Abstract
The Generative Adversarial Network (GAN) has made incredible progress in creating sensible synthetic information. The authors propose a structure for creating convincing text through adversarial training. It enables the creation of new sentences whilst maintaining the semantics and syntax of genuine phrases whilst being possibly unique from any of the models used to evaluate the model. The authors propose an adversarial process between a discriminator and a generator. The discriminator’s goal is to detect false samples created by the generator from those which are genuine. The target tries to comprise a generator, that practically maps tests from guaranteed (straightforward) earlier appropriation, to synthetic information that seem, by all accounts, to be sensible. In this paper, the authors present various classifiers and test them based on various performance metrics and develop a suitable model to test the authenticity of tweets.
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Jockim, J.M., Meghana, K., Karthika, S. (2023). Generative Adversarial Networks and Their Application in Fake Tweet Detection. In: Ranganathan, G., Fernando, X., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 383. Springer, Singapore. https://doi.org/10.1007/978-981-19-4960-9_48
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DOI: https://doi.org/10.1007/978-981-19-4960-9_48
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