Abstract
The use of Generative Adversarial Networks is almost traditional in creating synthetic images for medical purposes. They are probably the best use of GANs until now, as their results can easily be checked by the eye of specialists. In fake news detection models, we have seen lately that neural models (and deep learning) can provide a considerable improvement from standard classifiers. Yet, the most problematic problem still is the lack of data, mostly fake news data to feed these models. In this paper, we address that by proposing the use of a GAN. Results show a better capacity to generalize when used for training an extended dataset based on synthetic samples created by this GAN.
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Notes
- 1.
The input space of the generator is often called the latent space.
- 2.
The categories of both user_verified and contains_profanity were yes or no, but we have encoded them with numbers so as to then feed then to the models. Hence, 0 represents no, and 1, yes.
- 3.
A few more examples of well known GAN architectures are BigGAN, StyleGAN, or CycleGAN, but there are a whole lot more, and endless possibilities to develop new architectures.
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This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020.
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Vaz, B., Bernardes, V., Figueira, Á. (2022). On Creation of Synthetic Samples from GANs for Fake News Identification Algorithms. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. Lecture Notes in Networks and Systems, vol 470. Springer, Cham. https://doi.org/10.1007/978-3-031-04829-6_28
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