ISSN (Online) : 2456 - 0774

Email : ijasret@gmail.com

ISSN (Online) 2456 - 0774

THE COVID-19 PANDEMIC INTELLIGENCE PROVIDED VIA TWITTER AND ITS DEPENDABILITY

Abstract

Abstract: It has become beneficial for Twitter and other social networks (ONS) to disseminate information. However, they have become the breeder for false information, especially with the 2019 pandemic of the continued coronavirus (COVID-19). The hazards posed by these COVID-19 approaches are better defined as infodemic, and scientific evidence and sentiment classification are more important than ever. The reliability of Twitter intelligence about the COVID-19 pandemics is explored in this article. Based on our results on many tweets, we suggest an ensemble-learning method for searching validity. We study, in particular, an extensive dataset of COVID-19-related tweets. We divide information into two categories in our approach: positive and negative. For our Tweet reputation scores, various variables like tweets and user expectations are used. On the obtained and labelled dataset, we conducted multiple experiments. The results show a bigdifference between credible and unbelievable tweets containing the knowledge COVID-19 is drawn between the proposed systems. Keywords: COVID-19, Misinformation, Machine Learning, Social Media, Twitter, Fake News

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