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.

Depression detection from speech aims to findsigns of depression using behavioral signals.This approach enables early mental healthscreening and makes it scalable. However, thetask is tough because of subtle acoustic cues,differences among speakers, and language-specific patterns. In this work, we introduceour system for the Shared Task on DepressionDetection in Dravidian Languages (DD-DL)at DravidianLangTech@ACL 2026. We fo-cus on speech in Tamil and Malayalam. Weexplore pretrained self-supervised speech en-coders, including HuBERT, XLS-R, and Whis-per, to identify acoustic patterns related to de-pression directly from raw audio. Our methodcombines these models through ensembling tocapture different acoustic features. The ex-periments use stratified evaluation and cross-lingual analysis to check how well the mod-els work across languages. Results show thatpretrained acoustic representations effectivelycapture vocal features of depression, achiev-ing Macro-F1 scores of 0.9058 for Tamil and0.9396 for Malayalam. However, cross-lingualtransfer faces challenges because of phoneticand prosodic differences.