Coronavirus communicators face an infodemic

But Zignal Labs and nonprofit the Public Good Projects are helping them navigate the chaos.

The world is facing a pandemic. That much is obvious. But it’s also facing an “infodemic,” a term coined by the World Health Organization to describe the “over-abundance” of information about coronavirus. Some is accurate but much of it is not.

Zignal Labs has partnered with one of its long-time customers, The Public Good Projects, to help communicators in the health community and other industries navigate this mass of conflicting information and provide reliable messages to the public and consumers.

Zignal Labs is a real-time media-monitoring platform that tracks a broad range of sources, everything from Twitter to Reddit to LexisNexis. “We help our customers very quickly make sense of information and respond to it,” says Zignal chief customer officer Jennifer Granston. “We work mostly with large enterprises, but also with government. We have Fortune 50 customers in financial services, health, technology and energy, including Prudential, Under Armour and Uber.”

PGP, meanwhile, is a public health nonprofit founded in 2013. CEO Dr. Joe Smyser explains that the organization is very closely aligned with public health authorities. He was preceded as CEO by Dr. Tom Farley, Philadelphia’s health commissioner. 

The joint venture to cope with the infodemic is called Project RCAID, which stands for Rapid Collection Analysis Interpretation Dissemination. The Zignal platform pulls in the same media data that would be available to any Zignal users. “The difference now,” says Smyser, “is that there are five analysts with master’s degrees in public health sitting in front of the dashboards, seven days a week, interpreting the data and providing context.”

Zignal itself has the capability to look at disinformation from the standpoint of understanding how content is being shared and propagated, especially through automated channels. “We look for true human engagement as opposed to bot networks, influencer operations and troll farms,” says Granston.

But the need goes beyond distinguishing real information from the fake. 

“What you have is this excessive amount of information, not just on coronavirus but on all the issues surrounding it,” Granston explains. “You have conflicting information coming from very reliable sources. When you have governments and health ministers contradicting each other and giving different guidance and advice, it’s creating a situation that goes beyond a traditional disinformation situation. It’s really difficult for brands to navigate.” One example is conflicting advice on ibuprofen use.

The rapid and automated, but indiscriminate, sharing of information only adds to the noise and confusion. The volume of data is soaring, too: Zignal is processing 500 million stories per week compared with 150 million per week in December; the number of inference calculations performed by its machine learning has grown from 5 trillion to 25 trillion per week.

Smyser approached Zignal, recognizing this as not only a serious, but also a long-term challenge. The strong public health expertise of Smyser’s team, says Granston, offered an opportunity to “provide that context layer and that real insight from a public health perspective that would create a much more powerful tool for the private and public entities who are figuring out how to deal with this and how to respond.”

Among its users are communications teams at private healthcare entities and brands in other verticals. “We’re also interested in giving it away to state health authorities, and reporters,” Smyser says.

The project also has a potential long tail going beyond the coronavirus pandemic. 

“We have a mental health crisis in the U.S., with suicide as the leading cause of death for young people,” says Smyser. “Coronavirus is only going to exacerbate the issue. And so it’s not just about telling people what’s going on right now, but being more proactive, getting ahead of it, and talking about what we know is on the horizon.”

This story was updated on April 2 to correct the number of inference calculations performed by Zignal's machine learning per week. 

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