An Interpretable Model for Real-time Tracking of Economic Indicators Using Social Media Data

Abstract

Measures of public opinion on economic matters are vital in creating accurate official statistics needed to design appropriate policy interventions and shape private investment decisions. This is traditionally done using public opinion polls and representative surveys. However, this process is time and money intensive and currently suffers from reduced public participation. As a result, official statistics are usually delayed and are not frequently available at the resolution needed for better policy interventions. Hence, researchers have looked into anonymized digital data to continuously sense information about public behaviors, especially those related to consumer sentiment index and unemployment insurance claims. However, past studies relied on linear models with simplistic assumptions and thus provided limited extrapolatory power and no insights as to why these predictive methods work. Worryingly, the strong correlations reported in these studies disappeared when the original models were tested with newer social media data.We propose a novel interpretable machine learning model, called Group Additive Gaussian Processes, to provide accurate and near real-time estimates of economic indicators about public behaviors using social media data, along with insights into the model behavior. Our model exploits the underlying structure in data and encodes interpretability in the modeling framework. It is based on Gaussian Process regression, which provides a robust non-parametric Bayesian learning framework that produces calibrated uncertainty measures along with its predictions. A key challenge in the learning task is learning from limited training data. We demonstrate how our model not only learns but also generalizes well in these scenarios. Through extensive evaluation we show how our model performs on two important indicators of economic health-consumer confidence index and unemployment insurance claims data. Further, we demonstrate how our model can reduce the need to conduct surveys by producing highly accurate and frequent estimates in between the surveying periods.