์น˜ ์ฐธ์—ฌ์™€ ๊ด€๋ จํ•˜์—ฌ ์ „์ฒด ์ผ€์ด์Šค์—์„œ ๊ณตํ†ต์ ์œผ๋กœ ์ •์น˜ ์ฐธ์—ฌ ํ–‰๋™์„ ์˜ˆ์ธกํ•˜๋Š” ๊ฒƒ์€ ์„ ํƒ์  ๋…ธ์ถœ์ด๋‚˜ ์„ ํƒ์  ํšŒํ”ผ์™€ ๊ฐ™์€ ํ™•์ฆ ํŽธํ–ฅ์  ์ •๋ณด ์ด์šฉ์ด๋ผ๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ด์Šˆ ๊ด€์—ฌ๋„, ์ด๋… ๊ทน๋‹จ์„ฑ, ๊ด€์šฉ, ์—ฐ๋ น์ด ์†Œ์…œ ๋ฏธ๋””์–ด์˜ ์„ ํƒ์  ์ •๋ณด ์ด์šฉ ๊ณผ์ •์—์„œ ์„œ๋กœ ๋‹ค๋ฅธ ๊ฒฝ๋กœ์— ์กฐ์ ˆ ํšจ๊ณผ๊ฐ€ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด์ƒ์˜ ๊ฒฐ๊ณผ๊ฐ€ ๊ฐ–๋Š” ์ด๋ก ์  ํ•จ์˜์™€ ์ œํ•œ์  ๋ฐ ํ›„์† ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ์ œ์–ธ์„ ๋ณธ๋ฌธ์— ์„œ์ˆ ํ•˜์˜€๋‹ค.Social media users selectively use information from the media that is consistent with their political views to reduce cognitive dissonance through defensive avoidance. They also experience cross-exposure to political information that is inconsistent with their views because of the information received from their social relationships. This study focused on the selective use of political information on social media and aimed to explore the political effects of three types of selective use of information: selective exposure, cross-exposure, and selective avoidance. First, we looked at network characteristics and information diversity as predictors of the use of selective information since it takes place within the social media networks. Social media users receive political news and opinions on political issues from their friends' newsfeeds on social media platforms or messenger rooms. Therefore, the diversity of the information environment, such as news diversity and balanced viewpoints, along with network characteristics such as network homogeneity and network size as structural and environmental factors that affect the selective use of information by social media users were considered. This study validated the direct effects of the political and social homogeneity of social media friends and network size on the selective use of information, as well as the mediating effects of information diversity that users might unintentionally encounter on social media. Furthermore, the study assumed that selective use of information affects not only political attitudes and negative emotions of social media users but also their political participation. Use of information by selective exposure or selective avoidance based on confirmation bias can further strengthen the individual's attitude, whereas cross-exposure can