1 Department of Computational Science, University of Texas at El Paso, US.
2 Department of Electrical Engineering, San Francisco Bay University, Fremont, CA 94539, US.
3 Department of Mathematical Science, University of Texas at El Paso, US
4 Department of Mechanical & Aerospace Engineering, University of Texas at El Paso, US.
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(01), 836-842
Received on 02 December 2023; revised on 07 January 2024; accepted on 10 January 2024
In this paper, we delve into the public discourse surrounding COVID-19 on Twitter to unearth the collective sentiments, concerns, and spread of information during the pandemic. By leveraging a dataset of relevant tweets and corresponding ISO country codes, our analysis will map out the geographical and digital landscape of these conversations. The significance of this work lies in its potential to inform public health strategies, shape policymaking, and contribute to social research on crisis communication. Stakeholders ranging from health officials to the public have a vested interest in understanding the contours of this dialogue. Our objective is to craft a data-driven narrative through visualizations that reveal how the world engages with the pandemic on the digital front, providing actionable insights into global and local responses to COVID-19 using Machine Learning techniques.
COVID-19; Machine Learning; Twitter; Word Cloud.
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Selim Molla, Ehsan Bazgir, S M Mustaquim, Iqtiar Md Siddique and Anamika Ahmed Siddique. Uncovering COVID-19 conversations: Twitter insights and trends. World Journal of Advanced Research and Reviews, 2024, 21(1), 836-842. Article DOI: https://doi.org/10.30574/wjarr.2024.21.1.0121