, published in the 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.
"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, co-director of the Center for Digital Behavior at UCLA, and 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.
Researchers collected over 550 million tweets between May 26 and Dec. 9, 2012, and developed an algorithm to find phrases including words such as "sex" and "get high." Then they plotted the matched tweets on a map and ran statistical models to discover the points of origin to see if any HIV cases had been reported in the area.
The study found the largest proportion tweets, both general and HIV-related, were in California, Texas, New York and Florida. On a per capita basis, the largest number of HIV risk–related tweets came from the District of Columbia, Delaware, Louisiana and South Carolina.
Drug use has been associated with HIV sexual risk behaviors and transmission of infectious disease in previous studies. 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.
Researchers say the study's main weakness lies in the fact that the HIV data used in the study comes from 2009. In order to test if this new approach can be used to predict future outbreaks, there needs to be a "gold standard" of frequently updated data, where tweets can be accessed instantly and compared with disease outbreaks.
"This study was designed to call for future research to understand the potential cost-effectiveness of this approach and to refine methods of using real-time social networking data for HIV and public health prevention and detection," they conclude.