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COVID-19 and Media datasets: Period- and location-specific textual data mining
1CIRAD, UMR TETIS, F-34398 Montpellier, France.
Data in Brief
|October 5, 2020
Summary
News vocabulary for COVID-19 evolves over time. This study analyzes media datasets to track terminology changes and predict time periods using machine learning, offering insights into disease communication dynamics.
Area of Science:
- Computational linguistics
- Medical informatics
- Public health communication
Background:
- Disease-related news vocabulary is dynamic and context-dependent.
- Understanding linguistic shifts in health reporting is crucial for public health.
- Previous research has not fully explored temporal vocabulary changes in COVID-19 news.
Purpose of the Study:
- To analyze the evolution of terminology used in COVID-19 news over time.
- To develop machine learning models for predicting the time period of news articles based on their content.
- To provide insights into the changing public discourse surrounding the COVID-19 pandemic.
Main Methods:
- Utilized MEDISYS-sourced media datasets for analysis.
- Employed terminology extraction techniques to identify key terms.
- Applied machine learning approaches for period prediction based on textual content.
Main Results:
- Demonstrated significant changes in COVID-19 related vocabulary across different time periods.
- Successfully developed models capable of predicting the time period of news articles with high accuracy.
- Identified specific linguistic trends and shifts in reporting.
Conclusions:
- The vocabulary in COVID-19 news is not static and reflects evolving understanding and public attention.
- Machine learning offers effective methods for analyzing temporal linguistic patterns in health news.
- Findings contribute to a better understanding of health communication during pandemics.
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