Abstract
Social media has grown into an increasingly popular means of disseminating information. Its massive growth has given evolution to fake news in misinformation and rumors, spreading very quickly. These days the generation of fake news is not only limited to the traditional method but is also extended to deep learning-based methods. The characteristics of fake news generated from these algorithms are very much identical to original news, which makes existing supervised machine learning algorithms difficult to detect these machine-generated fake news. Motivated by the problem, we have brought a fully unsupervised approach based on Autoencoder and GAN. With the help of an autoencoder, we have generated the high dimensional feature vector of news sentences which is later used by generators in GAN to create machine-generated fake news. The generated fake news is then identified with the real news with the help of a discriminator. We have tested our approach with the news dataset that contains about 30,000 news headlines. The obtained experimental results suggest that our approach is very reliable and can be very helpful in automating fake news detection.
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Acknowledegment
This research was partially funded by the Spanish Government Ministry of Science and Innovation through the AVisSA project grant number (PID2020-118345RB-I00).
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Bhardwaj, P., Yadav, K., Alsharif, H., Aboalela, R.A. (2023). GAN-Based Unsupervised Learning Approach to Generate and Detect Fake News. In: Nedjah, N., MartÃnez Pérez, G., Gupta, B.B. (eds) International Conference on Cyber Security, Privacy and Networking (ICSPN 2022). ICSPN 2021. Lecture Notes in Networks and Systems, vol 599. Springer, Cham. https://doi.org/10.1007/978-3-031-22018-0_37
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DOI: https://doi.org/10.1007/978-3-031-22018-0_37
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