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Application of ChatGPT text extraction model in analyzing rhetorical principles of COVID-19 pandemic information on a question-and-answer community

첫 페이지 보기
  • 발행기관
    국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
  • 간행물
    The International Journal of Advanced Smart Convergence 바로가기
  • 통권
    Volume 13 Number 2 (2024.06)바로가기
  • 페이지
    pp.205-213
  • 저자
    Hyunwoo Moon, Beom Jun Bae, Sangwon Bae
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A452344

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원문정보

초록

영어
This study uses a large language model (LLM) to identify Aristotle's rhetorical principles (ethos, pathos, and logos) in COVID-19 information on Naver Knowledge-iN, South Korea's leading question-and-answer community. The research analyzed the differences of these rhetorical elements in the most upvoted answers with random answers. A total of 193 answer pairs were randomly selected, with 135 pairs for training and 58 for testing. These answers were then coded in line with the rhetorical principles to refine GPT 3.5-based models. The models achieved F1 scores of .88 (ethos), .81 (pathos), and .69 (logos). Subsequent analysis of 128 new answer pairs revealed that logos, particularly factual information and logical reasoning, was more frequently used in the most upvoted answers than the random answers, whereas there were no differences in ethos and pathos between the answer groups. The results suggest that health information consumers value information including logos while ethos and pathos were not associated with consumers’ preference for health information. By utilizing an LLM for the analysis of persuasive content, which has been typically conducted manually with much labor and time, this study not only demonstrates the feasibility of using an LLM for latent content but also contributes to expanding the horizon in the field of AI text extraction.

목차

Abstract
1. INTRODUCTION
2. THEOREICAL FRAMEWORK AND HYPOTHESES
3. METHOD
4. RESULTS
5. DISCUSSION
6. CONCLUSION
REFERENCES

키워드

Artificial Intelligence Machine Learning Aristotle’s Rhetoric ChatGPT Persuasion Question and Answer Community COVID-19

저자

  • Hyunwoo Moon [ Doctoral Student, Department of MetaBioHealth Sungkyunkwan University, Seoul, Republic of Korea ]
  • Beom Jun Bae [ Associate Professor, Ph.D. Department of Communication Arts Georgia Southern University Statesboro, GA, USA ] Corresponding Author
  • Sangwon Bae [ B.B.A., Terry College of Business University of Georgia Athens, GA, USA ]

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    국제인공지능학회(구 한국인터넷방송통신학회) [The International Association for Artificial Intelligence]
  • 설립연도
    2000
  • 분야
    공학>전자/정보통신공학
  • 소개
    인터넷방송, 인터넷 TV , 방송 통신 네트워크 및 관련 분야에 대한 국내는 물론 국제적인 학술, 기술의 진흥발전에 공헌하고 지식 정보화 사회에 기여하고자 한다.

간행물

  • 간행물명
    The International Journal of Advanced Smart Convergence
  • 간기
    계간
  • pISSN
    2288-2847
  • eISSN
    2288-2855
  • 수록기간
    2012~2025
  • 십진분류
    KDC 326 DDC 380

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