Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh
This work provides domain-specific insights for policymakers in Bangladesh to monitor social dynamics during the COVID-19 pandemic.
This study analyzed spatio-temporal Bangladeshi newspaper data related to the COVID-19 pandemic using volume, topic, classification, and sentiment analysis to gain insights into pandemic impacts across sectors and regions. The analysis aims to help government and organizations identify societal challenges and inform immediate and post-pandemic response strategies.
Newspapers are trustworthy media where people get the most reliable and credible information compared with other sources. On the other hand, social media often spread rumors and misleading news to get more traffic and attention. Careful characterization, evaluation, and interpretation of newspaper data can provide insight into intrigue and passionate social issues to monitor any big social incidence. This study analyzed a large set of spatio-temporal Bangladeshi newspaper data related to the COVID-19 pandemic. The methodology included volume analysis, topic analysis, automated classification, and sentiment analysis of news articles to get insight into the COVID-19 pandemic in different sectors and regions in Bangladesh over a period of time. This analysis will help the government and other organizations to figure out the challenges that have arisen in society due to this pandemic, what steps should be taken immediately and in the post-pandemic period, how the government and its allies can come together to address the crisis in the future, keeping these problems in mind.