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A Biased Topic Modeling Approach for Case Control Study from Health Related Social Media Postings

Abstract Online social networks are the hubs of social activity in cyberspace, and using them to exchange knowledge, experiences, and opinions is common. In this work, an advanced topic modeling framework is designed to analyse complex longitudinal health information from social media with minimal human annotation, and Adverse Drug Events and Reaction (ADR) information is extracted and automatically processed by using a biased topic modeling method. This framework improves and extends existing topic modelling algorithms that incorporate background knowledge. Using this approach, background knowledge such as ADR terms and other biomedical knowledge can be incorporated during the text mining process, with scores which indicate the presence of ADR bein... (more)
Created Date 2017
Contributor Yang, Jian (Author) / Gonzalez, Graciela (Advisor) / Davulcu, Hasan (Advisor) / Liu, Huan (Committee member) / Papotti, Paolo (Committee member) / Arizona State University (Publisher)
Subject Computer science / Biomedical engineering
Type Doctoral Dissertation
Extent 100 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Computer Science 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis