Online Health Monitoring using Facebook Advertisement Audience Estimates in the United States: Evaluation Study

BACKGROUND: Facebook, the most popular social network with over one billion daily users, provides rich opportunities for its use in the health domain. Though much of Facebook’s data are not available to outsiders, the company provides a tool for estimating the audience of Facebook advertisements, which includes aggregated information on the demographics and interests, such as weight loss or dieting, of Facebook users. This paper explores the potential uses of Facebook ad audience estimates for eHealth by studying the following: (1) for what type of health conditions prevalence estimates can be obtained via social media and (2) what type of marker interests are useful in obtaining such estimates, which can then be used for recruitment within online health interventions. OBJECTIVE: The objective of this study was to understand the limitations and capabilities of using Facebook ad audience estimates for public health monitoring and as a recruitment tool for eHealth interventions. METHODS: We use the Facebook Marketing application programming interface to correlate estimated sizes of audiences having health-related interests with public health data. Using several study cases, we identify both potential benefits and challenges in using this tool. RESULTS: We find several limitations in using Facebook ad audience estimates, for example, using placebo interest estimates to control for background level of user activity on the platform. Some Facebook interests such as plus-size clothing show encouraging levels of correlation (r=.74) across the 50 US states; however, we also sometimes find substantial correlations with the placebo interests such as r=.68 between interest in Technology and Obesity prevalence. Furthermore, we find demographic-specific peculiarities in the interests on health-related topics. CONCLUSIONS: Facebook’s advertising platform provides aggregate data for more than 190 million US adults. We show how disease-specific marker interests can be used to model prevalence rates in a simple and intuitive manner. However, we also illustrate that building effective marker interests involves some trial-and-error, as many details about Facebook’s black box remain opaque.