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This study looks for signals of economic awareness on online social media andtests their significance in economic predictions. The study analyses, over aperiod of two years, the relationship between the West Texas Intermediate dailycrude 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 andcomplexity of the language used. Autoregressive Integrated Moving Average withExplanatory Variable (ARIMAX) models are used to make predictions and toconfirm the value of the study variables. Results show that the combinedanalysis of the four media platforms carries valuable information in makingfinancial forecasting. Twitter language complexity, GDELT number of articlesand Wikipedia page reads have the highest predictive power. This study alsoallows 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 beforeit happens. In comparison with previous work, more media sources and moredimensions of the interaction and of the language used are combined in a jointanalysis.

Using four different online media sources to forecast the crude oil price