@inproceedings{m-etal-2024-techwhiz,
    title = "{T}ech{W}hiz@{D}ravidian{L}ang{T}ech 2024: Fake News Detection Using Deep Learning Models",
    author = "M, Madhumitha  and
      M, Kunguma  and
      J, Tejashri  and
      C, Jerin Mahibha",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Sherly, Elizabeth  and
      Nadarajan, Rajeswari  and
      Ravikiran, Manikandan",
    booktitle = "Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
    month = mar,
    year = "2024",
    address = "St. Julian's, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.dravidianlangtech-1.33/",
    pages = "200--204",
    abstract = "The ever-evolving landscape of online social media has initiated a transformative phase in communication, presenting unprecedented opportunities alongside inherent challenges. The pervasive issue of false information, commonly termed fake news, has emerged as a significant concern within these dynamic platforms. This study delves into the domain of Fake News Detection, with a specific focus on Malayalam. Utilizing advanced transformer models like mBERT, ALBERT, and XMLRoBERTa, our research proficiently classifies social media text into original or fake categories. Notably, our proposed model achieved commendable results, securing a rank of 3 in Task 1 with macro F1 scores of 0.84 using mBERT, 0.56 using ALBERT, and 0.84 using XMLRoBERTa. In Task 2, the XMLRoBERTa model excelled with a rank of 12, attaining a macro F1 score of 0.21, while mBERT and BERT achieved scores of 0.16 and 0.11, respectively. This research aims to develop robust systems capable of discerning authentic from deceptive content, a crucial endeavor in maintaining information reliability on social media platforms amid the rampant spread of misinformation."
}