  People vary in their ability to make accurate predictions about the future.
Prior studies have shown that some individuals can predict the outcome of
future events with consistently better accuracy. This leads to a natural
question: what makes some forecasters better than others? In this paper we
explore connections between the language people use to describe their
predictions and their forecasting skill. Datasets from two different
forecasting domains are explored: (1) geopolitical forecasts from Good Judgment
Open, an online prediction forum and (2) a corpus of company earnings forecasts
made by financial analysts. We present a number of linguistic metrics which are
computed over text associated with people's predictions about the future
including: uncertainty, readability, and emotion. By studying linguistic
factors associated with predictions, we are able to shed some light on the
approach taken by skilled forecasters. Furthermore, we demonstrate that it is
possible to accurately predict forecasting skill using a model that is based
solely on language. This could potentially be useful for identifying accurate
predictions or potentially skilled forecasters earlier.
