@inproceedings{mahajan-s-2024-halelab,
    title = "{H}ale{L}ab{\_}{NITK}@{SMM}4{H}{'}24: Binary classification of {E}nglish tweets reporting children{'}s medical disorders",
    author = "Mahajan, Ritik  and
      S., Sowmya",
    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://preview.aclanthology.org/ingest-emnlp/2024.smm4h-1.31/",
    pages = "133--135",
    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."
}