Text Monitoring with NLP for Covid Related Data

ABSTRACT

Since the adoption of the internet as a medium of communication of information, fake or false information or news has always been a major issue. Incidents of false information have always increased at times of crisis on national or international scales. The world witnessed a global pandemic from the Coronavirus, causing a complete disruption in the functioning of society. News of bogus cures, home remedies, and medicines started to make their way around the world. The number of incidents of such false news only increased as the pandemic worsened and more people were falling sick and dying. In times of desperation, people can easily be persuaded to try unverified and possibly dangerous medicines or cures, that can cost them their money as well as health. In this paper, natural language processing is used to first identify and differentiate text that has information regarding Covid 19 from the text that does not contain information regarding Covid 19. Word frequency scores like TF and IDF scores are then calculated. The intent of the text is then analyzed by observing the mannerisms detected in false news. With this analysis, the potential of the text to be false or fake is then determined. This research intends to explore the linguistics of false news and to get one step ahead in identifying fake news. The same methodology can be used to analyze data related to other specific topics. © 2022 IEEE.