We estimate monetary policy surprises (sentiment) from the perspective of three different textual sources: direct central bank communication (FOMC statements and press conferences), news articles, and Twitter posts during FOMC announcement days. Textual sentiment across sources is highly correlated, but there are times when news and Twitter sentiment substantially disagree with the sentiment conveyed by the central bank. We find that sentiment estimated using news articles correlates better with daily U.S. Treasury yield changes than the sentiment extracted directly from Fed communication, and better predicts revisions in economic forecasts and FOMC decisions. Twitter sentiment is also useful, but slightly less so than news sentiment. These results suggest that news coverage and Tweets are not a simple echo chamber but they provide additional useful information. We use Sastry (2022)’s theoretical model to guide our empirical analysis and test three mechanisms that can explain what drives monetary policy surprises extracted from different sources: asymmetric information (central bank has better information than journalists and Tweeters), journalists (and Tweeters) have erroneous beliefs about the monetary policy rule, and the central bank and journalists (Tweeters) have different confidence in public information. Our empirical results suggest that the latter mechanism is the most likely mechanism.