We build an indicator of individual wellbeing in the United States based on Google Trends. The indicator is a combination of keyword groups that are endogenously identified to fit with weekly timeseries of subjective wellbeing measures collected by Gallup Analytics surveys. We show that such information from Big Data can be used to build a model that accurately forecasts survey-based measures of subjective well-being. The model successfully predicts the out-of-sample evolution of most subjective well-being measures at a one-year horizon. This opens up the possibility to use Big Data as a complement to traditional survey data to measure and analyze the well-being of population at high frequency and very local geographic level. We show that we can also exploit the internet search volume to elicit the main life dimensions related to well-being. We find that keywords associated with job search, financial security, family life and leisure are the strongest predictors of the variations in subjective well-being in the United States. This paper contributes to the new research agenda on data sciences by showing how Big Data can improve our understanding of the foundations of human well-being
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