weaken the intensity of the attitude toward the issue. This study also examined how selective use of information affects users' feelings of anger toward issues because sharing political information on social media can spread negative feelings. Moreover, it can directly or indirectly affect political participation through attitudes and anger on issues. Political participation was divided into three categories: social media activities, online political participation, and offline political participation. It examined the direct and indirect impact of selective information use on political participation through their attitude and feelings of anger. Moreover, the moderated effects of individual characteristics, such as ideological extremity, issue involvement, tolerance, and age, on selective information use and political influence on social media were also analyzed. To answer the research questions and hypotheses, an online survey of adults aged 20-60 years was conducted. To examine whether selective use of information and political views varied depending on the media platform or issues, we asked users of Kakao Talk and SNS whether they had used information on two specific issues: one political and one health-related. With these two issues and the two social media platforms, we had a total of four cases to examine. To examine the full structural model of integrated political communication, structural equation modeling analysis was conducted. This study also conducted multiple mediation, multi-group, and logistic regression analyses. The results showed that in the four cases network homogeneity had an indirect impact on selective exposure and cross-exposure through news diversity, and a direct impact on selective avoidance. There were positive effects of selective exposure and negative effects of cross-exposure on attitudes. Selective exposure positively affected social media activities and online political participation, and selective avoidance also positively affected political participation. Only attitude and anger had a positive impact on social media activities. The difference between issues and social media platforms are as follows: In the political issue (issue 1), balanced viewpoints had a positive effect on cross-exposure, and in the health-related issue (issue 2) balanced viewpoints had a negative effect on selective avoidance. Moreover, for issue 1, selective