@inproceedings{sathvik-etal-2025-f2,
    title = "F$^2$ ({F}uture{F}iction): Detection of Fake News on Futuristic Technology",
    author = "Sathvik, Msvpj  and
      Velugubantla, Venkatesh  and
      Potla, Ravi Teja",
    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/moar-dois/2025.dravidianlangtech-1.46/",
    doi = "10.18653/v1/2025.dravidianlangtech-1.46",
    pages = "264--272",
    ISBN = "979-8-89176-228-2",
    abstract = "There is widespread of misinformation on futuristic technology and society. To accurately detect such news, the algorithms require up-to-date knowledge. The Large Language Models excel in the NLP but cannot retrieve the ongoing events or innovations. For example, GPT and it{'}s variants are restricted till the knowledge of 2021. We introduce a new methodology for the identification of fake news pertaining to futuristic technology and society. Leveraging the power of Google Knowledge, we enhance the capabilities of the GPT-3.5 language model, thereby elevating its performance in the detection of misinformation. The proposed framework exhibits superior efficacy compared to established baselines with the accuracy of 81.04{\%}. Moreover, we propose a novel dataset consisting of fake news in three languages English, Telugu and Tenglish of around 21000 from various sources."
}