Augmenting survey data with digital traces is a promising direction for combining the advantages of active and passive data collection. However, extracting interpretable measurements from digital traces for social science research is challenging. In this study, we demonstrate how to obtain measurements of news media consumption from survey respondents' web browsing data using Bidirectional Encoder Representations from Transformers, a powerful natural language processing algorithm that estimates contextual word embeddings from text data. Our approach is particularly relevant for political scientists and communication researchers studying exposure to online news content but can easily be adapted to projects in other disciplines working with similar data sets.

中文翻译：

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通过数字跟踪数据和自然语言处理了解政治新闻媒体消费

使用数字轨迹增强调查数据是结合主动和被动数据收集优势的有前途的方向。然而，从用于社会科学研究的数字痕迹中提取可解释的测量值具有挑战性。在这项研究中，我们演示了如何使用来自 Transformers 的双向编码器表示从调查受访者的网络浏览数据中获取新闻媒体消费的测量值，Transformers 是一种强大的自然语言处理算法，可从文本数据中估计上下文词嵌入。我们的方法与研究在线新闻内容曝光的政治学家和传播研究人员特别相关，但可以轻松地适用于使用类似数据集的其他学科的项目。