@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Overview of the 11th Social Media Mining for Health (#SMM4H) and Health Real-World Data (HeaRD) Shared Tasks at ACL 2026
%A Lopez-Garcia, Guillermo
%A Acitores Cortina, Jose Miguel
%A Berkowitz, Jacob
%A Chan, Joey
%A Dey, Sumon Kanti
%A Flores Amaro, Ivan
%A Gallego, Fernando
%A Gryboski, Lauren
%A Klein, Ari Z.
%A Zeidi Kolehparcheh, Farnoush
%A Krallinger, Martin
%A Lima-Lopez, Salvador
%A Ma, Yujun
%A Nishiyama, Tomohiro
%A Rezaie Mianroodi, Ahmad
%A Rezaie Mianroodi, Amirali
%A Raithel, Lisa
%A Roller, Roland
%A Rosell, Judith
%A Rudzicz, Frank
%A Sarker, Abeed
%A Tatonetti, Nicholas
%A Thomas, Philippe
%A Tutubalina, Elena
%A Xu, Dongfang
%A Zeidi, Farnaz
%A Zhai, Yu
%A Zweigenbaum, Pierre
%A Gonzalez-Hernandez, Graciela
%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 lopez-garcia-etal-2026-overview
%X 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.
%U https://aclanthology.org/2026.smm4h-1.53/
%P 353-369
Markdown (Informal)
[Overview of the 11th Social Media Mining for Health (#SMM4H) and Health Real-World Data (HeaRD) Shared Tasks at ACL 2026](https://aclanthology.org/2026.smm4h-1.53/) (Lopez-Garcia et al., SMM4H 2026)
ACL
- Guillermo Lopez-Garcia, Jose Miguel Acitores Cortina, Jacob Berkowitz, Joey Chan, Sumon Kanti Dey, Ivan Flores Amaro, Fernando Gallego, Lauren Gryboski, Ari Z. Klein, Farnoush Zeidi Kolehparcheh, Martin Krallinger, Salvador Lima-Lopez, Yujun Ma, Tomohiro Nishiyama, Ahmad Rezaie Mianroodi, Amirali Rezaie Mianroodi, Lisa Raithel, Roland Roller, Judith Rosell, Frank Rudzicz, Abeed Sarker, Nicholas Tatonetti, Philippe Thomas, Elena Tutubalina, Dongfang Xu, Farnaz Zeidi, Yu Zhai, Pierre Zweigenbaum, and Graciela Gonzalez-Hernandez. 2026. Overview of the 11th Social Media Mining for Health (#SMM4H) and Health Real-World Data (HeaRD) Shared Tasks at ACL 2026. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 353–369, San Diego, United States. Association for Computational Linguistics.