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Automatic Annotation of Disposition Counts in News Articles
1Digital Technologies, National Research Council Canada.
Studies in Health Technology and Informatics
|May 17, 2025
Summary
This study introduces a method to automatically extract crisis disposition counts from news reports. This supports timely and trustworthy public health data, like epidemic curves.
Area of Science:
- Public Health Informatics
- Natural Language Processing
- Epidemiology
Background:
- News media frequently report disposition counts (affected, deceased, recovered) during public health crises.
- These counts are crucial for understanding crisis impact and informing public health responses.
- Timely and trustworthy data aggregation is essential during emergencies.
Purpose of the Study:
- To develop and evaluate a method for automatically identifying and extracting disposition counts from unstructured news text.
- To support downstream applications, such as the automated generation of epidemic curves.
Main Methods:
- Utilized natural language processing (NLP) techniques to parse news articles.
- Developed algorithms to identify and quantify disposition counts (affected, deceased, recovered).
- Evaluated the accuracy and efficiency of the proposed method.
Main Results:
- The method successfully identifies disposition counts in news media reports.
- Demonstrated the feasibility of automated data extraction for public health surveillance.
- The system supports the creation of epidemic curves from unstructured text.
Conclusions:
- Automated extraction of disposition counts from news media is a viable approach.
- This method enhances the timeliness and trustworthiness of crisis data.
- Facilitates improved public health situational awareness and response.
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