论文标题
测量文本的预测技能
Measuring Forecasting Skill from Text
论文作者
论文摘要
人们在对未来做出准确预测的能力上有所不同。先前的研究表明，有些人可以以更好的准确性来预测未来事件的结果。这导致了一个自然的问题：是什么使某些预报员比其他预报员更好？在本文中，我们探讨了人们用来描述其预测和预测技能的语言之间的联系。探索了来自两个不同预测域的数据集：（1）良好判断开放的地缘政治预测，在线预测论坛和（2）财务分析师对公司收益预测的语料库。我们提供了许多语言指标，这些指标是根据与人们对未来的预测相关的文本进行计算的，包括：不确定性，可读性和情感。通过研究与预测相关的语言因素，我们可以阐明熟练的预报员采取的方法。此外，我们证明，可以使用仅基于语言的模型来准确预测预测技能。这可能对识别准确的预测或可能较早的可能熟练的预测者有用。
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.
