Health, Social Media & Technology Group

This interdisciplinary group studies social and online media influences on health behavior and epidemiological patterns and how to leverage social media and technology to improve human health. The projects use a combination of experiments, surveys, machine learning, and artificial intelligence to solve problems in the area of HIV, drug use, hepatitis C, lifestyle, and vaccination.

Across three NIH grant-funded projects, our research concerns social and online media influences on HIV/STI transmission behaviors.

To combat HIV effectively, we need to know where treatment resources need to be aimed at and where new health risks are developing. One challenge is that communities’ and individuals’ risk factors for HIV change dynamically and it usually takes several years for these changes to show up in the large surveys that government agencies publish. In that time, early prevention opportunities go unused.

One candidate for a data source that can predict HIV rates and that is updated often is Twitter. Twitter can be accessed in real-time and a subset of tweets can be mapped to state, county, and even zip-code regions of origin. We expect that sociodemographic variables (e.g., poverty) and Twitter content will predict HIV outcomes, and that these two factors interact. For instance, when sociodemographic factors make people more likely to seek out social information (e.g., because they are uncertain and lack resources; Albarracín et al., 2010), reading a local friend’s tweet that encourages risky sex may be more influential.

We are using research-guided methods to identify HIV risk factors and associated language features (e.g., words referring to drug use). Additionally, we will use machine learning methods to explore new keywords and topics that have not been captured by the research literature yet. In the long term, we are planning to extend these studies to predict future outbreaks of HIV.

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