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 ± 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 (−7.2 pts), and layer-wise probing confirms belief information peaks at mid-stack layers (layer 16/24).

We investigate the factors affecting salience in movie screenplay by posing two research questions: 1. Does Uniform Information Density (UID) predict salient scenes? 2. What other linguistic properties distinguish salient scenes from non-salient background? We answer both questions through a controlled corpus study on the MENSA corpus, paired with a Linguistically-Grounded Salience Model (LGSM), a dual-stream classifier that fuses a frozen BERT encoder with explicit linguistic features through adaptive gating. The UID picture is layered in long-form discourse. Perscene UID summaries carry no independent signal once length is controlled, but at the token level salient scenes match non-salient material in the bulk while specifically suppressing the rarest, hardest-to-predict tokens in the upper tail. UID therefore lives at the within-scene token level rather than as a per-scene aggregate. Salient scenes also show a moderate and sustained cast of central characters (following an inverted-U pattern), strong third-person usage, syntactically simpler language, and more abstract vocabulary about motivation and conflict. LGSM exploits this signature and improves recall on salient scenes over a strong semantic baseline while yielding better downstream summaries.