Creative Catalysts at #SMM4H-HeaRD 2026: XLM-RoBERTa for Task 1 Binary Classification of Social Media Posts Containing Adverse Drug Events
Résumé
Adverse drug events (ADEs) automatic detection from social media posts has become an important task for healthcare systems with real-world, patient-collected data.The current work deals with ADE on user generated content for Task 1 of the Social Media Mining for Health Research and Applications Workshop (SMM4H 2026), Creative Catalysts.We fine-tuned XLM-RoBERTa, pre-trained model chosen for its robustness in handling multilingual content and linguistic diversity common in social media text.To better handle the class imbalance, we subsequently implemented a classweighting strategy to increase the model's focus on the underrepresented positive class.This adjusted model improved the validation F1score to 65%.Our results demonstrate the effectiveness of transformer-based architectures for ADE detection while highlighting the critical need for robust class-balancing techniques and multilingual generalization to handle realworld, imbalanced social media data.
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