Mideth Abisado
ABSTRACT
Early detection of diseases is crucial to control and prevent the emergence of infectious diseases. The disease monitoring and tracking of digital public health refer to social media data. The proposed study pulls data from a Twitter user who posts on various topics. The social media data will be continuously collected and performed computing analysis and interpretation. The results and assessment refer to the trends in emerging infectious diseases disseminated to those who have the right to know to act. This project aims to detect trends in social media posts on emerging infectious diseases in the Philippines, using the low-resourced languages Filipino and Cebuano, to understand the context of a social media post. Machine learning and natural language processing principles shall be used in the study. The insights from this study's dashboard can assist health professionals, officials, and the public have informed decisions and policies for better public health services for all Filipinos. It is well anticipated that policies based on the dashboard analytics will help prevent the surge or emergence of an outbreak or pandemic and, therefore, decrease the risk of economic loss. Thus, this ongoing research can result in fewer social and economic disruptions.
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Index Terms
- Using Low-Resourced Language in Social Media Platforms Towards Disease Surveillance for Public Health Monitoring using Artificial Intelligence
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