🤖 AI Summary
This study addresses the effectiveness of computational approaches for automatically detecting generic news frames in political communication. By systematically evaluating the performance of five pre-trained language models—BERT, RoBERTa, DeBERTa, DistilBERT, and ALBERT—the research constructs and leverages the first high-quality annotated dataset of Swiss election news, enabling assessment of model robustness beyond U.S.-centric contexts. The work contributes a comprehensive empirical comparison of BERT variants for news framing detection, releases the annotated dataset and fine-tuned models as open-source resources, and validates the applicability of the best-performing model for political communication analysis. These contributions establish a reproducible methodological foundation for computational social science research on media framing across diverse sociopolitical settings.
📝 Abstract
Framing continues to remain one of the most extensively applied theories in political communication. Developments in computation, particularly with the introduction of transformer architecture and more so with large language models (LLMs), have naturally prompted scholars to explore various novel computational approaches, especially for deductive frame detection, in recent years. While many studies have shown that different transformer models outperform their preceding models that use bag-of-words features, the debate continues to evolve regarding how these models compare with each other on classification tasks. By placing itself at this juncture, this study makes three key contributions: First, it comparatively performs generic news frame detection and compares the performance of five BERT-based variants (BERT, RoBERTa, DeBERTa, DistilBERT and ALBERT) to add to the debate on best practices around employing computational text analysis for political communication studies. Second, it introduces various fine-tuned models capable of robustly performing generic news frame detection. Third, building upon numerous previous studies that work with US-centric data, this study provides the scholarly community with a labelled generic news frames dataset based on the Swiss electoral context that aids in testing the contextual robustness of these computational approaches to framing analysis.