Research used data mining tools combat covid 19 misinformation


Computer scientists at UC Riverside are working on tools to assist track and monitor COVID-19 symptoms, as well as sort through disinformation about the condition on social media.

Using Google Trends data, a team led by Vagelis Papalexakis, an associate professor in the Marlan and Rosemary Bourns College of Engineering, and Jia Chen, an assistant professor of teaching, created an algorithm that identified three COVID-19 symptoms that were distinct from the flu: Ageusia (loss of taste function of the tongue), shortness of breath, and anosmia (loss of smell) are all symptoms of ageusia. The algorithm was created.

“Much of the work using Google Trends for flu has focused on forecasting the flu season,” Papalexakis said. “We, on the other hand, used it to see if we could find a needle in a haystack: symptoms unique to COVID-19 among all the flu-like symptoms people search for.”

The researchers found symptoms on Google Trends for 2019 and 2020 and used a technique known as nonnegative discriminative analysis, or DNA, to extract terms that were unique to one dataset versus the other.

“We assumed that symptom searches in 2019 would lead to influenza or other respiratory ailments, while searches for the same symptoms in 2020 could be either,” Chen said. “Using DNA, we were able to find the difference between the two datasets. This happened to be terms clinicians have already identified as unique to COVID-19, showing that our approach works.”

“Google trends data is very noisy, but hospital data is not publicly available. People might search for symptoms because they are experiencing them or because they have heard of them and want to know more,” Papalexakis said. “Searches reflect interest in symptoms better than people actively experiencing them, but given the lack of other data, we think this tool could help researchers understand symptoms better.”

Although the tool developed by Papalexakis and Shiao is a prototype under active research development, it could eventually be incorporated into a smartphone app or into social media platforms like Facebook.

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