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April 10, 2020 Friday 12:51:40 AM IST

Social media can forecast economic impact of disasters including COVID-19

Policy Indications

According to new research published in Nature Communications, social media should be used to chart the economic impact and recovery of businesses in countries affected by the COVID-19 pandemic. University of Bristol scientists describe a 'real time' method accurately trialled across three global natural disasters which could be used to reliably forecast the financial impact of the current global health crisis.

Traditional economic recovery estimates, such as surveys and interviews, are usually costly, time-consuming and do not scale-up well. However, researchers from Bristol's School of Engineering Maths and Department of Civil Engineering show they were able to accurately estimate the downtime and recovery of small businesses in countries affected by three different natural hazards using aggregated social media data. The method relies on the assumption that businesses tend to publish more social media posts when they are open and fewer when they are closed, hence analysing the aggregated posting activity of a group of businesses over time it is possible to infer when they are open or closed.

Using data from the public Facebook posts of local businesses collected before, during and after three natural disasters comprising the 2015 Gorkha earthquake in Nepal, the 2017 Chiapas earthquake in Mexico, and the 2017 hurricane Maria in Puerto Rico, the team charted the number of smaller urban businesses who were closed and then were able to measure their recovery post-event. The team validated their analysis using field surveys, official reports, Facebook surveys, Facebook posts text analysis and other studies available in literature. Importantly, the framework works in 'real time' without the need for text analysis which can be largely dependent on language, culture or semantic analysis and can be applied to any size area or type of natural disaster, in developed and developing countries, allowing local governments to better target the distribution of resources.

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