Prediction of Population Health Indices from Social Media using Kernel-based Textual and Temporal Features

From 1984, the US has annually conducted the Behavioral Risk Factor Surveillance System (BRFSS) surveys to capture either health behaviors, such as drinking or smoking, or health outcomes, including mental, physical, and generic health, of the population. Although this kind of information at a population level, such as US counties, is important for local governments to identify local needs, traditional datasets may take years to collate and to become publicly available. Geocoded social media data can provide an alternative reflection of local health trends. In this work, to predict the percentage of adults in a county reporting“insufficient sleep”, a health behavior, and, at the same time, their health outcomes, novel textual and temporal features are proposed. The proposed textual features are defined at midlevel and can be applied on top of various low-level textual features. They are computed via kernel functions on underlying features and encode the relationships between individual underlying features over a population. To further enrich the predictive ability of the health indices, the textual features are augmented with temporal information. We evaluated the proposed features and compared them with existing features using a dataset collected from the BRFSS. Experimental results show that the combination of kernel-based textual features and temporal information predict well both the health behavior (with best performance at rho=0.82) and health outcomes (with best performance at rho=0.78), demonstrating the capability of social media data in prediction of population health indices. The results also show that our proposed features gained higher correlation coeffi©2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. WWW 2017 Companion, April 3–7, 2017, Perth, Australia. ACM 978-1-4503-4914-7/17/04. http://dx.doi.org/10.1145/3041021.3054136 . cients than did the existing ones, increasing the correlation coefficient by up to 0.16, suggesting the potential of the approach in a wide spectrum of applications on data analytics at population levels.