London, April 10 Analysis of content that businesses post on Facebook or other social media platforms can help chart the economic impact and their recovery in countries affected by the COVID-19 pandemic, says a study.
Traditional economic recovery estimates, such as surveys and interviews, are usually costly, time-consuming and do not scale-up well.
However, in the new study published in the journal Nature Communications, the researchers 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 challenge of nowcasting the effect of natural hazards such as earthquakes, floods, hurricanes, and pandemics on assets, people and society has never been more timely than ever for assessing the ability of countries to recover from extreme events," said the study's lead author Filippo Simini, Senior Lecturer at University of Bristol in Britain.
"Often, small to medium-sized businesses slip through the net of traditional monitoring process of recovery. We noticed in areas struck by natural hazard events that not all areas and populations react in the same way," he said.
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 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, said the study.
( With inputs from IANS )