@inproceedings{afren-etal-2026-creative,
    title = "Creative Catalysts at {\#}{SMM}4{H}-{H}ea{RD} 2026: {XLM}-{R}o{BERT}a for Task 1 Binary Classification of Social Media Posts Containing Adverse Drug Events",
    author = "Afren, Radja  and
      Rahab, Hichem  and
      Guellil, Imane",
    editor = "Lopez-Garcia, Guillermo  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the 11th Social Media Mining for Health Research and Applications ({SMM}4{H}-{H}ea{RD} 2026) Workshop and Shared Tasks",
    month = jul,
    year = "2026",
    address = "San Diego, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.smm4h-1.40/",
    pages = "246--251",
    ISBN = "979-8-89176-432-3",
    abstract = "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 class-weighting strategy to increase the model{'}s focus on the underrepresented positive class. This adjusted model improved the validation F1-score 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 real-world, imbalanced social media data."
}