Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287921 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of the Royal Statistical Society: Series A (Statistics in Society) [ISSN:] 1467-985X [Volume:] 185 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2022 [Pages:] S246-S269
Publisher: 
Wiley, Hoboken, NJ
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.
Subjects: 
digital trace data
news consumption
NLP
political preferences
web tracking
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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