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• W3201565887 abstract "Spreading of automatically generated clickbaits, fake news, and fake reviews undermines the veracity of the internet as a credible source of information. We investigate the problem of recognizing automatically generated short texts by exploring different Deep Learning models. To improve the classification results, we use text augmentation techniques and classifier hyperparameter optimization. For word embedding and vectorization we use Glove and RoBERTa. We compare the performance of dense neural network, convolutional neural network, gated recurrent network, and hierarchical attention network. The experiments on the TweepFake dataset achieved an 89.7% accuracy." @default.
• W3201565887 created "2021-09-27" @default.
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• W3201565887 date "2021-01-01" @default.
• W3201565887 modified "2023-10-16" @default.
• W3201565887 title "Deep Fake Recognition in Tweets Using Text Augmentation, Word Embeddings and Deep Learning" @default.
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• W3201565887 doi "https://doi.org/10.1007/978-3-030-86979-3_37" @default.
• W3201565887 hasPublicationYear "2021" @default.
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