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.

翻译：框架理论依然是政治传播学中应用最广泛的理论之一。近年来，计算技术的发展，特别是Transformer架构的引入以及大型语言模型的涌现，自然促使学者们探索各种新颖的计算方法，尤其是在演绎式框架检测领域。尽管许多研究表明，不同的Transformer模型优于先前使用词袋特征的模型，但关于这些模型在分类任务上如何相互比较的讨论仍在持续演进。本研究立足于这一节点，做出了三项关键贡献：第一，通过对比性执行通用新闻框架检测并比较五种基于BERT的变体（BERT、RoBERTa、DeBERTa、DistilBERT和ALBERT）的性能，为政治传播研究中应用计算文本分析的最佳实践讨论增添新证据；第二，引入了多种能够稳健执行通用新闻框架检测的微调模型；第三，在大量以美国为中心数据的先前研究基础上，本研究为学术界提供了一个基于瑞士选举背景的标注通用新闻框架数据集，有助于检验这些计算框架分析方法在不同语境下的稳健性。