@inproceedings{sankar-etal-2024-iitroorkee,
    title = "{IITR}oorkee@{SMM}4{H} 2024 Cross-Platform Age Detection in {T}witter and {R}eddit Using Transformer-Based Model",
    author = "Sankar, Thadavarthi  and
      Suraj, Dudekula  and
      Reddy, Mallamgari  and
      Toshniwal, Durga  and
      Agarwal, Amit",
    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/fix-sig-urls/2024.smm4h-1.23/",
    pages = "101--105",
    abstract = "This paper outlines the methodology for the automatic extraction of self-reported ages from social media posts as part of the Social Media Mining for Health (SMM4H) 2024 Workshop Shared Tasks. The focus was on Task 6: ``Self-reported exact age classification with cross-platform evaluation in English.'' The goal was to accurately identify age-related information from user-generated content, which is crucial for applications in public health monitoring, targeted advertising, and demographic research. A number of transformer-based models were employed, including RoBERTa-Base, BERT-Base, BiLSTM, and Flan T5 Base, leveraging their advanced capabilities in natural language understanding. The training strategies included fine-tuning foundational pre-trained language models and evaluating model performance using standard metrics: F1-score, Precision, and Recall. The experimental results demonstrated that the RoBERTa-Base model significantly outperformed the other models in this classification task. The best results achieved with the RoBERTa-Base model were an F1-score of 0.878, a Precision of 0.899, and a Recall of 0.858."
}