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Learning from Task Heterogeneity in Social Media

Abstract 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 focuse... (more)
Created Date 2019
Contributor Nelakurthi, Arun Reddy (Author) / He, Jingrui (Advisor) / Cook, Curtiss B (Committee member) / Maciejewski, Ross (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Subject Computer science / data heterogeneity / domain adaptation / similar users / task heterogeneity / transfer learning / user modeling
Type Doctoral Dissertation
Extent 146 pages
Language English
Note Doctoral Dissertation Computer Science 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis