Infodemic Management using Natural Language Processing: A COVID-19 Case Study

Poster Number: P157
Presentation Time: 05:00 PM - 06:30 PM

Abstract Keywords: Natural Language Processing, Infectious Diseases and Epidemiology, Data Mining
Working Group: Knowledge Discovery and Data Mining Working Group
Primary Track: Applications
Programmatic Theme: Public Health Informatics

Considering the lingering consequences of Coronavirus disease 2019 (COVID-19), the world needs to
be better equipped with infodemic management technologies in case of a future outbreak. One of the major bottle-
necks in the management of future infodemics is the lack of verified information with balanced class annotations. This is likely to be further amplified by a lack of understanding of the evolution of disease-related topics over time.The current era of global change has been associated with growth in both emerging and re-emerging infectious diseases. Consequently, researchers need to be capable of detecting and disseminating verified information related to such diseases. Resurgence waves of COVID-19 cases have been observed worldwide since 20201. During this unprecedented crisis, people have increasingly relied on online information sources for dealing with the pandemic. A real-time assessment of X (formerly known as Twitter) discussions can be useful for timely addressing of public health emergency responses. In this direction, we sought to use natural language processing (NLP) techniques to classify information available from X related to the surveillance and prevention of COVID-19

Speaker(s):
Reshma Kar, PhD
Saint Peter’s University