A Hierarchical Learning Model for Extracting Public Health Data from Social Media

In decision-making processes, particularly in the healthcare domain, each relevant piece of information is important. This is particularly important when it comes to the health conditions for them there remains a high degree of non-determinism regarding treatment approaches. 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 perceived the efficacy of 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 healthcare 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. Back pain was selected for our case study to demonstrate how the proposed model works.