蓝莓视频

Researchers have created a new system that increases the correctness and reliability of health-related searches by 80 per cent to help people make better decisions about topics like COVID-19.

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 a team at the University of 蓝莓视频 has created a way to make these searches more reliable.听

鈥淲ith so much new information coming out all the time, it can be challenging for people to know what鈥檚 true and what isn鈥檛,鈥� said Ronak Pradeep, a PhD student in the Cheriton School of Computer Science at 蓝莓视频 and lead author of a study about the program. 鈥淏ut the consequences of misinformation can be pretty bad, like people going out and buying medicines or using home remedies that can hurt them.鈥�

Even the big search engines that host billions of searches every day can鈥檛 keep up, he said, since there has been so much scientific data and research on COVID-19 in such a short time.

鈥淢ost of the systems are trained on well-curated data, so they don鈥檛 always know how to differentiate between an article promoting drinking bleach to prevent COVID-19 as opposed to real health information,鈥� Pradeep said. 鈥淥ur 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.鈥�

Pradeep says the project aims to refine search programs to promote the best health information for users. He and his research team have leveraged their two-stage neural reranking architecture called mono-duo-T5 for search which they augmented with Vera, 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 and verified information as the basis for ranking, promoting and sometimes even excluding online articles.

A recent paper with results from preliminary testing of the system, 鈥�,鈥� with co-authors Pradeep, Xueguang Ma, Rodrigo Nogueira and Jimmy Lin, was recently published in听SIGIR 鈥�21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.