Pattern Analysis of COVID-19 Based On Geotagged Social Media Data with Sociodemographic Factors

Abstract

The world has faced a catastrophic global crisis of COVID-19 caused by coronavirus and called for analyzing the affected areas in any country. The study helps to understand how the second wave affected different states in India concerning sociodemographic factors, such as population density, economy, and unemployment rate. During the lockdown, the sudden impact of staying at home has led to increased social media usage, where people expressed their opinions on multiple topics. Twitter provides timestamp and sometimes spatial information of the tweets generated. Using the geotagged Twitter dataset, a study in India is performed in this work considering the second wave of COVID-19, which occurred approximately from April to June 2021. It analyses the temporal and spatial patterns of the geotagged tweets generated from all the states during the period mentioned above. Also, topic modeling and sentiment analysis are performed to understand the concerns discussed by the people. We use different states' sociodemographic factors and machine learning algorithms to divide the population into high and low categories to understand the topic prevalence in different socioeconomic groups. This study reveals that the low socioeconomic groups have shared more concerns, urging the government to help fight the COVID-19 pandemic. © 2022 IEEE.