@inproceedings{peng-gehao-2026-zhangpeng-semeval,
title = "zhangpeng at {S}em{E}val-2026 Task 10: {P}sy{C}o{M}ark - Psycholinguistic Conspiracy Marker Extraction and Detection",
author = "Peng, Zhang and
Gehao, Lu",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.107/",
pages = "755--760",
ISBN = "979-8-89176-414-9",
abstract = "We 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{\textasciitilde}1 and a Weighted-F1 score of 0.7267 for Subtask{\textasciitilde}2. These results show that simple fine-tuning of pre-trained models provides a strong baseline for both marker extraction and conspiracy detection."
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<abstract>We 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.</abstract>
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%0 Conference Proceedings
%T zhangpeng at SemEval-2026 Task 10: PsyCoMark - Psycholinguistic Conspiracy Marker Extraction and Detection
%A Peng, Zhang
%A Gehao, Lu
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F peng-gehao-2026-zhangpeng-semeval
%X We 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.
%U https://aclanthology.org/2026.semeval-1.107/
%P 755-760
Markdown (Informal)
[zhangpeng at SemEval-2026 Task 10: PsyCoMark - Psycholinguistic Conspiracy Marker Extraction and Detection](https://aclanthology.org/2026.semeval-1.107/) (Peng & Gehao, SemEval 2026)
ACL