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