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UCSC NLP systems achieve top ranks in SemEval-2026 conspiracy detection task

By PulseAugur Editorial · [2 sources] ·

UCSC NLP researchers have developed systems for SemEval-2026 Task 10, focusing on conspiracy marker extraction and document-level conspiracy detection. Their approach for marker extraction involves multi-label span classification with advanced techniques like hard-negative sampling and boundary-aware representations. For document classification, they employed a sequence classifier with label smoothing. The systems achieved 7th place in marker extraction and 11th place in document detection on the official test set. AI

IMPACT This research contributes to the advancement of NLP techniques for detecting conspiracy markers and classifying conspiracy-related documents.

RANK_REASON The cluster describes a research paper detailing systems developed for a specific NLP task at a competition.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

UCSC NLP systems achieve top ranks in SemEval-2026 conspiracy detection task

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Dom Marhoefer, Milos Suvakovic, Glenn Grant-Richards, Aidan Pinero, Ryan King ·

    UCSC NLP at SemEval-2026 Task 10: Boundary-Aware Span Extraction and RoBERTa Classification for Conspiracy Detection

    arXiv:2607.05689v1 Announce Type: new Abstract: We present our systems for SemEval-2026 Task 10 (PsyCoMark), addressing conspiracy marker extraction (Subtask 1) and document-level conspiracy detection (Subtask 2). For marker extraction, we formulate the task as multi-label span c…

  2. arXiv cs.CL TIER_1 English(EN) · Ryan King ·

    UCSC NLP at SemEval-2026 Task 10: Boundary-Aware Span Extraction and RoBERTa Classification for Conspiracy Detection

    We present our systems for SemEval-2026 Task 10 (PsyCoMark), addressing conspiracy marker extraction (Subtask 1) and document-level conspiracy detection (Subtask 2). For marker extraction, we formulate the task as multi-label span classification over enumerated candidate spans, u…

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