UNLP-2025
Social Media Manipulation Detection in Ukrainian Telegram Posts
UNLP-2025 Shared Task: Detecting Social Media Manipulation
Purdue University Fort Wayne, 2025
Multi-label classification and span-level detection of manipulation techniques in Ukrainian Telegram posts. This work focused on identifying propaganda and manipulation techniques using transformer-based models with explainability and failure-mode analysis.
Task Description
- Multi-label Classification: Identify multiple manipulation techniques present in social media posts
- Span Detection: Locate specific text spans where manipulation occurs
- Context: Ukrainian Telegram posts in high-stakes disinformation environment
Approach
- RoBERTa-based models with rationale-guided training
- Emphasis on explainability and interpretable predictions
- Systematic failure-mode analysis to understand model limitations
- Cross-lingual transfer learning techniques
Key Findings
- Achieved competitive performance on manipulation technique classification
- Identified systematic challenges in span-level detection for code-mixed text
- Demonstrated importance of explainability in high-stakes NLP applications
Skills & Tools
Python, Transformers, RoBERTa, XLM-R, Multi-label Classification, Span Detection, Explainable AI