Social media bot detection using Dropout-GAN

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F24%3A00375930" target="_blank" >RIV/68407700:21240/24:00375930 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s11416-024-00521-5" target="_blank" >https://doi.org/10.1007/s11416-024-00521-5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11416-024-00521-5" target="_blank" >10.1007/s11416-024-00521-5</a>

Result language
angličtina
Original language name
Social media bot detection using Dropout-GAN
Original language description
Bot activity on social media platforms is a pervasive problem, undermining the credibility of online discourse and potentially leading to cybercrime. We propose an approach to bot detection using Generative Adversarial Networks (GAN). We discuss how we overcome the issue of mode collapse by utilizing multiple discriminators to train against one generator, while decoupling the discriminator to perform social media bot detection and utilizing the generator for data augmentation. In terms of classification accuracy, our approach outperforms the state-of-the-art techniques in this field. We also show how the generator in the GAN can be used to evade such a classification technique.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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