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        <title>Creative Catalysts at #SMM4H-HeaRD 2026: XLM-RoBERTa for Task 1 Binary Classification of Social Media Posts Containing Adverse Drug Events</title>
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            <namePart type="given">Guillermo</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Creative Catalysts at #SMM4H-HeaRD 2026: XLM-RoBERTa for Task 1 Binary Classification of Social Media Posts Containing Adverse Drug Events
%A Afren, Radja
%A Rahab, Hichem
%A Guellil, Imane
%Y Lopez-Garcia, Guillermo
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, United States
%@ 979-8-89176-432-3
%F afren-etal-2026-creative
%X 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.
%U https://aclanthology.org/2026.smm4h-1.40/
%P 246-251

Markdown (Informal)

[Creative Catalysts at #SMM4H-HeaRD 2026: XLM-RoBERTa for Task 1 Binary Classification of Social Media Posts Containing Adverse Drug Events](https://aclanthology.org/2026.smm4h-1.40/) (Afren et al., SMM4H 2026)

• Creative Catalysts at #SMM4H-HeaRD 2026: XLM-RoBERTa for Task 1 Binary Classification of Social Media Posts Containing Adverse Drug Events (Afren et al., SMM4H 2026)

ACL

• Radja Afren, Hichem Rahab, and Imane Guellil. 2026. Creative Catalysts at #SMM4H-HeaRD 2026: XLM-RoBERTa for Task 1 Binary Classification of Social Media Posts Containing Adverse Drug Events. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 246–251, San Diego, United States. Association for Computational Linguistics.