Applying the Character-Role Narrative Framework with LLMs to Investigate Environmental Narratives in Scientific Editorials and Tweets

Communication aiming to persuade an audience uses strategies to frame certain entities in ‘character roles’ such as hero, villain, victim, or beneficiary, and to build narratives around these ascriptions. The Character-Role Framework is an approach to model these narrative strategies, which has been used extensively in the Social Sciences and is just beginning to get attention in Natural Language Processing (NLP). This work extends the framework to scientific editorials and social media texts within the domains of ecology and climate change. We identify characters’ roles across expanded categories (human, natural, instrumental) at the entity level, and present two annotated datasets: 1,559 tweets from the Ecoverse dataset and 2,150 editorial paragraphs from Nature & Science. Using manually annotated test sets, we evaluate four state-of-the-art Large Language Models (LLMs) (GPT-4o, GPT-4, GPT-4-turbo, LLaMA-3.1-8B) for character-role detection and categorization, with GPT-4 achieving the highest agreement with human annotators. We then apply the best-performing model to automatically annotate the full datasets, introducing a novel entity-level resource for character-role analysis in the environmental domain.