  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.
