| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000432223 |
| 005 | | 20200224115112 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085689571 |
| 035 | |
▼a (MiAaPQ)AAI13902914 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 020 |
| 100 | 1 |
▼a Nelakurthi, Arun Reddy. |
| 245 | 10 |
▼a Learning from Task Heterogeneity in Social Media. |
| 260 | |
▼a [S.l.]:
▼b Arizona State University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 147 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: A. |
| 500 | |
▼a Advisor: He, Jingrui. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Arizona State University, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0010. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Mass communications. |
| 650 | 4 |
▼a Information science. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0723 |
| 690 | |
▼a 0708 |
| 710 | 20 |
▼a Arizona State University.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04A. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0010 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15492410
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |