Twitter Keyword Volume, Current Spending, and Weekday Spending Norms Predict Consumer Spending

We examine whether aggregate daily Twitter keyword volumes over eight months from November 2011 to June 2012 can be used to predict aggregate daily consumer spending as reported by Gallup. We also examine whether Twitter keyword volume improves predictive ability over prediction based solely on current spending, weekday spending norms, and spending history. We divide spending and Twitter data into (i) in-sample data used to identify which Twitter words are highly correlated with spending and to estimate model coefficients, and (ii) out-of-sample data used to measure model forecast success. Our methods are very general and include n-grams (e.g., pairs of words, like âgoing shoppingâ). We note that the historical spending data exhibit a weekday pattern of high spending on two days and low spending over the rest of the week. Spending history also shows some striking deviations from weekday norms, such as Black Friday (the day after the American Thanksgiving holiday) and Boxing day (the day after Christmas)â"historically large shopping days. We build models on combinations of Twitter keyword volume (T), current spending (S), and weekday spending norms (D), and compare four model forecast success measures: the correlation between actual and forecast daily spending changes, the percentage of correctly forecast directions of daily spending change, the correlation between actual and forecast deviations from weekday spending norms, and the percentage of correctly forecast deviations from weekday norms. We test model forecasts over the period: April - June. Our results show that weekday Twitter keyword volume, current spending, and weekday spending norms all have significant value for predicting consumer spending three days in advance, but none demonstrates a significant predictive advantage over the others.