@inproceedings{xu-etal-2024-overview-9th,
title = "Overview of the 9th Social Media Mining for Health Applications ({\#}{SMM}4{H}) Shared Tasks at {ACL} 2024 {--} Large Language Models and Generalizability for Social Media {NLP}",
author = "Xu, Dongfang and
Garcia, Guillermo and
Raithel, Lisa and
Thomas, Philippe and
Roller, Roland and
Aramaki, Eiji and
Wakamiya, Shoko and
Yada, Shuntaro and
Zweigenbaum, Pierre and
O{'}Connor, Karen and
Samineni, Sai and
Hernandez, Sophia and
Ge, Yao and
Rajwal, Swati and
Das, Sudeshna and
Sarker, Abeed and
Klein, Ari and
Schmidt, Ana and
Sharma, Vishakha and
Rodriguez-Esteban, Raul and
Banda, Juan and
Amaro, Ivan and
Weissenbacher, Davy and
Gonzalez-Hernandez, Graciela",
editor = "Xu, Dongfang and
Gonzalez-Hernandez, Graciela",
booktitle = "Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.smm4h-1.40",
pages = "183--195",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T 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
%A Xu, Dongfang
%A Garcia, Guillermo
%A Raithel, Lisa
%A Thomas, Philippe
%A Roller, Roland
%A Aramaki, Eiji
%A Wakamiya, Shoko
%A Yada, Shuntaro
%A Zweigenbaum, Pierre
%A O’Connor, Karen
%A Samineni, Sai
%A Hernandez, Sophia
%A Ge, Yao
%A Rajwal, Swati
%A Das, Sudeshna
%A Sarker, Abeed
%A Klein, Ari
%A Schmidt, Ana
%A Sharma, Vishakha
%A Rodriguez-Esteban, Raul
%A Banda, Juan
%A Amaro, Ivan
%A Weissenbacher, Davy
%A Gonzalez-Hernandez, Graciela
%Y Xu, Dongfang
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F xu-etal-2024-overview-9th
%X 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.
%U https://aclanthology.org/2024.smm4h-1.40
%P 183-195
Markdown (Informal)
[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](https://aclanthology.org/2024.smm4h-1.40) (Xu et al., SMM4H-WS 2024)
ACL
- Dongfang Xu, Guillermo Garcia, Lisa Raithel, Philippe Thomas, Roland Roller, Eiji Aramaki, Shoko Wakamiya, Shuntaro Yada, Pierre Zweigenbaum, Karen O’Connor, Sai Samineni, Sophia Hernandez, Yao Ge, Swati Rajwal, Sudeshna Das, Abeed Sarker, Ari Klein, Ana Schmidt, Vishakha Sharma, et al.. 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. In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, pages 183–195, Bangkok, Thailand. Association for Computational Linguistics.