Publication details
Fine-grained and Dense Annotation of Czech Propaganda Using Large Language Models
| Authors | |
|---|---|
| Year of publication | 2025 |
| Type | Article in Proceedings |
| Conference | Recent Advances in Slavonic Natural Language Processing, RASLAN 2025 |
| MU Faculty or unit | |
| Citation | |
| web | |
| Keywords | manipulative techniques; propaganda; detection; annotation; large language models; LLM; Czech |
| Attached files | |
| Description | Within the previous project of Czech Propaganda Detection that aimed to recognize manipulative techniques in Czech news articles, the annotation was mostly limited to indicating the presence of a technique in a document. In about 35% of the documents, 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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