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    <abstract>Conspiracy theories widely spread on social media and can harm society by increasing mistrust, vaccine hesitancy, and political radicalization. However, most automated detection systems have traditionally relied on topic-specific classifiers, which often struggle to generalize across domains and provide little explanation for why a text is considered conspiratorial. To address these limitations, this paper explores various LLMs on the SemEval-2026 Task 10: psycholinguistic conspiracy marker extraction and binary conspiracy detection from Reddit submission statements. Specifically, we adopt a training-free few-shot prompting approach using different instruction-tuned large language models under a variety of few-shot settings (k in {0,1,5,10,15, 20}). Within this framework, the proposed prompting strategy incorporates psychology-informed instructions to guide the models in identifying conspiracy-related signals. As a result, the presented system achieves an F1 score of 0.21 for marker extraction and 0.81 for conspiracy detection, ranking 16th out of 30 teams in Subtask~1 and 36th out of 52 in Subtask~2 without any task-specific fine-tuning. These results suggest that psycholinguistically grounded prompting can support interpretable conspiracy analysis; however, challenges remain in identifying implicit markers.</abstract>
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%0 Conference Proceedings
%T CUET320 at SemEval-2026 Task 10: Few-Shot Large Language Models for Psycholinguistic Marker Extraction and Conspiracy Detection
%A Fariha, Faozia
%A Khan, Lamia
%A Chowdhury, Madiha Ahmed
%A Ahmed, Kawsar
%A Hoque, Mohammed Moshiul
%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 fariha-etal-2026-cuet320
%X Conspiracy theories widely spread on social media and can harm society by increasing mistrust, vaccine hesitancy, and political radicalization. However, most automated detection systems have traditionally relied on topic-specific classifiers, which often struggle to generalize across domains and provide little explanation for why a text is considered conspiratorial. To address these limitations, this paper explores various LLMs on the SemEval-2026 Task 10: psycholinguistic conspiracy marker extraction and binary conspiracy detection from Reddit submission statements. Specifically, we adopt a training-free few-shot prompting approach using different instruction-tuned large language models under a variety of few-shot settings (k in {0,1,5,10,15, 20}). Within this framework, the proposed prompting strategy incorporates psychology-informed instructions to guide the models in identifying conspiracy-related signals. As a result, the presented system achieves an F1 score of 0.21 for marker extraction and 0.81 for conspiracy detection, ranking 16th out of 30 teams in Subtask~1 and 36th out of 52 in Subtask~2 without any task-specific fine-tuning. These results suggest that psycholinguistically grounded prompting can support interpretable conspiracy analysis; however, challenges remain in identifying implicit markers.
%U https://aclanthology.org/2026.semeval-1.432/
%P 3495-3507

Markdown (Informal)

[CUET320 at SemEval-2026 Task 10: Few-Shot Large Language Models for Psycholinguistic Marker Extraction and Conspiracy Detection](https://aclanthology.org/2026.semeval-1.432/) (Fariha et al., SemEval 2026)

• CUET320 at SemEval-2026 Task 10: Few-Shot Large Language Models for Psycholinguistic Marker Extraction and Conspiracy Detection (Fariha et al., SemEval 2026)

ACL

• Faozia Fariha, Lamia Khan, Madiha Ahmed Chowdhury, Kawsar Ahmed, and Mohammed Moshiul Hoque. 2026. CUET320 at SemEval-2026 Task 10: Few-Shot Large Language Models for Psycholinguistic Marker Extraction and Conspiracy Detection. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3495–3507, San Diego, California, USA. Association for Computational Linguistics.