Work

Cuet_Data_Wizards at #SMM4H-HeaRD 2026: Multilingual ADE Detection and Influenza Vaccine Effectiveness Estimation from Social Media

Year: 2026

Type: conference-paper

Abstract: 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 crossentropy loss and augment low-resource settings with additional CADEC data and machinetranslation-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.

Source: Social Media Mining for Health Research and Applications Workshop and Shared Tasks (SMM4H)

DOI: 10.18653/v1/2026.smm4h-1.36

Authors: Abir Dey, Mohammed Omar Faiaz, Muhammad Ibrahim Khan

Institution: Chittagong University of Engineering & Technology

Language: English

Topic: Pharmacovigilance and Adverse Drug Reactions

Subfield: Toxicology

Field: Pharmacology, Toxicology and Pharmaceutics

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W7166889810