We present our systems for Task 1 and Task 3 of the #SMM4H-HeaRD 2026 shared tasks. Task 1 focuses on binary classification of adverse drug event (ADE) mentions across seven languages, including a zero-shot Persian setting without labeled training data. We fine-tune XLM-RoBERTa-large using weighted cross-entropy loss and augment low-resource settings with additional CADEC data and machine translation-based Persian augmentation. Our system achieves a macro F1 score of 0.582, outperforming the shared task average of 0.547. Task 3 addresses influenza vaccine effectiveness estimation through classification of vaccination status and flu-test results from X posts. We fine-tune twitter-roberta-large, achieving micro F1 scores of 0.845 for vaccination status and 0.883 for flu-test classification on the official test set. Post-evaluation experiments with focal loss, test-time augmentation, and head-tail truncation further improve performance. These results highlight the effectiveness of robust transformer adaptation for health-related social media classification.