This paper describes the system developed by the LATE-iimas team for Task 10 of SemEval-2026: Psycomark, specifically for Subtask 2, which involves conspiracy detection. Our approach was based on fine-tuning the popular pre-trained language model DeBERTa-v3-Large. To address the challenges inherent in the provided dataset, such as class imbalance and the linguistic ambiguity of the "Can’t tell" label, we implemented a 5-Fold Stratified Cross-Validation technique combined with a Weighted Cross-Entropy Loss function. The final system, which operates using an ensemble of the resulting models, achieved a Weighted F1-Score of 0.75, placing it in the top 10 of the ranking.