Abstract
We empirically explore the drive towards emotionalisation in policy narratives in a highly technical, yet polarised, policy debate: the Farm to Fork strategy (F2F) of the EU. We do so by leveraging the Narrative Policy Framework (NPF) and applying it to the official statements of the Hungarian Government (opposing F2F). We first develop expectations drawing on the literature. For example, we expect correlations between NPF categories and emotions. The villain should be associated with anger and fear, the victim with compassion-empathy, the hero with pride. The plot can have different emotional associations. The doomsday should be discursively represented to elicit fear. Next, we built a corpus of 53 narratives decomposed into 794 sentences. This corpus is coded first at the level of NPF categories, then for the presence-absence of emotions and, when present, the exact type of emotion. Human coding is benchmarked against a large language model developed in the MORES project. We find that Hungary articulates its F2F position with emotional narratives bereft of empirical substance. When it is politically feasible, Hungary goes for rhetorical entrapment, asking the Commission to account for the lack of evidence-based tests and impact studies. The association NPF category-emotion works well, especially at the level of characters like the hero, the victim, and the villain. Our findings contribute to the NPF by specifying exactly how emotions map onto characters, narrators, and the overall narrative.
Keywords
Discourse, Emotions, Farm to Fork, Green Deal, Hungary, Narrative Policy Framework