The states with the largest proportion of geo-located tweets, both general as well as HIV-related, were California (9.4 percent), Texas (9.0 percent), New York (5.7 percent) and Florida (5.4 percent). On a per capita basis, the largest raw number of HIV risk–related tweets came from the District of Columbia, Delaware, Louisiana and South Carolina. States with the highest per capita rate of tweets were Utah, North Dakota and Nevada.  

When the researchers linked the tweets to data on HIV cases, they found a significant relationship between those indicating risky behavior and counties where the highest numbers of HIV cases were reported, according to Young.

Based on this study, the researchers concluded that it is possible to collect "big data" on real-time social media like Twitter about sexual and drug use behaviors, create a map of where the tweets are occur and use this information to understand and possibly predict where HIV cases and drug use occur.

The study's main weakness, the researchers say, is that the HIV data comes from 2009, so in order to test if this approach can be used to predict future behaviors and outbreaks there is a need for a "gold standard" of frequently updated data. In this way, tweets can be accessed instantly to compare them with disease outbreaks.

[See also: Twitter app takes on public health.]

The study does however demonstrate the feasibility of using real-time social networking to identify and map HIV risk-related communications and link them to national HIV data, the researchers write. Caitlin Rivers and Bryan Lewis of Virginia Tech co-authored the study, which was funded by a grant from the National Institutes of Mental Health.

"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," the researchers conclude.