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Data discoveries: Can social media become a tool for studying economic recovery?

 


A new study from the University of Bristol successfully predicts the economic recovery of small businesses in the wake of natural risk events using social media

In blatant disregard of the government’s advice, many US citizens took to the streets in April to demand that stringent closures affect their civil liberties and, notably, their actions. To combat the financial risks of small businesses, the UK government will have to borrow an unprecedented 38 percent of GDP for the year if social divergence measures are in place until the end of 2020, according to the Resolution Foundation. With skepticism heating up, it is necessary to understand how economies recover from crises.

“We thought it would be interesting to see the different types of data available to study small businesses in the wake of a natural hazard event,” says Filippo Simini, senior lecturer at the School of Engineering, University of Bristol. Team methods were remarkably simple: they recovered data from business pages available on Facebook in general, were modified to fit an unusually high social activity, and then compared posting tables before and after the natural hazard occurred.

By doing this, they accurately estimated the recovery time of three normal hazardous events; The 2015 Gurkala earthquake in Kathmandu, which affected 8.1 million Nepalese citizens; Hurricane Maria in San Juan, which caused major damage to infrastructure and affected all Puerto Rican residents; The Chiapas earthquake is in Juchitan de Zaragoza, the second strongest earthquake in Mexico’s history. All estimated recovery times were verified using textual analyzes, surveys, official reports and scientific publications – all of which indicated similar estimates.

The Gorka earthquake on April 25, 2015 affected nearly 8.1 million people. The Nepalese people move through the rubble in Paktapur near Kathmandu

“Our next step is to try to assess the recovery of small businesses after the Covid-19 shutdown procedures,” Simini says. “This technique can be performed in real time, which means you can collect data at any time; not only during natural risk events, but in theory during periods of economic crisis,” he adds.

In developing countries, this method can prove to be cheaper and more extensible than traditional business recovery field surveys. It is also privacy-friendly, and has no geographical restrictions, although Simini warns that results are only as good as the data – you might miss the least active companies on Facebook.

In developed countries, government evaluation of long-term business data is more complex – however, the real-time nature of this new technology can provide dynamic support for economic monitoring. “We want to be very careful and test the methodology first to see how it works. We hope that in the future this will have a global application as a monitoring system to identify early warning signs of unusual economic activity.

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London’s streets and stores are slowly opening after the closing period, in response to an outbreak of coronavirus. Could social media be a tool for monitoring business recovery?

As a data force, many have already called on Facebook and Twitter to use their data to help during humanitarian crises, with some success. As a data force, many have called on Facebook and Twitter to use their data to help during humanitarian crises. Recently, Facebook has started looking at how their data can be used to support NGOs and humanitarian organizations in understanding the effects of natural hazardous events. They also have a “data for good” campaign, which uses privacy-saved data to help with humanitarian issues. Through the COVID-19 pandemic, Facebook produces highly populated maps, disease prevention maps and social media measures to help governments and nonprofits track the epidemic and effectiveness of prevention initiatives.

However, Simini adds that while social media data has begun to spread for the sake of human goodness, we must remain aware of privacy and quality issues. These data sets only capture a sample of the population, which means that paid social media analyzes should support existing models, not replace them.

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