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Learning from Task Heterogeneity in Social Media

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서명/저자사항Learning from Task Heterogeneity in Social Media.
개인저자Nelakurthi, Arun Reddy.
단체저자명Arizona State University. Computer Science.
발행사항[S.l.]: Arizona State University., 2019.
발행사항Ann Arbor: ProQuest Dissertations & Theses, 2019.
형태사항147 p.
기본자료 저록Dissertations Abstracts International 81-04A.
Dissertation Abstract International
ISBN9781085689571
학위논문주기Thesis (Ph.D.)--Arizona State University, 2019.
일반주기 Source: Dissertations Abstracts International, Volume: 81-04, Section: A.
Advisor: He, Jingrui.
이용제한사항This item must not be sold to any third party vendors.
요약In 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
일반주제명Computer science.
Mass communications.
Information science.
언어영어
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