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
- 학위논문
- 저자정보
- 지도교수
- 최재원
- 발행연도
- 2019
- 저작권
- 순천향대학교 논문은 저작권에 의해 보호받습니다.
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초록· 키워드
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In the crowdfunding market, many number of crowdfunding platforms can offer founders the possibilities to collect funding and launch someone’s next campaign, project or events that need. Especially, medical crowdfunding is a fast-growing practice in, which based on online platforms are used to raise money for health- related issues and needs. One of the largest platforms, GoFundMe, has raised US$ 5 billion since 2010 and is currently rising in 2019. Unfortunately, while providing crucial help to care for many people, it is also increasing risk of fraud.
Using the largest platform of crowdfunding market, GoFundMe, we conduct an exhaustive search of detection on fraud from October 2016 to September 2019. Data sets are based on 6 main types of medical focused crowdfunding campaigns or events, such as cancer, in vitro fertilization (ivf), leukemia, health insurance, lymphoma and also, surgery type.
This thesis study will close to research gap by arranging and evaluating a detect of fraud process to identify fraud from non-fraud medical crowdfunding campaigns.
Using the largest platform of crowdfunding market, GoFundMe, we conduct an exhaustive search of detection on fraud from October 2016 to September 2019. Data sets are based on 6 main types of medical focused crowdfunding campaigns or events, such as cancer, in vitro fertilization (ivf), leukemia, health insurance, lymphoma and also, surgery type.
This thesis study will close to research gap by arranging and evaluating a detect of fraud process to identify fraud from non-fraud medical crowdfunding campaigns.
목차
Table of Contents ⅰList of Tables ⅱList of Figures ⅲList of Pictures ⅳChapter 1- Introduction 11-1 Introduction 11-2 Research aims and Research questions 4Chapter 2- Literature Background 52-1 Crowdfunding Market 52-2 Medical Crowdfunding 112-3 Fraud Detection (Fraud Detection Theories) 15Chapter 3- Research Framework 28Chapter 4- Research Methodology 304-1 Latent Dirichlet Allocation (LDA) 304-2 Collaborative Filtering (CF) 344-3 Data Collection 394-4 Data analysis 414-4.1 Study 1 Result 444-4.2 Study 2 Result 51Chapter 5- Conclusion 575-1 Discussion 575-2 Theoretical and Practical Implication 585-3 Future work 58Reference 59Acknowledgement 67
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