Understanding Political News Media Consumption with Digital Trace Data and Natural Language Processing
本研究展示了如何利用Transformer模型从受访者的网页浏览数据中提取新闻媒体消费的测量指标，对研究在线新闻接触的政治学和传播学学者尤其有用。
Abstract 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.