Identifying the frames of news is important to understand the articles' vision, intention, message to be conveyed, and which aspects of the news are emphasized. Framing is a widely studied concept in journalism, and has emerged as a new topic in computing, with the potential to automate processes and facilitate the work of journalism professionals. In this paper, we study this issue with articles related to the Covid-19 anti-vaccine movement. First, to understand the perspectives used to treat this theme, we developed a protocol for human labeling of frames for 1786 headlines of No-Vax movement articles of European newspapers from 5 countries. Headlines are key units in the written press, and worth of analysis as many people only read headlines (or use them to guide their decision for further reading.) Second, considering advances in Natural Language Processing (NLP) with large language models, we investigated two approaches for frame inference of news headlines: first with a GPT-3.5 fine-tuning approach, and second with GPT-3.5 prompt-engineering. Our work contributes to the study and analysis of the performance that these models have to facilitate journalistic tasks like classification of frames, while understanding whether the models are able to replicate human perception in the identification of these frames.

翻译：识别新闻框架对于理解文章的观点、意图、传递的信息以及新闻中被强调的方面至关重要。框架是新闻学中广泛研究的概念，并已成为计算领域的新兴课题，有望实现流程自动化并简化新闻专业人士的工作。本文以新冠疫情反疫苗运动相关文章为研究对象。首先，为理解该主题的处理视角，我们设计了一套人工标注协议，对来自5个国家的欧洲报纸中反疫苗运动文章的1786条标题进行框架标注。标题是纸质媒体的关键单元，鉴于许多人仅阅读标题（或以此决定是否进一步阅读），其分析具有重要价值。其次，鉴于大语言模型在自然语言处理领域的进展，我们探讨了两种新闻标题框架推理方法：一是基于GPT-3.5的微调方法，二是基于GPT-3.5的提示工程方法。本研究通过分析模型在框架分类等新闻任务中的表现能力，以及检验模型能否复现人类在框架识别中的感知机制，为该领域的研究与分析做出贡献。