@inproceedings{lopez-garcia-etal-2026-overview,
    title = "Overview of the 11th Social Media Mining for Health ({\#}{SMM}4{H}) and Health Real-World Data ({H}ea{RD}) Shared Tasks at {ACL} 2026",
    author = "Lopez-Garcia, Guillermo  and
      Acitores Cortina, Jose Miguel  and
      Berkowitz, Jacob  and
      Chan, Joey  and
      Dey, Sumon Kanti  and
      Flores Amaro, Ivan  and
      Gallego, Fernando  and
      Gryboski, Lauren  and
      Klein, Ari Z.  and
      Zeidi Kolehparcheh, Farnoush  and
      Krallinger, Martin  and
      Lima-Lopez, Salvador  and
      Ma, Yujun  and
      Nishiyama, Tomohiro  and
      Rezaie Mianroodi, Ahmad  and
      Rezaie Mianroodi, Amirali  and
      Raithel, Lisa  and
      Roller, Roland  and
      Rosell, Judith  and
      Rudzicz, Frank  and
      Sarker, Abeed  and
      Tatonetti, Nicholas  and
      Thomas, Philippe  and
      Tutubalina, Elena  and
      Xu, Dongfang  and
      Zeidi, Farnaz  and
      Zhai, Yu  and
      Zweigenbaum, Pierre  and
      Gonzalez-Hernandez, Graciela",
    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-1.53/",
    pages = "353--369",
    ISBN = "979-8-89176-432-3",
    abstract = "The aim of the Social Media Mining for Health Applications and Health Real-World Data ({\#}SMM4H-HeaRD) shared tasks is to fos- ter the development and evaluation of natural language processing, machine learning, and artificial intelligence methods for analyzing health-related text from social media and other real-world data sources. For the 11th iteration, held online and co-located with ACL 2026, the workshop continued the expanded {\#}SMM4H- HeaRD platform initiated in 2025, broaden-ing its scope beyond social media to include additional health real-world data sources such as clinical narratives and biomedical literature. The 8 shared tasks covered diverse data sources, health domains (e.g., adverse drug events, insomnia, influenza vaccine effectiveness, cancer staging, substance use), and task formulations (e.g., classification, named entity recognition, span extraction, and text generation). In total, 110 teams registered, representing 31 countries. In this paper, we present an overview of the datasets, participant systems, and performance results, providing insights into current methods for mining social media and health real-world data for biomedical and clinical applications."
}