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TwitterGAN: robust spam detection in twitter using novel generative adversarial networks

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Abstract

As social media platforms like Twitter continue to evolve, the proliferation of spam content has become a pressing issue, undermining the credibility of shared messages. Traditional spam detection methods, such as black-and-white listing and rule-based learning techniques, struggle to efficiently handle large datasets and adapt to dynamic environments. To address these challenges, we propose a novel spam detection model that leverages generative learning techniques, offering improved performance on vast datasets and changing circumstances. Using a substantial Twitter dataset with an 80% training and 20% testing split, our innovative model demonstrates remarkable effectiveness. Experimental results show a G-Loss score of 8.1207, significantly outperforming the D-Loss score of 0.0081, indicating the model’s exceptional accuracy and low error rate. Consequently, our groundbreaking approach emerges as a highly promising solution for real-world spam identification, raising the bar for spam detection research.

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Data availability

The dataset comes from the NSClab/Resources Twitter Spam. (http://nsclab.org/nsclab/resources/).

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Authors and Affiliations

  1. Universitas Respati Yogyakarta, Yogyakarta, Indonesia

    Mohammad Diqi

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  1. Mohammad Diqi
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Correspondence to Mohammad Diqi.

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Diqi, M. TwitterGAN: robust spam detection in twitter using novel generative adversarial networks. Int. j. inf. tecnol. 15, 3103–3111 (2023). https://doi.org/10.1007/s41870-023-01352-1

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