@inproceedings{goyal-2026-truth,
    title = "Truth Gradient at {S}em{E}val-2026 Task 10:Conspiracy Belief Detection via Narrative Density and Mean Pooling",
    author = "Goyal, Ekansh",
    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://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.424/",
    pages = "3422--3431",
    ISBN = "979-8-89176-414-9",
    abstract = "Conspiracy believers use significantly more psycholinguistic markers per post than nonbelievers (Cohen{'}s d = 0.56, p 10⁻⁸⁰), a pattern we term narrative density, suggesting that belief manifests as structurally denser conspiratorial frames distributed across the full text rather than concentrated in specific lexical cues.We present Truth Gradient{'}s system for SemEval-2026 Task 10 Subtask 2 (Samory et al., 2026): a DeBERTaV3-large model with mean pooling and a 5-seed probability-averaging ensemble achieving macro F1 = 0.829 on the 77-sample development set and 0.75 on the official test set. The 5-fold CV estimate (0.734 {\ensuremath{\pm}} 0.007) proves the more reliable predictor of test performance, and we recommend it as standard practice for low-resource shared tasks.Two convergent tests support the narrative density account: masking annotated marker spans drops F1 by 5.3 pp, and direct marker-count fusion recovers +0.9 pp, though we note these are not conclusive given the small dev set. Cross-validated ablation identifies encoder fine-tuning as the dominant design factor ({\ensuremath{-}}7.2 pts), and layer-wise probing confirms belief information peaks at mid-stack layers (layer 16/24)."
}