Transformer-Based Detection of Propaganda Techniques in a Low-Resource Language: A Case Study in Lithuanian
Pub. online: 12 June 2026
Type: Research Article
Open Access
Open Access
Received
1 April 2026
1 April 2026
Accepted
1 June 2026
1 June 2026
Published
12 June 2026
12 June 2026
Abstract
Propaganda techniques are a key tool for creating misleading content, often disseminated in native languages to increase their impact. Therefore, it is increasingly important to develop detection models not only for high-resource languages but also for low-resource languages, which still face significant limitations in propaganda detection. This study presents the first approach to automated propaganda technique detection in Lithuanian using the HALT-PROP corpus. We adapt the standard framework to account for frequent overlap between techniques. Experiments with the Lithuanian transformer LT-MLKM-modernBERT show that BILOU tagging improves span identification, while sentence classification based on span-level information enhances technique detection for most techniques. The results also indicate that training separate binary classifiers is more effective than multi-label classification in this setting. Overall, the proposed approach outperforms GPT-5.3 on most techniques and provides a strong baseline for propaganda technique detection in Lithuanian.
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Biographies
Rizgelienė Ieva
I. Rizgelienė is a PhD student at the Institute of Data Science and Digital Technologies, Vilnius University. Her primary research interests include propaganda detection and analysis, with an emphasis on low-resource languages.
Zaranka Paulius
P. Zaranka received his master’s degree in computer modelling from Vilnius University in 2025 and is currently a lecturer in NLP at Vilnius University. His primary research interests include large language models, natural language processing, and agent-based modelling.
Korvel Gražina
P. Zaranka received his master’s degree in computer modelling from Vilnius University in 2025 and is currently a lecturer in NLP at Vilnius University. His primary research interests include large language models, natural language processing, and agent-based modelling.
Marcinkevičius Virginijus
P. Zaranka received his master’s degree in computer modelling from Vilnius University in 2025 and is currently a lecturer in NLP at Vilnius University. His primary research interests include large language models, natural language processing, and agent-based modelling.
