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        <title>HaleLab_NITK@SMM4H’24: Binary classification of English tweets reporting children’s medical disorders</title>
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        <namePart type="given">Ritik</namePart>
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            <namePart type="given">Dongfang</namePart>
            <namePart type="family">Xu</namePart>
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    <abstract>This paper describes the work undertaken as part of the SMM4H-2024 shared task, specifically Task 5, which involves the binary classification of English tweets reporting children’s medical disorders. The primary objective is to develop a system capable of automatically identifying tweets from users who report their pregnancy and mention children with specific medical conditions, such as attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), delayed speech, or asthma, while distinguishing them from tweets that merely reference a disorder without much context. Our approach leverages advanced natural language processing techniques and machine learning algorithms to accurately classify the tweets. The system achieved an overall F1-score of 0.87, highlighting its robustness and effectiveness in addressing the classification challenge posed by this task.</abstract>
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%0 Conference Proceedings
%T HaleLab_NITK@SMM4H’24: Binary classification of English tweets reporting children’s medical disorders
%A Mahajan, Ritik
%A S., Sowmya
%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 mahajan-s-2024-halelab
%X This paper describes the work undertaken as part of the SMM4H-2024 shared task, specifically Task 5, which involves the binary classification of English tweets reporting children’s medical disorders. The primary objective is to develop a system capable of automatically identifying tweets from users who report their pregnancy and mention children with specific medical conditions, such as attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders (ASD), delayed speech, or asthma, while distinguishing them from tweets that merely reference a disorder without much context. Our approach leverages advanced natural language processing techniques and machine learning algorithms to accurately classify the tweets. The system achieved an overall F1-score of 0.87, highlighting its robustness and effectiveness in addressing the classification challenge posed by this task.
%U https://aclanthology.org/2024.smm4h-1.31
%P 133-135

Markdown (Informal)

[HaleLab_NITK@SMM4H’24: Binary classification of English tweets reporting children’s medical disorders](https://aclanthology.org/2024.smm4h-1.31) (Mahajan & S., SMM4H-WS 2024)

• HaleLab_NITK@SMM4H’24: Binary classification of English tweets reporting children’s medical disorders (Mahajan & S., SMM4H-WS 2024)

ACL

• Ritik Mahajan and Sowmya S.. 2024. HaleLab_NITK@SMM4H’24: Binary classification of English tweets reporting children’s medical disorders. In Proceedings of The 9th Social Media Mining for Health Research and Applications (SMM4H 2024) Workshop and Shared Tasks, pages 133–135, Bangkok, Thailand. Association for Computational Linguistics.