소셜미디어 상에서의 통역사에 대한 인식과 통역 콘텐츠 분석: 유튜브(Youtube)의 비정형 데이터를 이용한 텍스트 마이닝 분석과 질적 사례연구
Understanding perception and representation of interpreters on YouTube: Combining text mining and qualitative case study
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Social media has become an important online field in which professional identity construction occurs(Gal et al., 2016). This study aims to understand how interpreters are perceived and represented on the video-sharing website YouTube (www.youtube.com). Using Selenium, a Python library, the title and description of all 298 videos searched with the keyword “tongyeoksa”(meaning “interpreter” in Korean) were scraped. Keyword analysis, co-occurrence network analysis and LDA topic modeling analysis were employed to derive insights from the scraped data set. A qualitative content analysis was performed to complement the result of analysis. Through the analysis, the study reveals that the main topics of ‘interpreter-related’ YouTube videos are “Occupation and English Learning”, “Interpreters under the Media Spotlight”, “Sign Language Interpretation and Certificate”, “Female Sports Interpreter and Appearance”, and “Lectures and Awareness-raising”. Perception of interpreters in general showed an association with ‘foreign language skills’, ‘foreign language learning’, and ‘the growing popularity of Korean cultural content overseas’. On the other hand, ‘certificate’ and ‘awareness-raising about human rights of the socially-underprivileged’ appeared as main topics in the online discourse of sign language interpreters while ‘femininity’ and ‘appearance’ were highlighted in sports interpreters.
Abstract
Social media has become an important online field in which professional identity construction occurs(Gal et al., 2016). This study aims to understand how interpreters are perceived and represented on the video-sharing website YouTube (www.youtube.com). Using Selenium, a Python library, the title and description of all 298 videos searched with the keyword “tongyeoksa”(meaning “interpreter” in Korean) were scraped. Keyword analysis, co-occurrence network analysis and LDA topic modeling analysis were employed to derive insights from the scraped data set. A qualitative content analysis was performed to complement the result of analysis. Through the analysis, the study reveals that the main topics of ‘interpreter-related’ YouTube videos are “Occupation and English Learning”, “Interpreters under the Media Spotlight”, “Sign Language Interpretation and Certificate”, “Female Sports Interpreter and Appearance”, and “Lectures and Awareness-raising”. Perception of interpreters in general showed an association with ‘foreign language skills’, ‘foreign language learning’, and ‘the growing popularity of Korean cultural content overseas’. On the other hand, ‘certificate’ and ‘awareness-raising about human rights of the socially-underprivileged’ appeared as main topics in the online discourse of sign language interpreters while ‘femininity’ and ‘appearance’ were highlighted in sports interpreters.
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