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A graph-based approach for population health analysis using Geo-tagged tweets
Hung Nguyen1, Thin Nguyen2, Duc Thanh Nguyen3
1Faculty of IT, Nha Trang University, Nha Trang, Vietnam.
Multimedia Tools and Applications
|November 2, 2020
Summary
This study introduces a novel graph-based method for public health analysis using social media data. The approach effectively models social media interactions to estimate health indices and classify county health situations, outperforming existing methods.
Area of Science:
- Computational social science
- Public health informatics
- Network analysis
Background:
- Social media platforms generate vast amounts of data relevant to public health.
- Traditional public health analysis methods may not fully capture the dynamic nature of population health trends reflected in social media.
- Modeling interactions within social media is crucial for accurate public health insights.
Purpose of the Study:
- To propose and evaluate a graph-based approach for automatic public health analysis using social media data.
- To investigate the effectiveness of different graph properties and construction methods for population health analysis.
- To demonstrate the application of the approach in estimating health indices and classifying county-level health situations.
Main Methods:
- Constructing graphs to represent interactions between features and tweets in social media.
- Investigating various graph properties and construction methods for representation.
- Applying the graph-based approach to two case studies: health index estimation and US county health situation classification.
- Evaluating the approach on a large dataset of over one billion tweets (2014-2016) and Behavioral Risk Factor Surveillance System data.
Main Results:
- The proposed graph-based approach demonstrated robustness and superiority over existing methods in both case studies.
- Experimental results validated the effectiveness of modeling social media interactions for population health analysis.
- The approach successfully estimated health indices and classified health situations of US counties.
Conclusions:
- Graph-based modeling of social media interactions offers a powerful tool for automatic public health analysis.
- The proposed method provides a scalable and effective solution for leveraging social media data in public health.
- This approach holds significant potential for advancing population health monitoring and surveillance.
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