Monitoring Indonesian online news for COVID-19 event detection using deep learning
International Journal of Electrical and Computer Engineering
; 13(1):957-971, 2023.
Article
in English
| ProQuest Central | ID: covidwho-2234587
ABSTRACT
Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy 95.16%, precision 94.71%, recall 94.32%, F1-score 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners.
Engineering--Electrical Engineering; BERT; Coronavirus disease 2019; Deep learning; Event detection; News monitoring; Online news; System framework; Infectious diseases; Classification; Epidemics; News; Impact analysis; Viral diseases; Machine learning; Mutation; Coders; Coronaviruses; Surveillance systems; Economic impact; COVID-19; United Kingdom--UK; Indonesia
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Prognostic study
Topics:
Vaccines
/
Variants
Language:
English
Journal:
International Journal of Electrical and Computer Engineering
Year:
2023
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS