@inproceedings{bala-krishnamurthy-2023-abhipaw-dravidianlangtech-fake,
    title = "{A}bhi{P}aw@ {D}ravidian{L}ang{T}ech: Fake News Detection in {D}ravidian Languages using Multilingual {BERT}",
    author = "Bala, Abhinaba  and
      Krishnamurthy, Parameswari",
    editor = "Chakravarthi, Bharathi R.  and
      Priyadharshini, Ruba  and
      M, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Sherly, Elizabeth",
    booktitle = "Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages",
    month = sep,
    year = "2023",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.dravidianlangtech-1.34/",
    pages = "235--238",
    abstract = "This study addresses the challenge of detecting fake news in Dravidian languages by leveraging Google{'}s MuRIL (Multilingual Representations for Indian Languages) model. Drawing upon previous research, we investigate the intricacies involved in identifying fake news and explore the potential of transformer-based models for linguistic analysis and contextual understanding. Through supervised learning, we fine-tune the ``muril-base-cased'' variant of MuRIL using a carefully curated dataset of labeled comments and posts in Dravidian languages, enabling the model to discern between original and fake news. During the inference phase, the fine-tuned MuRIL model analyzes new textual content, extracting contextual and semantic features to predict the content{'}s classification. We evaluate the model{'}s performance using standard metrics, highlighting the effectiveness of MuRIL in detecting fake news in Dravidian languages and contributing to the establishment of a safer digital ecosystem. Keywords: fake news detection, Dravidian languages, MuRIL, transformer-based models, linguistic analysis, contextual understanding."
}