Published Paper Details:

The global community has been inundated with reports of the COVID-19 epidemic thanks to social media. Over time, there was a deluge of COVID-19-related messages, upgrades, video, and postings, much like the actual epidemic. In besides the health danger that COVID-19 posed, widespread panic ensued. Unsurprisingly, widespread fear spread owing to misunderstandings, a lack of knowledge, and even deliberate disinformation concerning the nature and effects of COVID-19. Ex post facto analysis of the first information flows on social media during the epidemic, in addition to a test case of growth of public sentiment on social networks, is therefore topical and essential. This research hopes to inform legislation that may be implemented on social media platforms, such as figuring out how much moderation is required to reduce disinformation. This investigation also examines the perspectives of Twitter users in regards to COVID-19. We provide a big new sentiment data set called COVIDSENTI, which comprises of 90,000 tweets gathered in the early phases of epidemic, during February to March of 2020, and provides a foundation for our research. A favorable, negative, and neutral category has been assigned to each tweet. Using many feature and classifier sets, we investigated the twitter data for sentiment classification. Public opinion was heavily conditioned by negative commentary; for example, we found that individuals approved lockdown measures at the outset of the epidemic but, as predicted, public opinion switched by mid-March. The results of our research lend credence to the idea that public health agencies, in the wake of a pandemic, need to have a more proactive and nimble online presence to counter spreading of defeatist effect