Los Angeles:
Real-time social media like Twitter could be used to track HIV incidence and drug-related behaviours to help detect and potentially prevent outbreaks, a new study has found.
The study, led by University of California Los Angeles, suggests it may be possible to predict sexual risk and drug use behaviours 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 behaviours and transmission of infectious disease, researchers said.
"Ultimately, these methods suggest that we can use 'big data' from social media for remote monitoring and surveillance of HIV risk behaviours 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 Behaviour at UCLA.
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 behaviours and as a method for monitoring HIV risk behaviours and drug use," he said.
For the study, researchers collected more than 550 million tweets between May 26 and December 9, 2012, and created an algorithm to find words and phrases in them suggesting drug use or potentially risky behaviours, 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.
The algorithm captured 8,538 tweets indicating sexually risky behaviour 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 US; this mapping data was from 2009.
The states with the largest proportion of geo-located tweets, both general as well as HIV-related, were California (9.4 per cent), Texas (9.0 per cent), New York (5.7 per cent) and Florida (5.4 per cent).
When the researchers linked the tweets to data on HIV cases, they found a significant relationship between those indicating risky behaviour and counties where the highest numbers of HIV cases were reported.
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 behaviours, 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 was published in the journal Preventive Medicine.
The study, led by University of California Los Angeles, suggests it may be possible to predict sexual risk and drug use behaviours 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 behaviours and transmission of infectious disease, researchers said.
"Ultimately, these methods suggest that we can use 'big data' from social media for remote monitoring and surveillance of HIV risk behaviours 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 Behaviour at UCLA.
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 behaviours and as a method for monitoring HIV risk behaviours and drug use," he said.
For the study, researchers collected more than 550 million tweets between May 26 and December 9, 2012, and created an algorithm to find words and phrases in them suggesting drug use or potentially risky behaviours, 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.
The algorithm captured 8,538 tweets indicating sexually risky behaviour 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 US; this mapping data was from 2009.
The states with the largest proportion of geo-located tweets, both general as well as HIV-related, were California (9.4 per cent), Texas (9.0 per cent), New York (5.7 per cent) and Florida (5.4 per cent).
When the researchers linked the tweets to data on HIV cases, they found a significant relationship between those indicating risky behaviour and counties where the highest numbers of HIV cases were reported.
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 behaviours, 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 was published in the journal Preventive Medicine.
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