@inproceedings{singh-etal-2024-smm4h24,
title = "{SMM}4{H}{'}24 Task6 : Extracting Self-Reported Age with {LLM} and {BERT}weet: Fine-Grained Approaches for Social Media Text",
author = "Singh, Jaskaran and
Bedi, Jatin and
Kaur, Maninder",
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-1.24",
pages = "106--109",
abstract = "The paper presents two distinct approaches to Task 6 of the SMM4H{'}24 workshop: extracting self-reported exact age information from social media posts across platforms. This research task focuses on developing methods for automatically extracting self-reported ages from posts on two prominent social media platforms: Twitter (now X) and Reddit. The work leverages two ways, one Mistral-7B-Instruct-v0.2 Large Language Model (LLM) and another pre-trained language model BERTweet, to achieve robust and generalizable age classification, surpassing limitations of existing methods that rely on predefined age groups. The proposed models aim to advance the automatic extraction of self-reported exact ages from social media posts, enabling more nuanced analyses and insights into user demographics across different platforms.",
}
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%0 Conference Proceedings
%T SMM4H’24 Task6 : Extracting Self-Reported Age with LLM and BERTweet: Fine-Grained Approaches for Social Media Text
%A Singh, Jaskaran
%A Bedi, Jatin
%A Kaur, Maninder
%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 singh-etal-2024-smm4h24
%X The paper presents two distinct approaches to Task 6 of the SMM4H’24 workshop: extracting self-reported exact age information from social media posts across platforms. This research task focuses on developing methods for automatically extracting self-reported ages from posts on two prominent social media platforms: Twitter (now X) and Reddit. The work leverages two ways, one Mistral-7B-Instruct-v0.2 Large Language Model (LLM) and another pre-trained language model BERTweet, to achieve robust and generalizable age classification, surpassing limitations of existing methods that rely on predefined age groups. The proposed models aim to advance the automatic extraction of self-reported exact ages from social media posts, enabling more nuanced analyses and insights into user demographics across different platforms.
%U https://aclanthology.org/2024.smm4h-1.24
%P 106-109
Markdown (Informal)
[SMM4H’24 Task6 : Extracting Self-Reported Age with LLM and BERTweet: Fine-Grained Approaches for Social Media Text](https://aclanthology.org/2024.smm4h-1.24) (Singh et al., SMM4H-WS 2024)
ACL