DAVIS (CBS13) – UC Davis researchers conducting a study of coronavirus-related posts on China’s popular microblogging website Weibo say social media surveillance could help health officials identify and respond to emerging outbreaks.

The study involved the analysis of over 12 million Weibo posts regarding COVID-19 between November 2019 and March 2020.

The research found that posts about symptoms and the disease could help health officials predict daily case counts up to week earlier than officials statistics.

Cuihua “Cindy” Shen, a UC Davis associate professor of communication, along with campus colleagues and others from two universities in China, used machine learning to sift sick posts from other COVID-19 related posts, according to a post published by Kathleen Holder in the Society, Arts & Culture section on the university’s website. The researchers built a pool of 250 million Weibo users and utilized 167 keywords related to coronavirus to identify a pattern in the posts.

The researchers said the sick posts held true for both Hubei province, the place of origin for the virus, and the rest of mainland China.

“Being the epicenter of the outbreak, Hubei province experienced extreme testing shortages during the early stage of the study period,” the researchers wrote in their paper. “As a result, many Hubei residents turned to social media sites such as Weibo to seek help for testing and medical care.”

The researchers suggest this new method as “a fast, low-cost way to inform disease containment and mitigation.” They also cited the low-risk factor of social media tracking as it can be done from afar.

“It is imperative that international organizations such as the World Health Organization integrate such data into their outbreak forecasting management practices, in order to mobilize and coordinate relief efforts to help combat COVID-19,” UC Davis researchers said.

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