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A dataset for detecting emotional manipulation techniques in Lithuanian text
Rita Butkienė1, Algirdas Šukys1, Edgaras Dambrauskas1
1Centre of Information Systems Design Technologies, Faculty of Informatics, Kaunas University of Technology, K. Baršausko st. 59, 51368423, Kaunas, Lithuania.
Data in Brief
|July 9, 2026
Summary
This study introduces a new dataset of Lithuanian comments annotated for emotional manipulation techniques. It includes human annotations and GPT-4.1 model outputs to aid research in low-resource languages.
Area of Science:
- Natural Language Processing
- Computational Linguistics
- Sociolinguistics
Background:
- Emotional manipulation in online discourse is a growing concern.
- Limited resources exist for analyzing such phenomena in morphologically rich, low-resource languages like Lithuanian.
Purpose of the Study:
- To present the first publicly available dataset of Lithuanian comments annotated for emotional manipulation techniques.
- To provide a benchmark for evaluating computational models in span extraction and classification.
- To facilitate research on large language model (LLM) behavior and prompt engineering.
Main Methods:
- Manual annotation of 1000 Lithuanian news comments by four annotators using Label Studio.
- Identification and span-level annotation of fourteen emotional manipulation techniques.
- Generation and post-processing of GPT-4.1 model outputs using various prompting strategies.
Main Results:
- A comprehensive dataset comprising source comments, human annotations, prompt templates, and GPT-4.1 predictions.
- Span-level annotations for fourteen distinct emotional manipulation techniques.
- Machine-generated data enabling LLM behavior analysis and prompt engineering studies.
Conclusions:
- The dataset serves as a crucial resource for studying manipulation and persuasion in Lithuanian.
- It supports the development and evaluation of NLP models for low-resource languages.
- Facilitates comparative studies of human and machine annotation in the context of emotional manipulation detection.
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