Relationship between emotions and emoticons in adolescents in digital communication environment
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Purpose: Adolescents use emoticons to express their emotions in an online environment. Hence, medical experts can understand the emotions of adolescents by emoticons. The goal of this study was to investigate the relationship between various emotions and emoticons among the Korean adolescents. Methods: The questionnaire survey was conducted between September 1 and 30, 2014, involving 3,272 students in elementary schools, middle schools, and high schools affiliated in the Department of Education of the metropolitan city of Busan. A total of 1,717 students responded to the survey. The participants consisted of 806 males (46.9%), and 911 females (53.1%). Among these, there were 557 elementary school students (32.4%), 617 middle school students (35.9%), and 543 high school students (31.6%). A social networking analysis was conducted using NodeXL. Results: The frequency of emoticon use among adolescents runs in the order of joy, sadness, fear, surprise, anger, disgust, and then depression. Elementary school females mainly use emoticons to express joy; middle school females use emoticons to express sadness, surprise, anger, disgust, and depression; and high school females use emoticons to express fear. Age- and gender-specific emoticon networks were visualized by using the Haren-Korel fast multiscale algorithm. Commonly used emoticons by age and gender were expressed in the networks. Results of age- and gender-specific emoticon networks visualization show similar results of centrality of seven emoticons. Conclusion: In the digital communication environment, emoticons could be used to catch the emotions of adolescents in Korea.
Abstract
Purpose: Adolescents use emoticons to express their emotions in an online environment. Hence, medical experts can understand the emotions of adolescents by emoticons. The goal of this study was to investigate the relationship between various emotions and emoticons among the Korean adolescents. Methods: The questionnaire survey was conducted between September 1 and 30, 2014, involving 3,272 students in elementary schools, middle schools, and high schools affiliated in the Department of Education of the metropolitan city of Busan. A total of 1,717 students responded to the survey. The participants consisted of 806 males (46.9%), and 911 females (53.1%). Among these, there were 557 elementary school students (32.4%), 617 middle school students (35.9%), and 543 high school students (31.6%). A social networking analysis was conducted using NodeXL. Results: The frequency of emoticon use among adolescents runs in the order of joy, sadness, fear, surprise, anger, disgust, and then depression. Elementary school females mainly use emoticons to express joy; middle school females use emoticons to express sadness, surprise, anger, disgust, and depression; and high school females use emoticons to express fear. Age- and gender-specific emoticon networks were visualized by using the Haren-Korel fast multiscale algorithm. Commonly used emoticons by age and gender were expressed in the networks. Results of age- and gender-specific emoticon networks visualization show similar results of centrality of seven emoticons. Conclusion: In the digital communication environment, emoticons could be used to catch the emotions of adolescents in Korea.
참고문헌 (32)
- 2016 Survey on the Internet Usage, Final Report, "Korea Internet & Security Agency", Korea Internet & Security Agency, ~ (2016)
- Tapscott D., "Educating the net generation", Educational leadership, 56(5), pp.6~11 (1999)
- Rno YO., "Youth culture" (2005), Publication GOOSAN
- Lee CH, "Change of youth generation concept: In the new generation up to 2.0 generation", National Youth Policy Institute, 2, pp.1~12 (2008)
- 조규명, "디지털 커뮤니케이션 환경에서 감성기호로서 이모티콘에 관한 연구", 디자인학연구, 17(1) (2004), 한국디자인학회
- Rezabek L, "Visual cues in computer-mediated communication: Supplementing text with emoticons", Journal of Visual Literacy, 18(2), pp.201~215 (1998)
- 박한우, "LexiURL을 이용한 동시링크 분석-정치웹진ㆍ정치포럼 사이트", Journal of The Korean Data Analysis Society, 10(2), pp.1089~1101 (2008), 한국자료분석학회
- Garton L, "Studying online social networks", Journal of Computer‐ Mediated Communication, 3(1) (1997)
- Berkowitz SD., "An introduction to structural analysis: The network approach to social research", pp.1~244 (2013), Elsevier
- 이원태, "소셜미디어 유력자의 네트워크 특성: 한국의 트위터 공동체를 중심으로", 언론정보연구, 48(2), pp.44~79 (2011), 언론정보연구소
- 박지원, "페이스북 팬페이지의 동시댓글 데이터를 이용한 네트워크 분석: 대구·경북 유력 후보자를 중심으로", Journal of The Korean Data Analysis Society, 16(6), pp.3243~3253 (2014), 한국자료분석학회
- Hu X, "Unsupervised sentiment analysis with emotional signals", Proceedings of the 22nd international conference on World Wide Web, pp.607~618 (2013)
- 이창균, "e-커머스 기업의 고객서비스 쿨트랜드 발견: 사회네트워크분석 NodeXL 활용", Information Systems Review, 13(1), pp.75~96 (2011), 한국경영정보학회
- 김지영, "영남지역 언론사의 온라인 사회자본 분석 : 웹사이트와 소셜미디어를 중심으로", 한국콘텐츠학회 논문지, 13(4), pp.73~85 (2013), 한국콘텐츠학회[DOI]
- Park J, "Cross‐cultural comparison of nonverbal cues in emoticons on twitter: Evidence from big data analysis", Journal of Communication, 64(2), pp.333~354 (2014)
- Ramage D, "Characterizing microblogs with topic models", ICWSM, 10, pp.130~137 (2010)
- Zhao J, "Moodlens: an emoticon-based sentiment analysis system for chinese tweets", Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.1528~1531 (2012)
- Castillo C, "Information credibility on twitter", Proceedings of the 20th international conference on World wide web, pp.675~684 (2011)
- Kouloumpis E, "Twitter sentiment analysis: The good the bad and the omg!", ICWSM, 11, pp.538~541 (2011)
- Plutchik R., "The Nature of Emotions Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice", American scientist, 89(4), pp.344~350 (2001)
- Goleman D., "Emotional development and emotional intelligence" (1997), Basic Books
- Hansen D, "Analyzing social media networks with NodeXL: Insights from a connected world", pp.1~304 (2010), Morgan Kaufmann
- Smith MA, "Simple network analysis for social media. Encyclopedia of Social Network Analysis and Mining", pp.115~1170 (2014), Springer
- Streeter CL, "Social network analysis", Journal of Social Service Research, 16, pp.201~222 (1993)
- Wasserman S, "Social network analysis: Methods and applications", pp.1~857 (1994), Cambridge university press
- Newman ME, "The mathematics of networks", The new palgrave encyclopedia of economics, 2, pp.1~12 (2008)
- Borgatti SP, "Centrality and AIDS", Connections, 18(1), pp.112~114 (1995)
- Freeman LC, "Centrality in social networks conceptual clarification", Social networks, 1(3), pp.215~239 (1978)
- Brandes U, "A faster algorithm for betweenness centrality", Journal of mathematical sociology, 25(2), pp.163~177 (2001)
- Sabidussi G, "The centrality index of a graph", Psychometrika, 31(4), pp.581~603 (1966)
- Brandes U, "Communicating centrality in policy network drawings", IEEE transactions on visualization and computer graphics, 9(2), pp.241~253 (2003)
- 진위, "오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례", 지능정보연구, 20(1), pp.49~65 (2014), 한국지능정보시스템학회[DOI]