지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2025.1
- 수록면
- 193 - 214 (22page)
- DOI
- 10.51979/KSSLS.2025.01.99.193
이용수
초록· 키워드
오류제보하기
Purpose: This study aims to analyze the trends and changes in news coverage before, during, and after the Paris Olympics. It collects data from news articles and uses Latent Dirichlet Allocation (LDA) topic modeling along with social network analysis.
Method: This study aimed to compare and analyze shifts in coverage of the Paris Olympics by collecting news big data as the research subject. Big Kinds and Netminer 4.5.1 were employed as analytical tools. Big Kinds was used to collect time-specific original news data related to the Paris Olympics and to conduct topic modeling. Prior to deriving the topic modeling results, the optimal parameter values were calculated using the Coherence Score formula. Additionally, a social network analysis was performed to visualize the results of topic modeling.
Results: First, pre-Olympic news articles primarily focused on concerns regarding the limited participation and anticipation of events following the opening ceremony. Second, during the Olympics, the majority of news coverage centered on medal victories and competition results. Additionally, controversies such as conflicts between athletes and their associations as well as issues related to organizational missteps during the opening ceremony were noted. Third, post-Olympic articles emphasized the successful return of national athletes after winning numerous medals, their subsequent activities, and the involvement of sponsoring corporations.
Conclusion: Pre-Olympic news highlighted public indifference and raised concerns about the low participation of the national team. However, record-breaking performance during the games enhanced national pride and patriotism, particularly in events and among athletes supported by corporate sponsorship. Post-Olympic reports highlighted the economic and social impact of medalists, including enhanced corporate visibility and environmental initiatives, such as low-carbon, eco-friendly practices, which align with global Environmental, Social, and Governance (ESG) trends.
Method: This study aimed to compare and analyze shifts in coverage of the Paris Olympics by collecting news big data as the research subject. Big Kinds and Netminer 4.5.1 were employed as analytical tools. Big Kinds was used to collect time-specific original news data related to the Paris Olympics and to conduct topic modeling. Prior to deriving the topic modeling results, the optimal parameter values were calculated using the Coherence Score formula. Additionally, a social network analysis was performed to visualize the results of topic modeling.
Results: First, pre-Olympic news articles primarily focused on concerns regarding the limited participation and anticipation of events following the opening ceremony. Second, during the Olympics, the majority of news coverage centered on medal victories and competition results. Additionally, controversies such as conflicts between athletes and their associations as well as issues related to organizational missteps during the opening ceremony were noted. Third, post-Olympic articles emphasized the successful return of national athletes after winning numerous medals, their subsequent activities, and the involvement of sponsoring corporations.
Conclusion: Pre-Olympic news highlighted public indifference and raised concerns about the low participation of the national team. However, record-breaking performance during the games enhanced national pride and patriotism, particularly in events and among athletes supported by corporate sponsorship. Post-Olympic reports highlighted the economic and social impact of medalists, including enhanced corporate visibility and environmental initiatives, such as low-carbon, eco-friendly practices, which align with global Environmental, Social, and Governance (ESG) trends.
목차
Ⅰ. 서론
Ⅱ. 연구방법
Ⅲ. 결과
Ⅳ. 논의
Ⅴ. 결론 및 제언
참고문헌
ABSTRACT
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