模型之争：比较基于BERT的变体在通用新闻框架检测中的性能
Clash of the models: Comparing performance of BERT-based variants for generic news frame detection
AI总结：
本文比较了BERT、RoBERTa、DeBERTa、DistilBERT和ALBERT在通用新闻框架检测中的性能，提出了多种经过微调的模型，并构建了一个基于瑞士选举语境的标注数据集以测试框架分析的上下文鲁棒性。
摘要：
框架分析仍然是政治传播中应用最广泛的理论之一。计算技术的发展，特别是transformer架构的引入以及大型语言模型（LLMs）的出现，自然促使学者探索各种新的计算方法，尤其是在演绎框架检测方面。尽管许多研究显示不同的transformer模型在使用词袋特征的模型上表现更优，但关于这些模型在分类任务中相互比较的争论仍然持续发展。本研究在此阶段做出三个关键贡献：首先，它比较了通用新闻框架检测，并比较了五种基于BERT的变体（BERT、RoBERTa、DeBERTa、DistilBERT和ALBERT）的性能，以补充关于使用计算文本分析进行政治传播研究的最佳实践的争论。其次，它引入了能够稳健地进行通用新闻框架检测的各种微调模型。第三，基于众多先前研究中使用美国中心数据的研究，本研究为学术界提供了一个基于瑞士选举语境的标注通用新闻框架数据集，有助于测试这些计算方法在框架分析中的上下文鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