exposure had a positive effect on anger, and for issue 2 cross-exposure had a positive effect on anger and offline political participation. Finally, the negative impact of network homogeneity on balanced viewpoints and the positive impact of network size on selective avoidance were found only in cases related to SNS. As for the moderating effect, issue involvement moderated the relationship between network homogeneity and cross-exposure in the case of issue 1 and the relationship between selective avoidance and online/offline political participation in the case of Kakao Talk. Moreover, ideological extremity moderated the relationship between cross-exposure and attitude in the case of issue 1, and tolerance moderated the relationship between news diversity and selective exposure in the case of SNS. Finally, age moderated the effect of selective and cross-exposure on anger in case of issue 1, and the relationship between selective avoidance and social media activity in the case of SNS. The study is useful as it analyzed the characteristics of the networks that individuals are exposed to, the information diversity provided from networks, and the relationships between the variables involved in the use of selective information, ranging from user attitudes and anger to political participation. This study recognized the role of networks based on the use of information in social media environments. Although network characteristics have different effects on information diversity and selective use of information depending on media, network homogeneity is an important variable that affects selective and cross-exposure by mediating news diversity regardless of media or issue. It is noteworthy that the balanced viewpoints have an impact on exposure to different opinions. There were also differences in the mediation effects of anger, depending on the type of issue. Finally, it was confirmed that issue engagement, ideological extremity, tolerance, and age have different moderating effects. On the basis of these results, the implications and limitations of the study are discussed.