Advisor Information
Hesham Ali
Location
UNO Criss Library, Room 231
Presentation Type
Oral Presentation
Start Date
3-3-2017 1:15 PM
End Date
3-3-2017 1:30 PM
Abstract
In decision making processes, particularly when it comes to health-related decisions, each relevant piece of information is important. This is particularly important when it comes to health conditions for which there remains a high degree of non-determinism as far as diagnosis and treatment. Online social media, are places in which people feel free to share their opinions about numerous topics, including public health issues and how individuals have responded to different types of treatments associated with diseases. social media could represent a secondary source that can be used as a supplement to other data sources. This would allow individuals as well as health care providers to gain insight related to public health from different angels. In this study, we construct a hierarchical learning model based on Twitter data that can extract valuable knowledge associated with public health concerns from Twitter network. Back pain was selected for our case study to demonstrate how the proposed model works.
A Hierarchical Learning Model for Extracting Public Health Data from Social Media
UNO Criss Library, Room 231
In decision making processes, particularly when it comes to health-related decisions, each relevant piece of information is important. This is particularly important when it comes to health conditions for which there remains a high degree of non-determinism as far as diagnosis and treatment. Online social media, are places in which people feel free to share their opinions about numerous topics, including public health issues and how individuals have responded to different types of treatments associated with diseases. social media could represent a secondary source that can be used as a supplement to other data sources. This would allow individuals as well as health care providers to gain insight related to public health from different angels. In this study, we construct a hierarchical learning model based on Twitter data that can extract valuable knowledge associated with public health concerns from Twitter network. Back pain was selected for our case study to demonstrate how the proposed model works.