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

Year: 2026

Type: conference-paper

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 classification over enumerated candidate spans, using IoU≥0.95positive labeling, hardnegative sampling, and containment-based nonmaximum suppression (NMS) with boundaryaware span representations.Document classification is modeled independently using a sequence classifier with label smoothing and a stratified train-validation split.Analysis shows that entity-like roles (Actor, Victim) are detected robustly, while abstract roles (Action, Effect, Evidence) remain sensitive to boundary criteria.On the official test set, our systems rank 7th in Subtask 1 (0.2251 macro F1) and 12th in Subtask 2 (0.7694 weighted F1).

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/2026.semeval-1.194

Authors: Dom Marhoefer, Milos Suvakovic, Glenn Grant-Richards, Aidan Pinero, Ryan King

Institution: University of California, Santa Cruz

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W7166862520

FWCI:

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