자료유형 | 학위논문 |
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서명/저자사항 | Learning from Task Heterogeneity in Social Media. |
개인저자 | Nelakurthi, Arun Reddy. |
단체저자명 | Arizona State University. Computer Science. |
발행사항 | [S.l.]: Arizona State University., 2019. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
형태사항 | 147 p. |
기본자료 저록 | Dissertations Abstracts International 81-04A. Dissertation Abstract International |
ISBN | 9781085689571 |
학위논문주기 | Thesis (Ph.D.)--Arizona State University, 2019. |
일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: A.
Advisor: He, Jingrui. |
이용제한사항 | This item must not be sold to any third party vendors. |
요약 | In recent years, the rise in social media usage both vertically in terms of the number of users by platform and horizontally in terms of the number of platforms per user has led to data explosion.User-generated social media content provides an excellent opportunity to mine data of interest and to build resourceful applications. The rise in the number of healthcare-related social media platforms and the volume of healthcare knowledge available online in the last decade has resulted in increased social media usage for personal healthcare. In the United States, nearly ninety percent of adults, in the age group 50-75, have used social media to seek and share health information. Motivated by the growth of social media usage, this thesis focuses on healthcare-related applications, study various challenges posed by social media data, and address them through novel and effective machine learning algorithms.The major challenges for effectively and efficiently mining social media data to build functional applications include: (1) Data reliability and acceptance: most social media data (especially in the context of healthcare-related social media) is not regulated and little has been studied on the benefits of healthcare-specific social media |
일반주제명 | Computer science. Mass communications. Information science. |
언어 | 영어 |
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