์ œ 1 ์žฅ. ๋ฌธ์ œ ์ œ๊ธฐ 1 ์ œ 2 ์žฅ. ์ด๋ก ์  ๋…ผ์˜ 6 ์ œ 1 ์ ˆ. ์†Œ์…œ ๋ฏธ๋””์–ด์™€ ์ •๋ณด ์ด์šฉ 6 1. ์ •๋ณด์— ๋Œ€ํ•œ ๋…ธ์ถœ 6 2. ์†Œ์…œ ๋ฏธ๋””์–ด์™€ ์ •๋ณด ๋…ธ์ถœ 11 ์ œ 2 ์ ˆ. ์†Œ์…œ ๋ฏธ๋””์–ด์™€ ์„ ํƒ์  ์ •๋ณด ์ด์šฉ 17 1. ์†Œ์…œ ๋ฏธ๋””์–ด์™€ ๋„คํŠธ์›Œํฌ 18 2. ๋„คํŠธ์›Œํฌ ํŠน์„ฑ๊ณผ ์ •๋ณด ๋‹ค์–‘์„ฑ 22 3. ๋„คํŠธ์›Œํฌ ํŠน์„ฑ๊ณผ ์„ ํƒ์  ์ •๋ณด ์ด์šฉ 26 4. ์†Œ์…œ ๋ฏธ๋””์–ด ์ •๋ณด ์ด์šฉ์˜ ๋งฅ๋ฝ 30 5. ๊ฐœ์ธ ๋‚ด์  ์š”์ธ๊ณผ ์„ ํƒ์  ์ •๋ณด ์ด์šฉ 37 ์ œ 3 ์ ˆ. ์†Œ์…œ ๋ฏธ๋””์–ด์™€ ์ •์น˜์  ์ฐธ์—ฌ 52 1. ์†Œ์…œ ๋ฏธ๋””์–ด ์ด์šฉ๊ณผ ์ •์น˜์  ์ฐธ์—ฌ ํ–‰๋™ 52 2. ์„ ํƒ์  ์ •๋ณด ์ด์šฉ๊ณผ ์ •์น˜์  ์–‘๊ทนํ™” 62 3. ํƒœ๋„์™€ ์ •์น˜์  ์ฐธ์—ฌ ํ–‰๋™ 67 4. ๋ถ„๋…ธ์™€ ์ •์น˜์  ์ฐธ์—ฌ ํ–‰๋™ 71 5. ์†Œ์…œ ๋ฏธ๋””์–ด์—์„œ์˜ ์ •์น˜์  ํ‘œํ˜„๊ณผ ์ •์น˜ ์ฐธ์—ฌ 75 ์ œ 4 ์ ˆ. ์—ฐ๊ตฌ ๋ฌธ์ œ ๋ฐ ์—ฐ๊ตฌ ๊ฐ€์„ค 78 1. ์—ฐ๊ตฌ ๋ฌธ์ œ ๋ฐ ์—ฐ๊ตฌ ๊ฐ€์„ค ์„ค์ • 78 2. ์—ฐ๊ตฌ ๋ชจํ˜• 90 ์ œ 3 ์žฅ. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 96 ์ œ 1 ์ ˆ. ์กฐ์‚ฌ ์ ˆ์ฐจ 96 1. ํ‘œ๋ณธ ์„ ์ • 96 2. ์†Œ์…œ ๋ฏธ๋””์–ด ์ด์šฉ์— ๊ด€ํ•œ ์„ค๋ฌธ 96 ์ œ 2 ์ ˆ. ์ฃผ์š” ๋ณ€์ธ์˜ ์ธก์ • ๋ฐ ๊ตฌ์„ฑ 100 1. ์†Œ์…œ ๋ฏธ๋””์–ด์˜ ๋„คํŠธ์›Œํฌ ํŠน์„ฑ 100 2. ์ •๋ณด ๋‹ค์–‘์„ฑ 103 3. ๊ฐœ์ธ ๋‚ด์  ํŠน์„ฑ 105 4. ์„ ํƒ์  ์ •๋ณด ์ด์šฉ 107 5. ํƒœ๋„ ๊ทน๋‹จ์„ฑ 111 6. ๋ถ„๋…ธ 112 7. ์ •์น˜์  ์ฐธ์—ฌ ํ–‰๋™ 113 8. ํ†ต์ œ๋ณ€์ธ 116 ์ œ 3 ์ ˆ. ์ž๋ฃŒ ๋ถ„์„ ๋ฐฉ๋ฒ• 118 1. ๊ตฌ์กฐ๋ฐฉ์ •์‹ ๋ชจํ˜• ๋ถ„์„ 118 2. ๋‹ค์ค‘ ๋งค๊ฐœํšจ๊ณผ ๋ถ„์„ 119 3. ๋‹ค์ง‘๋‹จ ๋ถ„์„ 121 4. ์ดํ•ญ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„ 123 ์ œ 4 ์žฅ. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 125 ์ œ 1 ์ ˆ. ์—ฐ๊ตฌ ๋Œ€์ƒ์ž์˜ ์ผ๋ฐ˜์ ์ธ ํŠน์ง• 125 ์ œ 2 ์ ˆ. ์ฃผ์š” ๋ณ€์ธ์˜ ํŠน์„ฑ 130 1. ์ฃผ์š” ๋ณ€์ธ์˜ ๊ธฐ์ˆ ํ†ต๊ณ„ 130 2. ์ฃผ์š” ๋ณ€์ธ ๊ฐ„ ์ƒ๊ด€๊ด€๊ณ„ 131 ์ œ3 ์ ˆ. ์—ฐ๊ตฌ ๋ฌธ์ œ ๋ฐ ์—ฐ๊ตฌ ๊ฐ€์„ค ๊ฒ€์ฆ 132 1. ๋ถ„์„ 1: ๊ตฌ์กฐ๋ฐฉ์ •์‹ ๋ชจํ˜• ๋ถ„์„ ๊ฒฐ๊ณผ 132 2. ๋ถ„์„ 2: ๋‹ค์ค‘ ๋งค๊ฐœํšจ๊ณผ ๋ถ„์„ ๊ฒฐ๊ณผ 147 3. ๋ถ„์„ 3: ๋‹ค์ง‘๋‹จ ๋ถ„์„ ๊ฒฐ๊ณผ 150 4. ๋ถ„์„ 4: ์ดํ•ญ ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€ ๋ถ„์„ ๊ฒฐ๊ณผ 156 ์ œ 4 ์ ˆ. ์ฃผ์š” ๊ฒฐ๊ณผ ์ •๋ฆฌ 161 1. ์ผ€์ด์Šค ๊ฐ„ ๊ณตํ†ต ๊ฒฐ๊ณผ 161 2. ์ด์Šˆ ๋ณ„ ๊ณตํ†ต ๊ฒฐ๊ณผ 164 3. ๋งค์ฒด ๊ฐ„ ๊ณตํ†ต ๊ฒฐ๊ณผ 167 4. ์ผ€์ด์Šค ๋ณ„ ๋งค๊ฐœํšจ๊ณผ ํŠน์ง• 169 5. ๊ฐœ์ธ ๋‚ด์  ํŠน์„ฑ๊ณผ ์—ฐ๋ น์˜ ์—ญํ•  172 ์ œ 5 ์žฅ. ์ข…ํ•ฉ ๋…ผ์˜ ๋ฐ ๊ฒฐ๋ก  176 ์ œ 1 ์ ˆ. ์—ฐ๊ตฌ ๋‚ด์šฉ ๋ฐ ๊ฒฐ๊ณผ ์š”์•ฝ 175 1. ์ •๋ณด ๋‹ค์–‘์„ฑ ์˜ํ–ฅ์„ ์ฃผ๋Š” ์š”์ธ 177 2. ์„ ํƒ์  ์ •๋ณด ์ด์šฉ์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ์š”์ธ 178 3. ํƒœ๋„ ๊ทน๋‹จ์„ฑ๊ณผ ๋ถ„๋…ธ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ 181 4. ์ •์น˜์  ์ฐธ์—ฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 181 ์ œ 2 ์ ˆ. ์—ฐ๊ตฌ์˜ ํ•จ์˜์™€ ํ•œ๊ณ„ 182 1. ์—ฐ๊ตฌ์˜ ํ•จ์˜ 182 2. ์—ฐ๊ตฌ์˜ ์ œํ•œ์ ๊ณผ ํ›„์† ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ์ œ์–ธ 190 ์ฐธ๊ณ ๋ฌธํ—Œ 194Docto

    ์žฅ๊ธฐ์š”์–‘ ํ•„์š”๋…ธ์ธ์˜ ์„œ๋น„์Šค ๋ฏธ์ด์šฉ ๊ด€๋ จ์š”์ธ ๋ฐ ์˜๋ฃŒ์ด์šฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋ณด๊ฑด๋Œ€ํ•™์› : ๋ณด๊ฑดํ•™๊ณผ ๋ณด๊ฑด์ •์ฑ…๊ด€๋ฆฌํ•™์ „๊ณต, 2016. 8. ๊น€ํ™์ˆ˜.๋ณธ ์—ฐ๊ตฌ๋Š” ์žฅ๊ธฐ์š”์–‘ ๋“ฑ๊ธ‰ํŒ์ •์„ ํ†ตํ•ด ์ˆ˜๊ธ‰์ž๊ฒฉ์„ ์ธ์ •๋ฐ›์€ ์žฅ๊ธฐ์š”์–‘ ํ•„์š”๋…ธ์ธ์˜ ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ๋ฏธ์ด์šฉ ๊ด€๋ จ์š”์ธ์„ ์•Œ์•„๋ณด๊ณ , ์„œ๋น„์Šค ๋ฏธ์ด์šฉ์ด ๋…ธ์ธ์˜ ์˜๋ฃŒ์ด์šฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜์˜€๋‹ค. ์ž๋ฃŒ์›์œผ๋กœ ๊ตญ๋ฏผ๊ฑด๊ฐ•๋ณดํ—˜๊ณต๋‹จ์˜ ๋…ธ์ธ์ฝ”ํ˜ธํŠธDB 2009๋…„๋ถ€ํ„ฐ 2013๋…„๊นŒ์ง€ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์˜€๊ณ , ์žฅ๊ธฐ์š”์–‘ ๋“ฑ๊ธ‰์„ ์ธ์ •๋ฐ›์€ 65์„ธ ์ด์ƒ ๋…ธ์ธ 27,141๋ช…์— ๋Œ€ํ•œ 73,652๊ฑด์˜ ๊ด€์ฐฐ์น˜๋ฅผ ๋ถ„์„๋Œ€์ƒ์œผ๋กœ ํ™œ์šฉํ•˜์˜€๋‹ค. ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ๋ฏธ์ด์šฉ ๊ด€๋ จ์š”์ธ์— ๋Œ€ํ•ด์„œ๋Š” ๋กœ์ง“ ํšŒ๊ท€๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ๋ฏธ์ด์šฉ์ด ์˜๋ฃŒ์ด์šฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•ด์„œ๋Š” two-part model์„ ์ ์šฉํ•˜์—ฌ ํ”„๋กœ๋น—๊ณผ OLS ํšŒ๊ท€๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ์ด ๋•Œ ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ๋ฏธ์ด์šฉ์˜ ๋‚ด์ƒ์„ฑ์„ ํ†ต์ œํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋„๊ตฌ๋ณ€์ˆ˜๋ฅผ ํ™œ์šฉํ•œ IVํ”„๋กœ๋น—๊ณผ 2SLS ๋ถ„์„๊ฒฐ๊ณผ๋ฅผ ํ•จ๊ป˜ ์ œ์‹œํ•˜์˜€๋‹ค. ์žฅ๊ธฐ์š”์–‘ ํ•„์š”๋…ธ์ธ์˜ ์„œ๋น„์Šค ๋ฏธ์ด์šฉ์— ๊ฐ€์žฅ ํฐ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์€ ์žฅ๊ธฐ์š”์–‘ ์ธ์ •์‹ ์ฒญ ํšŸ์ˆ˜์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๊ณ , ์ธ์ •์‹ ์ฒญ ํšŸ์ˆ˜๊ฐ€ ๋Š˜์–ด๋‚ ์ˆ˜๋ก ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค๋ฅผ ์ด์šฉํ•˜์ง€ ์•Š์„ ํ™•๋ฅ ์ด ๋†’์•„์กŒ๋‹ค. ์ธ์ •์‹ ์ฒญ ํšŸ์ˆ˜๊ฐ€ ๋งŽ๋‹ค๋Š” ๊ฒƒ์€ 1๋…„ ์ค‘ ์ˆ˜์ฐจ๋ก€ ๋“ฑ๊ธ‰ํŒ์ •์„ ์œ„ํ•œ ์ธ์ •์กฐ์‚ฌ๋ฅผ ์‹ ์ฒญํ–ˆ๋‹ค๋Š” ๊ฒƒ์ธ๋ฐ, ๊ทธ๋Ÿผ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋“ฑ๊ธ‰์„ ์ธ์ •๋ฐ›๊ณ ๋„ ์„œ๋น„์Šค๋ฅผ ์ด์šฉํ•˜์ง€ ์•Š์„ ํ™•๋ฅ ์€ ํ˜„๊ฒฉํ•˜๊ฒŒ ๋†’์•„์ง„ ๊ฒƒ์ด๋‹ค. ์ธ์ •์‹ ์ฒญ ํšŸ์ˆ˜์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ ํŒ์ •๋ฐ›์€ ๋“ฑ๊ธ‰์ด ๋‚ฎ์„์ˆ˜๋ก ์ธ์ •์‹ ์ฒญ ํšŸ์ˆ˜๊ฐ€ ๋Š˜์–ด๋‚˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๊ณ , ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ๋ฏธ์ด์šฉ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ ๋ถ„์„์—์„œ๋„ ๋‚ฎ์€ ํŒ์ •๋“ฑ๊ธ‰์ด ์„œ๋น„์Šค ๋ฏธ์ด์šฉ ํ™•๋ฅ ์„ ๋†’์ด๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๊ทผ๋ณธ์ ์œผ๋กœ ๋“ฑ๊ธ‰ํŒ์ • ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๋ถˆ๋งŒ์ด ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ๋ฏธ์ด์šฉ์˜ ์ฃผ๋œ ์›์ธ์œผ๋กœ ์ถ”์ธก๋˜๋Š” ๋ฐ”, ์ด๋“ค์˜ ๋ฏธ์ถฉ์กฑ ์š•๊ตฌ๋ฅผ ํŒŒ์•…ํ•˜์—ฌ ์ •์ฑ…์— ๋ฐ˜์˜ํ•˜๋Š” ๋…ธ๋ ฅ์ด ํ•„์š”ํ•  ๊ฒƒ์ด๋‹ค. ๊ทธ ๋ฐ–์— ์ž๋…€์™€ ๋™๊ฑฐํ•˜๊ฑฐ๋‚˜ ์ฃผ์ˆ˜๋ฐœ์ž๊ฐ€ ์กด์žฌํ•˜๋Š” ๊ฒฝ์šฐ, ์žํƒ์— ๊ฑฐ์ฃผํ•˜์ง€ ์•Š๋Š” ๊ฒฝ์šฐ์—๋„ ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ๋ฏธ์ด์šฉ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์•„์กŒ๋‹ค. ๊ฐ„ํ˜ธ์ฒ˜์น˜ ์˜์—ญ์—์„œ ์ œํ•œ์ •๋„๊ฐ€ ์‹ฌํ•˜๊ณ , ์˜ํ•™์  ์น˜๋ฃŒ๊ฐ€ ํ•„์š”ํ•˜๋‹ค๋Š” ์˜์‚ฌ์†Œ๊ฒฌ์ด ์žˆ๋Š” ๋…ธ์ธ์—์„œ๋„ ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ๋ฏธ์ด์šฉ ํ™•๋ฅ ์ด ๋‘๋“œ๋Ÿฌ์ง€๊ฒŒ ๋†’์•„์กŒ๋‹ค. ์ด๋Š” ๊ฐ„ํ˜ธ์ฒ˜์น˜๋‚˜ ์˜๋ฃŒ์  ๊ฐœ์ž…์„ ํ•„์š”๋กœ ํ•˜๋Š” ๋…ธ์ธ์—๊ฒŒ ์ ์ ˆํ•œ ์˜๋ฃŒ์„œ๋น„์Šค๊ฐ€ ์—ฐ๊ณ„๋˜์ง€ ์•Š๊ณ  ์žˆ์–ด์„œ ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ์ด์šฉ์ด ์ €ํ•ด๋˜๊ณ  ์žˆ์Œ์„ ๋ณด์—ฌ์ฃผ๋Š” ๊ฒฐ๊ณผ์ด๋‹ค. ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ๋ฏธ์ด์šฉ์ด ์˜๋ฃŒ์ด์šฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ, ๊ฐ™์€ ์กฐ๊ฑด์˜ ์žฅ๊ธฐ์š”์–‘ ํ•„์š”๋…ธ์ธ์ด ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค๋ฅผ ์ด์šฉํ•˜์ง€ ์•Š๋Š” ๊ฒฝ์šฐ ์˜๋ฃŒ์„œ๋น„์Šค ์ด์šฉ ๋ฐœ์ƒ ๋ฐ ์ด ์ง„๋ฃŒ๋น„, ์ž…์›์„œ๋น„์Šค ์ด์šฉ ๋ฐœ์ƒ ๋ฐ ์ž…์›์ผ์ˆ˜, ์š”์–‘๋ณ‘์› ์ด์šฉ ๋ฐœ์ƒ ๋ฐ ์š”์–‘๋ณ‘์› ์ž…์›์ผ์ˆ˜, ์‘๊ธ‰์‹ค ์ด์šฉ ๋ฐœ์ƒ ๋ฐ ์‘๊ธ‰์‹ค ๋‚ด์›์ผ์ˆ˜๊ฐ€ ๋ชจ๋‘ ์œ ์˜ํ•˜๊ฒŒ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ํ•œํŽธ ์™ธ๋ž˜์„œ๋น„์Šค ์ด์šฉ ๋ฐœ์ƒ ๋ฐ ์™ธ๋ž˜ ๋‚ด์›์ผ์ˆ˜์— ๋Œ€ํ•ด์„œ๋Š” ๋„๊ตฌ๋ณ€์ˆ˜๋ฅผ ํ™œ์šฉํ•˜์˜€์„ ๋•Œ์—๋งŒ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ์–‘์˜ ๋ฐฉํ–ฅ์œผ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์žฅ๊ธฐ์š”์–‘ ํ•„์š”๋…ธ์ธ์ด ๋ชจ๋‘ ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค๋ฅผ ์ด์šฉํ•œ๋‹ค๊ณ  ๊ฐ€์ •ํ•  ๋•Œ ์˜๋ฃŒ์ด์šฉ์— ๋ฐœ์ƒํ•˜๋Š” ํšจ๊ณผ๋ฅผ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ, ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ์ด์šฉ์„ ํ†ตํ•ด ์ด ์ง„๋ฃŒ๋น„, ์ž…์›์ผ์ˆ˜, ์™ธ๋ž˜ ๋‚ด์›์ผ์ˆ˜, ์š”์–‘๋ณ‘์› ์ž…์›์ผ์ˆ˜, ์‘๊ธ‰์‹ค ๋‚ด์›์ผ์ˆ˜์˜ ์ถ”๊ฐ€ ๋ฐœ์ƒ์„ ํฐ ํญ์œผ๋กœ ๊ฐ์†Œ์‹œํ‚ฌ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ์ด์šฉ๋น„์šฉ์˜ ํ•œ๊ณ„ํšจ๊ณผ ๋ถ„์„์—์„œ๋„ ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ์ด์šฉ์— ์˜๋ฃŒ์ด์šฉ์„ ๋Œ€์ฒดํ•˜๋Š” ํšจ๊ณผ๊ฐ€ ์žˆ์Œ์ด ํ™•์ธ๋˜์—ˆ์œผ๋ฉฐ, ํŠนํžˆ ์ด ์ง„๋ฃŒ๋น„์™€ ์ž…์›์„œ๋น„์Šค ๋ฐ ์š”์–‘๋ณ‘์› ์ด์šฉ ๋ถ€๋ฌธ์—์„œ ํšจ๊ณผ๊ฐ€ ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Š” ์žฅ๊ธฐ์š”์–‘ ํ•„์š”๋ฅผ ์ธ์ •๋ฐ›์€ ๋…ธ์ธ๋“ค์˜ ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ์ด์šฉ์„ ์žฅ๋ คํ•จ์œผ๋กœ์จ ๊ฑด๊ฐ•๋ณดํ—˜์— ๋ฐœ์ƒํ•˜๋Š” ๋…ธ์ธ์ธ๊ตฌ์˜ ์ถ”๊ฐ€์ ์ธ ์˜๋ฃŒ ์ˆ˜์š”๋ฅผ ์ƒ๋‹น ๋ถ€๋ถ„ ํก์ˆ˜ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ฃผ๋Š” ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ฒด๊ณ„์  ๊ด€๋ฆฌ๊ฐ€ ๋ถ€์žฌํ•ด์™”๋˜ ์žฅ๊ธฐ์š”์–‘ ํ•„์š”๋…ธ์ธ์˜ ๊ด€๋ จ ์„œ๋น„์Šค ๋ฏธ์ด์šฉ ์‚ฌ์œ ์— ๋Œ€ํ•œ ํŒŒ์•…์„ ์‹œ๋„ํ•˜๊ณ , ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ๋ฏธ์ด์šฉ์ด ๋…ธ์ธ์˜ ์˜๋ฃŒ์ด์šฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์‹ค์ฆ์ ์œผ๋กœ ํƒ์ƒ‰ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ, ๊ฐ„ํ˜ธ์ฒ˜์น˜๋‚˜ ์˜๋ฃŒ์„œ๋น„์Šค์— ๋Œ€ํ•œ ์ ๊ทน์ ์ธ ์—ฐ๊ณ„ ๊ด€๋ฆฌ ๋ถ€์žฌ๋Š” ์žฅ๊ธฐ์š”์–‘ ํ•„์š”๋…ธ์ธ์˜ ์„œ๋น„์Šค ์ด์šฉ์„ ์ €ํ•ดํ•˜๊ณ  ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ๊ณผ๋‹คํ•œ ์˜๋ฃŒ์ด์šฉ์„ ์ดˆ๋ž˜ํ•˜๋Š” ๋“ฑ ๋ถ€์ •์  ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ํŒŒ์•…๋˜์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ํ–ฅํ›„ ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ๋‚ด์‹คํ™” ์ •์ฑ…์€ ๋Œ€์ƒ ๋…ธ์ธ์ด ํ•„์š”๋กœ ํ•˜๋Š” ๊ฐ„ํ˜ธ์ฒ˜์น˜์™€ ์˜๋ฃŒ์  ๊ฐœ์ž…์„ ๋ณด์žฅ๋ฐ›์„ ์ˆ˜ ์žˆ๋„๋ก ์˜๋ฃŒ์„œ๋น„์Šค๋ฅผ ์ ๊ทน ์—ฐ๊ณ„ํ•˜๊ณ , ์ด์šฉ์„ ํ™œ์„ฑํ™”ํ•˜๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ์ถ”์ง„๋  ํ•„์š”์„ฑ์ด ์žˆ๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์  4 ์ œ 2 ์žฅ ์ด๋ก ์  ๊ณ ์ฐฐ 5 ์ œ 1 ์ ˆ ์žฅ๊ธฐ์š”์–‘ ๋Œ€์ƒ์ž 5 ์ œ 2 ์ ˆ ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ๋ฏธ์ด์šฉ ๊ด€๋ จ์š”์ธ 9 ์ œ 3 ์ ˆ ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ์ด์šฉ ๊ด€๋ จ์š”์ธ 11 ์ œ 4 ์ ˆ ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ์ด์šฉ์˜ ํšจ๊ณผ 15 ์ œ 3 ์žฅ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 21 ์ œ 1 ์ ˆ ์ž๋ฃŒ์› 21 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ๋Œ€์ƒ 22 ์ œ 3 ์ ˆ ๋ถ„์„๋ชจํ˜• ๋ฐ ๋ณ€์ˆ˜ 25 ์ œ 4 ์ ˆ ๋„๊ตฌ๋ณ€์ˆ˜ 32 ์ œ 5 ์ ˆ ๋ถ„์„๋ฐฉ๋ฒ• 34 ์ œ 4 ์žฅ ์—ฐ๊ตฌ๊ฒฐ๊ณผ 37 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋Œ€์ƒ์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ 37 ์ œ 2 ์ ˆ ์žฅ๊ธฐ์š”์–‘ ํ•„์š”๋…ธ์ธ์˜ ์„œ๋น„์Šค ๋ฏธ์ด์šฉ ๊ด€๋ จ์š”์ธ 45 ์ œ 3 ์ ˆ ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ๋ฏธ์ด์šฉ์ด ์˜๋ฃŒ์ด์šฉ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 52 ์ œ 4 ์ ˆ ์žฅ๊ธฐ์š”์–‘์„œ๋น„์Šค ์ด์šฉ์˜ ํšจ๊ณผ 64 ์ œ 5 ์žฅ ๊ณ ์ฐฐ ๋ฐ ๊ฒฐ๋ก  71 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๊ณ ์ฐฐ 71 ์ œ 2 ์ ˆ ๋ถ„์„๋ฐฉ๋ฒ• ๊ณ ์ฐฐ 81 ์ œ 3 ์ ˆ ๊ฒฐ๋ก  89 ์ฐธ๊ณ  ๋ฌธํ—Œ 92 Abstract 101Maste

