Abstract
People have indeed been increasingly exposed to fake information and fake news in recent years as a result of the growing usage of Web-based media and broadcasting organizations all over the planet, all of which have harmful effects on both collective views and government policies. Twitter is a popular microblog where individual expresses their thoughts on current events. The quality of data should be ensured by obtaining it from trusted sources. We compared with the performances of a BERT embedding with classical embeddings such as bag of words, TF-IDF, TF-IDF with SVD, and TF-IDF with NMF. Similar comparison was done with Google word embedding (skip gram and cbow) too. We used Python to boil a highly proficient forecast model, and we developed and assessed the order of the model utilizing execution measures; before testing the representation on the bunch of unspecified on COVID-19, there was a lot of fake news foresee a text arrangement, and each piece of fake news has its own classification.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Devika, S.P., Pooja, M.R., Arpitha, M.S., Ravi, V. (2023). BERT Transformer-Based Fake News Detection in Twitter Social Media. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Izonin, I. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-6004-8_8
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DOI: https://doi.org/10.1007/978-981-19-6004-8_8
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