The goal of this project is to explore popular discourse about the COVID-19 pandemic and policies implemented to manage it. The author explains how identification of common responses to the pandemic was done using Natural Language Processing (NLP), Text Mining, and Network Analysis using corpus of tweets that relate to the COVID-19 pandemic.
The paper also explains how information and misinformation were transmitted via Twitter, starting at the early stages of this pandemic. Finally this project introduces a dataset of tweets collected from all over the world, in multiple languages, dating back to January 22nd, when the total cases of reported COVID-19 were below 600 worldwide
The insights presented in this project can be very useful for data analysts and decision makers in the face of future pandemics.
The dataset repository contains an ongoing collection of tweet-IDs associated with the novel coronavirus (COVID-19).
Github Dataset: https://github.com/lopezbec/COVID19_Tweets_Dataset