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@article{160663,
        author = {Snehal More},
        title = {ENHANCING THE CREDIBILITY OF SOCIAL MEDIA USING BERT MODEL},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {1},
        pages = {968-972},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=160663},
        abstract = {By utilising transfer learning and the BERT (Bidirectional Encoder Representations from Transformers) model for the detection of Fake news, this research effort seeks to increase the trustworthiness of social media. Two CSV files, one with 21,416 real articles and the other with 23,480 fraudulent articles, make up the dataset. Each article has a title, a body of text, a date, and a subject. The subjects are divided into two categories: universal (47%) and political (53%). We want to increase the BERT model's ability to accurately detect bogus news on social media platforms. The findings and revelations from this research aid in the creation of practical strategies for thwarting false information, promoting a more reliable social media ecosystem},
        keywords = {Transfer Learning, Pre-trained BERT Model, Natural Language Processing},
        month = {},
        }