Fine-grained and Dense Annotation of Czech Propaganda Using Large Language Models
| Autoři | |
|---|---|
| Rok publikování | 2025 |
| Druh | Článek ve sborníku |
| Konference | Recent Advances in Slavonic Natural Language Processing, RASLAN 2025 |
| Fakulta / Pracoviště MU | |
| Citace | |
| www | Proceedings of the Nineteenth Workshop on Recent Advances in Slavonic Natural Languages Processing, RASLAN 2025. |
| Klíčová slova | manipulative techniques; propaganda; detection; annotation; large language models; LLM; Czech |
| Přiložené soubory | |
| Popis | WithinthepreviousprojectofCzechPropagandaDetectionthat aimed to recognize manipulative techniques in Czech news articles, the annotation was mostly limited to indicating the presence of a technique in a document. Inabout35%ofthedocuments,span-level evidence was also annotated, but only as a support for the document-level labels, resulting in a sparse coverage of the techniques. Thus, the resulting dataset has limitations for training and evaluating more fine-grained propaganda detection models. In this study, we examine the potential of large language models (LLMs) to generate dense span-level annotations of manipulative techniques in Czech news articles. We designed generation prompts tailored to each technique and experimented with several LLMs to produce annotations for a subset of the Czech Propaganda dataset. We present the details of the generation process, including the design of the prompts and the selection of models. We also evaluate the generated annotations both quantitatively and qualitatively, including a manual validation and comparison with human annotations. |
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