A Corpus of Persuasion Techniques in Slavic Languages
2026-07-12 • Computation and Language
Computation and LanguageArtificial Intelligence
AI summaryⓘ
The authors created a new collection of texts in Bulgarian, Polish, and Russian that show different ways people try to persuade others. They labeled parts of the text with specific persuasion techniques based on a system with 25 detailed types grouped into six bigger categories. This collection has about 7,500 labeled parts from 222 documents discussing important topics. The authors also tested computer models, including older machine learning and newer AI methods, to see how well they can identify these persuasion techniques. Their work helps understand and detect how persuasion works in Slavic languages.
persuasion techniquescorpusSlavic languagestext annotationmachine learninggenerative AIrhetorical strategiestext-span levelsentence classification
Authors
Jakub Piskorski, Dimitar Iliyanov Dimitrov, Marina Ernst, Jacek Haneczok, Michał Marcińczuk, Arkadiusz Modzelewski, Roman Yangarber
Abstract
Persuasion techniques are powerful rhetorical devices used to sway public opinion in a wide range of media. We present a new corpus of persuasion techniques, focusing on Slavic languages. The corpus contains documents in Bulgarian, Polish, and Russian, annotated with persuasion techniques at the coarse-grained text-span level and fine-grained sentence level. The techniques are drawn from a taxonomy of 25 fine-grained persuasion techniques, grouped under six broad categories of rhetorical persuasion strategies. The corpus contains approximately 7500 text spans from 222 documents that cover topics hotly debated at the national and international levels. We describe the corpus creation process, provide detailed statistics, and examine correlations between topics and persuasion techniques. We use classic ML-based and generative AI-based models to provide baselines and benchmark results for the detection and classification of persuasion techniques at the text-span level and sentence level.