Abstract
Using Italian data from Twitter, we employ textual data and machine learning techniques to build new real-time measures of consumers' inflation expectations. First, we select some relevant keywords to identify tweets related to prices and expectations thereof. Second, we build a set of daily measures of inflation expectations on the selected tweets combining the Latent Dirichlet Allocation (LDA) with a dictionary-based approach, using manually labelled bi-grams and tri-grams. Finally, we show that the Twitter-based indicators are highly correlated with both monthly survey-based and daily market-based inflation expectations. Our new indicators provide additional information beyond the market-based expectations, the professional forecasts, and the realized inflation, and anticipate consumers' expectations proving to be a good real-time proxy. Results suggest that Twitter can be a new timely source to elicit beliefs.
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