Overview of the 9th Social Media Mining for Health Applications (#SMM4H) Shared Tasks at ACL 2024 – Large Language Models and Generalizability for Social Media NLP - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

Overview of the 9th Social Media Mining for Health Applications (#SMM4H) Shared Tasks at ACL 2024 – Large Language Models and Generalizability for Social Media NLP

Dongfang Xu
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Guillermo Lopez-Garcia
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Karen O’connor
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Sai Tharuni Samineni
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Yao Ge
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Swati Rajwal
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Sudeshna Das
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Abeed Sarker
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Ari Klein
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Vishakha Sharma
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Juan M. Banda
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Ivan Flores Amaro
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Davy Weissenbacher
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Graciela Gonzalez-Hernandez
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Résumé

For the past nine years, the Social Media Mining for Health Applications (#SMM4H) shared tasks have promoted community-driven development and evaluation of advanced natural language processing systems to detect, extract, and normalize health-related information in publicly available user-generated content. This year, #SMM4H included seven shared tasks in English, Japanese, German, French, and Spanish from Twitter, Reddit, and health forums. A total of 84 teams from 22 countries registered for #SMM4H, and 45 teams participated in at least one task. This represents a growth of 180% and 160% in registration and participation, respectively, compared to the last iteration. This paper provides an overview of the tasks and participating systems. The data sets remain available upon request, and new systems can be evaluated through the post-evaluation phase on CodaLab.
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Dates et versions

hal-04781745 , version 1 (13-11-2024)

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  • HAL Id : hal-04781745 , version 1

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Dongfang Xu, Guillermo Lopez-Garcia, Lisa Raithel, Roland Roller, Philippe Thomas, et al.. Overview of the 9th Social Media Mining for Health Applications (#SMM4H) Shared Tasks at ACL 2024 – Large Language Models and Generalizability for Social Media NLP. The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, Association for Computational Linguistics, Aug 2024, Bangkok, Thailand. pp.183-195. ⟨hal-04781745⟩
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