Marketers today use social media influencers to push clothing, beauty products, and diet pills to their followers. Dr. Eric Rice uses influencers to save lives.
In an effort to curb the spread of HIV among homeless youth in Los Angeles, Rice, who is co-founder of the USC Center for Artificial Intelligence in Society (CAIS), has helped develop an algorithm that combs the social media networks of homeless youth to find their most connected members. Once found and recruited by social workers, these connectors, dubbed “mavens,” become powerful champions of HIV prevention, which they advocate to the other young people in their networks.
The homeless are particularly exposed to the risks of HIV. Almost one in 10 homeless in Los Angeles have HIV “but a lot of them don’t even know it,” said Rice. Also, social workers face particular challenges helping high-risk youth, who are both difficult to reach and are distrusting of adults—particularly those in authority. This is why the social influencer model is so potent. By using the same social networks that homeless youth use to share updates about safe shelters, free food, and danger, Rice’s project can reach more people faster and more efficiently than traditional means of outreach. This model can also connect disparate groups of homeless youth that rarely communicate with each other.
“Essentially, what we’re doing is word-of-mouth marketing,” said Rice. “It’s predicated on the idea that, in the context of public health, when you’re working with high-risk kids, they’re really influenced by one another more than anything else.”
The challenges that Rice and his social worker partners face are common ones in the public health field. When it comes to selling public health initiatives, policymakers and practitioners face the same challenges as marketers in every other area. Even the most well-crafted, well-intentioned, and data-backed initiative still needs effective marketing and communications behind it to compel people to take action. Policymakers and practitioners have to think critically about how to both make the public aware of issues and their impact, and compel individuals to take action on them.
Thanks to today’s technology, their job has gotten a lot easier. Now, instead of targeting a health message to a broad, unfocused group of people, policymakers can single out the specific demographics that are vulnerable and target them using the same digital advertising tools that brands do. This is a game-changer for public policymakers, who typically lack the marketing budgets of big companies, and are constantly looking for ways to squeeze more efficiency out of their funding. In addition, these targeted campaigns are often more effective ways to reach people and get them to respond, argues Dr. Monique Turner, assistant dean for Masters of Public Health programs at The George Washington University.
“The more narrowly targeted your audience is the more likely you can design a strategy that really resonates with them, their culture, their age, everything,” she said. “When you can design a message that resonates with them, it makes it far more likely you can actually get them to comprehend the arguments you’re trying to make.”
Turner is the author of the Anger Activism model, a behavioral theory that explains how negative emotions such as anger and guilt can be used to positive ends. This understanding, she said, is vital to marketing public health policy today because people are far more likely to be affected by marketing messages that resonate with them emotionally than by those that don’t.
What’s more, people’s susceptibility to emotional appeals is also affected by their psychological dispositions. For example, researchers have known for decades that people have different appetites for new experiences and feelings. Those who are more “sensation seeking,”(e.g., young people), are more likely to experiment with drugs and alcohol. So to reach them with anti-drug messaging, advocates craft high-sensation messages designed to catch their attention and keep them engaged. (Note, for example, the rapid editing and bold, bright colors of Truth’s youth-focused anti-smoking campaigns.)
In today’s information ecosystem, this understanding of how people think is vital. Not only has the internet streamlined people’s ability to construct their own news silos, but it’s also trained them to triage and dismiss information much more quickly. This gives anyone pushing a message—even a public health message—precious little time to get their point across.
This is why, when Syracuse University communications professor Michael Meath consults on a public health campaign, he starts by telling the client to distill everything down to a statement that’s 140 characters. “I know in Twitter you can now use 280 characters, but I like the short, simple messages,” he says. That one line could then be tweeted, posted on Facebook, used as a tagline in an advertisement, embedded in a news release, and posted on the website.
Meath’s argument is that while the specific marketing tools are changing, the fundamentals never will. “Whether you’re a public health official or a CEO of a major business, I say the same thing: You have to boil your message down to its most simple form and repeat it. If you can’t communicate your message, then all the great policy understanding in the world isn’t going to do anything for you. A lot people fail to realize that.”
For USC’s Rice, it’s clear that a more sophisticated understanding of how to use digital tools in public health campaigns will be central to where the healthcare policy space is headed. Other researchers are working on similar AI-driven projects focused on other at-risk groups, such as children in child welfare programs. Likewise, at USC, he teaches a course called “AI for Social Good,” where nearly a quarter of his students are working on viral marketing or AI-driven projects. Many of these students have worked in public health for over a decade, and see learning predictive analytics and prescriptive algorithms as key to being future leaders in the space. Rice’s prediction is that, two years from now, two-thirds of his class is going to be doing something with AI.
“This isn’t something that just a couple of professors are looking at. This is the future,” Rice said. “The power of what machine learning and algorithmic thinking can do far outstrips what traditional statistics can do in terms of mining data for information.”
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