Misinformation circulating on social platforms often distorts public understanding and can escalate real-world consequences, particularly in volatile geopolitical contexts. In this study, we describe the system constructed for subtask 3 of the PROMID 2025 shared task, which focuses on identifying misleading content within tweets pertaining to the Russo-Ukrainian conflict. The submission from our team (Sarang) secured 2nd place, supported by strong evaluation metrics: a precision of 0.90, a recall of 0.91, and a weighted F1-score of 0.90. Because the dataset contained instances in multiple languages, all non-English posts were rendered into English through a large language model. To mitigate skewed label distributions, we introduced synthetic variants of the minority class, thereby easing class imbalance. The classification pipeline relied on a deberta-v3-small encoder, which proved adept at capturing fine-grained semantic distinctions. The resulting performance underscores the reliability of the proposed approach and provides a competitive reference point for forthcoming work. Overall, the study offers practical insights for advancing misinformation detection in future shared-task settings.