    Popular Cultural Experiences and Affective Community Through Motion Picture(Film) Festivals in the Japanese Colonial Period

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    This paper examines how Motion Picture(film) festivals in the Japanese colonial period were expressions of 1920s colonial popular culture and their significance in creating collective public affect. Motion Picture(film) festivals (hwaldong sajin (yลnghwa) taehoe) were cultural events showcasing a series of film programs compiled for special purposes and screened in theaters, provincial meeting halls, or rural public facilities for free or cheap admission. These festivals were important as they were a key channel by which to experience colonial popular culture at all levels of administrative jurisdiction, from cities down to counties, towns, and villages. The hundreds of festivals held nationwide in the 1920s were either sponsored by the government or civilians, with the films seeking to enlighten the populace as propaganda along with lectures, fundraisers, and sales being held for promotion. These films were often accompanied by slide shows, performances, songs, theater, and sports, and the festivals themselves were parts of larger entertainment festivals or competitive exhibitions. They also were combined with government-run movements such as the Provincial Improvement Project or Rural Village Promotion Movement, conveying or justifying the methods of colonial rule in a region or inciting and awakening modern communal consciousness and affect in Korean society. The festivals created their own external narratives without regards to the narrative sintrinsic to their films, and the public would be exposed to a sensory experience of modernity and coloniality through participating in these narratives by attending them. The metaphors of comfort and sympathy advanced by the festivals also fostered a sense of community, simultaneously creating an affective community while nullifying the various conflicts within it. A vibrant 1920s created by the March First Movement and its aftereffect of cultural politics was perhaps no more than a brief respite between the violent realities of the 1910s and 1930s-40s
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