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.

This paper provides an overview of Task 6 from the Social Media Mining for Health/Health Real-World Data shared task (#SMM4H-HeaRD 2026), which focused on predicting TNM staging from pathology reports from TCGA. Seven teams submitted systems spanning fine-tuned clinical encoders, open-source generative LLMs, and closed-source API models. On a straightforward test set, most teams achieved near-perfect F1 scores (average 0.993, 0.972, and 0.957 for T, N, and M). However, on a harder tiebreak set where explicit TNM notation was removed and staging had to be inferred from clinical descriptions, performance dropped substantially (average 0.725, 0.783, and 0.846). Notably, the two teams using large closed-source API models generalized best to the harder set, achieving the highest T and N scores despite not leading on the easy set. These results suggest that while fine-tuned domain-specific encoders excel at surface-level extraction, larger general-purpose LLMs may be more robust when staging must be inferred from contextual clinical findings. All teams surpassed baseline overall performance on both test sets.

SMM4H 2024 Task 1 is focused on the identification of standardized Adverse Drug Events (ADEs) in tweets. We introduce a novel Retrieval-Augmented Generation (RAG) method, leveraging the capabilities of Llama 3, GPT-4, and the SFR-embedding-mistral model, along with few-shot prompting techniques, to map colloquial tweet language to MedDRA Preferred Terms (PTs) without relying on extensive training datasets. Our method achieved competitive performance, with an F1 score of 0.359 in the normalization task and 0.392 in the named entity recognition (NER) task. Notably, our model demonstrated robustness in identifying previously unseen MedDRA PTs (F1=0.363) greatly surpassing the median task score of 0.141 for such terms.