Artificial Intelligence and Social Media Data as an Alternative Source of Insights During Pandemics: A Case Study of Twitter for COVID-19 in Tanzania

Abstract

Recently, social media has become one of the major sources of information for different sectors, including healthcare. During COVID-19, there was a lot of community engagement on social media than traditional physical engagements. Tanzania, in particular, had a different approach to tackling the COVID-19 pandemic as the country didn’t really practice lockdown, and very little information was shared by the authorities in traditional media houses. To understand what really happened during the pandemic, we tried to investigate social media as people were sharing information rather than the traditional media houses, and find the correlation with other sources. In this study, we extracted and analysed four-day periods of Twitter posts of Kinondoni district, Dar es Salaam, Tanzania. The four days were picked intentionally as it was the time when Tanzania started approaching the pandemic, as the rest of the world changed its course. Most of our analysis results significantly correlate with the results reported by the government during the same period, 21st - 24th April 2020. We further performed an analysis of how much the COVID issue was discussed online. As the WHO and many governments around the world have been providing education to people on how to protect themselves and slow down the spread, none have had a way to measure how well people were educated. We analysed 20,421 tweets of Kinondoni district, the most populous district in Dar es Salaam, and where many expats live, and found out how well people were educated about the CORONAVIRUS disease. We further created an Artificial Intelligence algorithm, a Deep learning to be specific, which has been able to classify tweets into COVID and Non-COVID classes with an accuracy of 93%. Our results mean that social media data analysis can be used as a tool for topic modelling to detect the most trending topics like disasters, election events, and epidemics.