TY - GEN
T1 - Pattern Analysis of COVID-19 Based On Geotagged Social Media Data with Sociodemographic Factors
AU - Sabareesha, Seelam Sree Sai
AU - Bhattacharjee, Shrutilipi
AU - Shetty, Ramya D.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85141154288
UR - https://www.scopus.com/pages/publications/85141154288#tab=citedBy
U2 - 10.1109/ICAC55051.2022.9911119
DO - 10.1109/ICAC55051.2022.9911119
M3 - Conference contribution
AN - SCOPUS:85141154288
T3 - 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022
BT - 2022 27th International Conference on Automation and Computing
A2 - Yang, Chenguang
A2 - Xu, Yuchun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th International Conference on Automation and Computing, ICAC 2022
Y2 - 1 September 2022 through 3 September 2022
ER -
