  This study looks for signals of economic awareness on online social media and
tests their significance in economic predictions. The study analyses, over a
period of two years, the relationship between the West Texas Intermediate daily
crude oil price and multiple predictors extracted from Twitter, Google Trends,
Wikipedia, and the Global Data on Events, Language, and Tone database (GDELT).
Semantic analysis is applied to study the sentiment, emotionality and
complexity of the language used. Autoregressive Integrated Moving Average with
Explanatory Variable (ARIMAX) models are used to make predictions and to
confirm the value of the study variables. Results show that the combined
analysis of the four media platforms carries valuable information in making
financial forecasting. Twitter language complexity, GDELT number of articles
and Wikipedia page reads have the highest predictive power. This study also
allows a comparison of the different fore-sighting abilities of each platform,
in terms of how many days ahead a platform can predict a price movement before
it happens. In comparison with previous work, more media sources and more
dimensions of the interaction and of the language used are combined in a joint
analysis.
