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SMM4H 2024 : The 9th Social Media Mining for Health Research and Applications Workshop and Shared Tasks — Large Language Models (LLMs) and Generalizability for Social Media NLP


When Aug 15, 2024 - Aug 15, 2024
Where Bankok, Thailand
Submission Deadline May 17, 2024
Notification Due Jun 17, 2024
Final Version Due Jul 1, 2024
Categories    social media   NLP   health informatics   large language model

Call For Papers

#SMM4H-LLM 2024: The 9th Social Media Mining for Health Research and Applications Workshop and Shared Tasks -- Large Language Models and Generalizability for Social Media NLP @ACL 2024

When: Aug. 15, 2024
Where: Bankok, Thailand
Submission deadline: May 17, 2024
Submission website:

The 9th Social Media Mining for Health Research and Applications (#SMM4H) Workshop, co-located at ACL 2024, serves as a unique venue for bringing together researchers interested in developing and sharing NLP methods that enable the systematic use of SM data for health research. #SMM4H-2024 workshop and shared tasks have a special focus on Large Language Models (LLMs) and Generalizability for Social Media NLP. A variety of LLMs and their emerging capabilities promise the creation of a generalist artificial agent (Moor et al., 2023; Qin et al., 2023) capable of transferring knowledge acquired during training on massive corpora to solve unseen tasks on user-generated data. We seek to motivate such progress, benefiting from the ‘wisdom of the masses’ as reflected in SM, particularly in the realm of personal health.

The 9th #SMM4H Workshop invites the submission of papers on original, completed, and unpublished research. NLP topics of interest to our workshop include:
• Information retrieval methods for obtaining relevant SM data
• Annotation schemes and evaluation techniques for health-related texts in SM
• Classifying health-related texts in SM
• Methods for the automatic detection, extraction, and normalization of health-related concept mentions in SM data
• Semantic methods in SM analysis
• Domain adaptation and transfer learning techniques for health-related texts in SM

Shared Task
In 2024, #SMM4H is also organizing 7 shared tasks: participants will be provided with annotated training and validation data to develop their systems, followed by 7 days during which they will run their systems on unlabeled test data and upload their predictions to CodaLab. The individual CodaLab site for each task can be found from the above link.

Please use this form ( to register. When your registration is approved, you will be invited to a Google group, where the data sets will be made available. Registered teams are required to submit a paper describing their systems. System descriptions may consist of up to 4 pages and must follow the ACL formatting.

Task 1: Extraction and normalization of adverse drug events (ADEs) in English tweets.
Task 2: Cross-Lingual Few-Shot Relation Extraction for Pharmacovigilance in French, German, and Japanese
Task 3: Multi-class classification of effects of outdoor spaces on social anxiety symptoms in Reddit.
Task 4: Extraction of the clinical and social impacts of nonmedical substance use from Reddit.
Task 5: Binary classification of English tweets reporting children’s medical disorders.
Task 6: Self-reported exact age classification with cross-platform evaluation in English
Task 7: Identification of LLM or human domain-expert data annotations in the context of health-related applications.

Important Dates
Training data available Jan 10, 2024
CodaLab Available Jan 17, 2024
Test data available Apr 17, 2024
Evaluation end Apr 24, 2024
System description paper due May 17, 2024
Paper acceptance notification June 17, 2024
Camera-ready papers due July 1, 2024

Graciela Gonzalez-Hernandez, Cedars-Sinai Medical Center, USA
Dongfang Xu, Cedars-Sinai Medical Center, USA
Ivan Flores, Cedars-Sinai Medical Center, USA
Davy Weissenbacher, Cedars-Sinai Medical Center, USA
Ari Z. Klein, University of Pennsylvania, USA
Karen O'Connor, University of Pennsylvania , USA
Abeed Sarker, Emory University, USA
Yao Ge, Emory University, USA
Juan M. Banda, Stanford Health Care, USA
Raul Rodriguez-Esteban, Roche Pharmaceuticals, Switzerland
Lucia Schmidt, Roche Pharmaceuticals, Switzerland
Lisa Raithel, Technical University of Berlin, Germany
Pierre Zweigenbaum, Université Paris-Saclay, France
Roland Roller, German Research Center for Artificial Intelligence, Germany
Philippe Thomas, German Research Center for Artificial Intelligence, Germany
Eiji Aramaki, NAIST, Japan
Shuntaro Yada, NAIST, Japan

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