Twitter tested as monitor of HIV risk
March 3, 2014 in Medical Technology
A new UCLA-led study suggests that real-time social media, such as Twitter could be used to track HIV and drug related behavior to detect potential outbreaks – and ultimately, as a tool for prevention efforts.
The study, published in the peer-reviewed journal Preventive Medicine, suggests it may be possible to predict sexual risk and drug use behaviors by monitoring tweets, mapping where those messages come from and linking them with data on the geographical distribution of HIV cases. The use of various drugs had been associated in previous studies with HIV sexual risk behaviors and transmission of infectious disease.
“Ultimately, these methods suggest that we can use ‘big data’ from social media for remote monitoring and surveillance of HIV risk behaviors and potential outbreaks,” said Sean Young, assistant professor of family medicine at the David Geffen School of Medicine at UCLA and co-director of the Center for Digital Behavior at UCLA, in a news release.
Founded by Young, the new interdisciplinary center brings together academic researchers and private sector companies to study how social media and mobile technologies can be used to predict and change behavior.
Young is also a member of the UCLA Center for Behavioral and Addiction Medicine; UCLA’s Center for HIV Identification, Prevention and Treatment Services; and the UCLA AIDS Institute.
Other studies have examined how Twitter can be used to predict outbreaks of infections like influenza, said Young. “But this is the first to suggest that Twitter can be used to predict people’s health-related behaviors and as a method for monitoring HIV risk behaviors and drug use,” he said.
For the study, researchers collected more than 550 million tweets between May 26 and Dec. 9, 2012, and created an algorithm to find words and phrases in them suggesting drug use or potentially risky behaviors, such as “sex” or “get high.” They then plotted those tweets on a map to discover where they originated, running statistical models to see if these were areas where HIV cases had been reported.
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The algorithm captured 8,538 tweets indicating sexually risky behavior and 1,342 suggesting stimulant drug use. The geographical data on HIV cases to which researchers linked the tweets came from AIDSVu.org, an interactive online map that illustrates the prevalence of HIV in the U.S.; this mapping data was from 2009.