Indian-led team develops online search system to limit COVID-19 misinformation

Ateam led by an Indian-origin researcher in Canada has developed a new system that increases the correctness and reliability of online health-related searches by 80 percent to help people make better decisions about topics such as COVID-19.

The team at the University of Waterloo in Canada noted that the internet search engines are the most common tools the public uses to look for facts about COVID-19 and its effect on their health.

A proliferation of misinformation can have real consequences, so the team created a way to make these searches more reliable.

”With so much new information coming out all the time, it can be challenging for people to know what is true and what is not,” said Ronak Pradeep, a Ph.D. student in the Cheriton School of Computer Science at Waterloo and lead author of a study.

”But the consequences of misinformation can be pretty bad, like people going out and buying medicines or using home remedies that can hurt them,” Pradeep said.

The researchers said even the big search engines that host billions of searches every day can not keep up since there has been so much scientific data and research on COVID-19 in such a short time.

”Most of the systems are trained on well-curated data, so they don’t always know how to differentiate between an article promoting drinking bleach to prevent COVID-19 as opposed to real health information,” Pradeep said.

”Our goal is to help people see the right articles and get the right information so they can make better decisions in general with things like COVID,” he added.

Pradeep said the project aims to refine internet search programs to promote the best health information for users.

The team has leveraged its two-stage neural reranking architecture for search which they augmented with a label prediction system trained to discern correct from dubious and incorrect information.

The system links with a search protocol that relies on data from the World Health Organization (WHO) and verified information as the basis for ranking, promoting, and sometimes even excluding online articles.

”Our design can potentially improve consumer health search to combat misinformation, a challenge recently amplified by the COVID-19 pandemic,” the authors of the study wrote.

Pradeep and other authors Xueguang Ma, Rodrigo Nogueira, and Jimmy Lin, from the University of Waterloo, presented a paper on the preliminary findings of the system at SIGIR ’21, a conference on research and development in information retrieval, held between July 11-15 online.