LLMs for Detection and Classification of Persuasion Techniques in Slavic Parliamentary Debates and Social Media Texts

We present an LLM-based method for the Slavic NLP 2025 shared task on detection and classification of persuasion techniques in parliamentary debates and social media.Our system uses OpenAI's GPT models (gpt-4o-mini) and reasoning models (o4-mini) with chain-ofthought prompting, enforcing a 0.99 confidence threshold for verbatim span extraction.For subtask 1, each paragraph in the text is labeled "true" if any of the 25 persuasion techniques is present.For subtask 2, the model returns the full set of techniques used per paragraph.Across Bulgarian, Croatian, Polish, Russian, and Slovenian, we achieve Subtask 1 micro-F1 of 81.7%, 83.3%, 81.6%, 73.5%, 62.0%, respectively, and Subtask 2 F1 of 41.0%, 44.4%, 41.9%, 29.3%, 29.9%, respectively.Our system ranked in the top 2 for Subtask 2 and top 7 for Subtask 1.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.bsnlp-1.23
Citations
1
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
2.48
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article

LLMs for Detection and Classification of Persuasion Techniques in Slavic Parliamentary Debates and Social Media Texts

Rachel Greenstadt, Julia Jose
1 citations
Hate Speech and Cyberbullying Detection
2.48
article

LLMs for Detection and Classification of Persuasion Techniques in Slavic Parliamentary Debates and Social Media Texts

Rachel Greenstadt, Julia Jose
article en
1 citations

Abstract

We present an LLM-based method for the Slavic NLP 2025 shared task on detection and classification of persuasion techniques in parliamentary debates and social media.Our system uses OpenAI's GPT models (gpt-4o-mini) and reasoning models (o4-mini) with chain-ofthought prompting, enforcing a 0.99 confidence threshold for verbatim span extraction.For subtask 1, each paragraph in the text is labeled "true" if any of the 25 persuasion techniques is present.For subtask 2, the model returns the full set of techniques used per paragraph.Across Bulgarian, Croatian, Polish, Russian, and Slovenian, we achieve Subtask 1 micro-F1 of 81.7%, 83.3%, 81.6%, 73.5%, 62.0%, respectively, and Subtask 2 F1 of 41.0%, 44.4%, 41.9%, 29.3%, 29.9%, respectively.Our system ranked in the top 2 for Subtask 2 and top 7 for Subtask 1.

New York University (US)
Openalex Percentile: Top 9%
Hate Speech and Cyberbullying Detection
2.48
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