Finding frames with BERT: A transformer-based approach to generic news frame detection
This addresses the problem of automating framing analysis for researchers in communication science, though it appears incremental as it applies an existing method (BERT) to a new domain.
The paper tackles the challenge of scaling framing analysis in online communication by introducing a transformer-based approach for generic news frame detection in Anglophone online content, discussing dataset composition, model architecture, and validation.
Framing is among the most extensively used concepts in the field of communication science. The availability of digital data offers new possibilities for studying how specific aspects of social reality are made more salient in online communication but also raises challenges related to the scaling of framing analysis and its adoption to new research areas (e.g. studying the impact of artificial intelligence-powered systems on representation of societally relevant issues). To address these challenges, we introduce a transformer-based approach for generic news frame detection in Anglophone online content. While doing so, we discuss the composition of the training and test datasets, the model architecture, and the validation of the approach and reflect on the possibilities and limitations of the automated detection of generic news frames.