Big Data, Big Player in Tracking COVID-19

BY: CLAIRE KIM

Published last Thursday on arXiv.org, a research paper led by a team of Harvard researchers, “presented an algorithm that registered danger 14 days or more before case counts begin to increase. The system uses real-time monitoring of Twitter, Google searches, and mobility data from smartphones, among other data streams”.

Honestly, the idea that big data can reveal large enough trends and patterns to predict the next outbreak of the coronavirus is something we should’ve taken advantage of since the beginning of this pandemic.

Social media is, and has always been, one of the most telling sources when it comes to analyzing general public sentiment. With over 2.95 billion social media users worldwide in 2019, the simple use of hashtags or Instagram stories can reveal a wave of frustration or celebration in response to current events in real-time.

If hashtags such as #COVID-19 #virus #secondwave #globalpandemic suddenly top the trending charts on Twitter, this should be concerning. Especially in a world in which rumors and gossip tend to be the highlight of trending hashtags, if such hashtags regarding the coronavirus are “trending”, then serious considerations should be made. This is a clear warning signal.

If Instagram stories start saturating in COVID-19 content, then this should also be interpreted as a warning signal, a clear warning signal to all health professionals and government officials who struggle to discern when the coronavirus seems to be proliferating.

In the digital world, we live in today, the use of big data isn’t a segue into the “Big Brother” society that Winston and Julia had to deal with in George Orwell’s famous novel 1984. In fact, the use of big data indicates that we are becoming more comfortable with the technology we created, becoming more tech-savvy in our initiatives and societal responses. The use of big data is something to be excited about, not stigmatized.

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