Social media data reveals signal for public consumer perceptions

TL;DR

This paper develops a Bayesian Gaussian Process Regression framework to reliably estimate consumer confidence index from social media data, demonstrating improved accuracy and early prediction capabilities over previous methods.

Contribution

It introduces a robust, non-parametric Bayesian approach for social media-based CCI estimation, with a principled methodology for reducing survey frequency and enhancing prediction reliability.

Findings

Model estimates are reliable several months in advance.

The approach outperforms existing social media prediction methods.

Different micro-decisions significantly impact model accuracy.

Abstract

Researchers have used social media data to estimate various macroeconomic indicators about public behaviors, mostly as a way to reduce surveying costs. One of the most widely cited economic indicator is consumer confidence index (CCI). Numerous studies in the past have focused on using social media, especially Twitter data, to predict CCI. However, the strong correlations disappeared when those models were tested with newer data according to a recent comprehensive survey. In this work, we revisit this problem of assessing the true potential of using social media data to measure CCI, by proposing a robust non-parametric Bayesian modeling framework grounded in Gaussian Process Regression (which provides both an estimate and an uncertainty associated with it). Integral to our framework is a principled experimentation methodology that demonstrates how digital data can be employed to reduce the frequency of surveys, and thus periodic polling would be needed only to calibrate our model. Via extensive experimentation we show how the choice of different micro-decisions, such as the smoothing interval, various types of lags etc. have an important bearing on the results. By using decadal data (2008-2019) from Reddit, we show that both monthly and daily estimates of CCI can, indeed, be reliably estimated at least several months in advance, and that our model estimates are far superior to those generated by the existing methods.