zhangpeng at SemEval-2026 Task 10: PsyCoMark - Psycholinguistic Conspiracy Marker Extraction and Detection
Year: 2026
Type: conference-paper
Abstract: We describe our system for SemEval-2026 Task 10 on psycholinguistic conspiracy marker extraction and conspiracy detection from English texts (Ghosh et al., 2026).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 macroaveraged 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.
Authors: Zhang Peng, Lu Gehao
Institution: Yunnan University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7166799550