AAST-NLP@#SMM4H’24: Finetuning Language Models for Exact Age Classification and Effect of Outdoor Spaces on Social Anxiety

Ahmed El-Sayed, Omar Nasr, Noha Tawfik


Abstract
This paper evaluates the performance of “AAST-NLP” in the Social Media Mining for Health (SMM4H) Shared Tasks 3 and 6, where more than 20 teams participated in each. We leveraged state-of-the-art transformer-based models, including Mistral, to achieve our results. Our models consistently outperformed both the mean and median scores across the tasks. Specifically, an F1-score of 0.636 was achieved in classifying the impact of outdoor spaces on social anxiety symptoms, while an F1-score of 0.946 was recorded for the classification of self-reported exact ages.
Anthology ID:
2024.smm4h-1.25
Volume:
Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Dongfang Xu, Graciela Gonzalez-Hernandez
Venues:
SMM4H | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
110–113
Language:
URL:
https://aclanthology.org/2024.smm4h-1.25
DOI:
Bibkey:
Cite (ACL):
Ahmed El-Sayed, Omar Nasr, and Noha Tawfik. 2024. AAST-NLP@#SMM4H’24: Finetuning Language Models for Exact Age Classification and Effect of Outdoor Spaces on Social Anxiety. In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, pages 110–113, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
AAST-NLP@#SMM4H’24: Finetuning Language Models for Exact Age Classification and Effect of Outdoor Spaces on Social Anxiety (El-Sayed et al., SMM4H-WS 2024)
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PDF:
https://aclanthology.org/2024.smm4h-1.25.pdf