zhangpeng at SemEval-2026 Task 10: PsyCoMark - Psycholinguistic Conspiracy Marker Extraction and Detection

AbstractWe describe our system for SemEval-2026 Task 10 on psycholinguistic conspiracy marker extraction and conspiracy detection from English texts. The shared task consists of two subtasks: (1) extracting conspiracy-related markers—actor, action, effect, victim, and evidence—evaluated using an overlap-based macro F1-score, and (2) detecting conspiracy content as a binary text classification problem evaluated using macro-averaged F1-score. Our approach relies on fine-tuning pre-trained transformer encoders, including multilingual DistilBERT variants and DeBERTa-v3, without using external corpora or data augmentation techniques. Experimental results show that our best models achieve a macro-F1 score of 0.1476 for Subtask~1 and a Weighted-F1 score of 0.7267 for Subtask~2. These results show that simple fine-tuning of pre-trained models provides a strong baseline for both marker extraction and conspiracy detection.