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The forecasting of political, economic, and public health indicators usinginternet activity has demonstrated mixed results. For example, while somemeasures of explicitly surveyed public opinion correlate well with social mediaproxies, the opportunity for profitable investment strategies to be drivensolely by sentiment extracted from social media appears to have expired.Nevertheless, the internet's space of potentially predictive input signals iscombinatorially vast and will continue to invite careful exploration. Here, wecombine unemployment related search data from Google Trends with economiclanguage on Twitter to attempt to nowcast and forecast: 1. State and nationalunemployment claims for the US, and 2. Consumer confidence in G7 countries.Building off of a recently developed search-query-based model, we show thatincorporating Twitter data improves forecasting of unemployment claims, whilethe original method remains marginally better at nowcasting. Enriching theinput signal with temporal statistical features (e.g., moving average and rateof change) further reduces errors, and improves the predictive utility of theproposed method when applied to other economic indices, such as consumerconfidence.

Blending search queries with social media data to improve forecasts of economic indicators