Using Improved Conditional Generative Adversarial Networks to Detect Social Bots on Twitter
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The detection and removal of malicious social bots in social networks has become an area of interest in industry and academia. The widely used bot detection method based on machine learning leads to an imbalance in the number of samples in different categories. Classifier bias leads to a low detection rate of minority samples. Therefore, we propose an improved conditional generative adversarial network (improved CGAN) to extend imbalanced data sets before applying training classifiers to improve the detection accuracy of social bots. To generate an auxiliary condition, we propose a modified clustering algorithm, namely, the Gaussian kernel density peak clustering algorithm (GKDPCA), which avoids the generation of data-augmentation noise and eliminates imbalances between and within social bot class distributions. Furthermore, we improve the CGAN convergence judgment condition by introducing the Wasserstein distance with a gradient penalty, which addresses the model collapse and gradient disappearance in the traditional CGAN. Three common oversampling algorithms are compared in experiments. The effects of the imbalance degree and the expansion ratio of the original data on oversampling are studied, and the improved CGAN performs better than the others. Experimental results comparing with three common oversampling algorithms show that the improved CGAN achieves the higher evaluation scores in terms of F1-score, G-mean and AUC.
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A GAN trained in [33] for creating adversarial examples of social bots improved the training of downstream detectors.
“A notable example of this kind is represented by the GAN trained in [33] for creating adversarial examples of social bots that improved the training of downstream detectors.”
From A decade of social bot detection (2020) · cites this paper as [33]
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A decade of social bot detection
Cresci, S. (2020) · Communications of the ACM · doi:10.1145/3409116 · Cited by 323 · see citing works
“two networks. Originally, GANs were proposed as a form of generative model – that is, the focus was posed on the generator network. A notable example of this kind is represented by the GAN trained in [33] for creating adversarial examples of social bots that improved the training of downstream detectors. However, with…”
Background
- Authors
- Wu, B., Liu, L., Yang, Y., Zheng, K., & Wang, X.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- Beijing University of Technology (CN)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 36664-36680
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3006859404
- Cited by
- 54
- References
- 76
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 8 · 2023: 13 · 2022: 10 · 2021: 9 · 2020: 4
- FWCI
- 8.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Basic Research Program of China (2017YFB0802703)
- Natural Science Foundation of Beijing Municipality (4202002)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- social bot detection, Twitter, conditional generative adversarial networks, data augmentation, class imbalance, model collapse, minority class samples
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