@inproceedings{haq-etal-2025-kcrl,
    title = "{KCRL}@{D}ravidian{L}ang{T}ech 2025: Multi-Pooling Feature Fusion with {XLM}-{R}o{BERT}a for {M}alayalam Fake News Detection and Classification",
    author = "Haq, Fariha  and
      Shawon, Md. Tanvir Ahammed  and
      Mia, Md Ayon  and
      Md. Mursalin, Golam Sarwar  and
      Khan, Muhammad Ibrahim",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Sherly, Elizabeth  and
      Rajiakodi, Saranya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Cn, Subalalitha  and
      Chinnappa, Dhivya",
    booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
    month = may,
    year = "2025",
    address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.dravidianlangtech-1.107/",
    pages = "624--629",
    ISBN = "979-8-89176-228-2",
    abstract = "The rapid spread of misinformation on social media platforms necessitates robust detection mechanisms, particularly for languages with limited computational resources. This paper presents our system for the DravidianLangTech 2025 shared task on Fake News Detection in Malayalam YouTube comments, addressing both binary and multiclass classification challenges. We propose a Multi-Pooling Feature Fusion (MPFF) architecture that leverages [CLS] + Mean + Max pooling strategy with transformer models. Our system demonstrates strong performance across both tasks, achieving a macro-averaged F1 score of 0.874, ranking 6th in binary classification, and 0.628, securing 1st position in multiclass classification. Experimental results show that our MPFF approach with XLM-RoBERTa significantly outperforms traditional machine learning and deep learning baselines, particularly excelling in the more challenging multiclass scenario. These findings highlight the effectiveness of our methodology in capturing nuanced linguistic features for fake news detection in Malayalam, contributing to the advancement of automated verification systems for Dravidian languages."
}