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Social bot detection on Twitter: robustness evaluation and improvement

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Abstract

Online social networks are easily exploited by social bots. Although the current models for detecting social bots show promising results, they mainly rely on Graph Neural Networks (GNNs), which have been proven to have vulnerabilities in robustness and these detection models likely have similar robustness vulnerabilities. Therefore, it is crucial to evaluate and improve their robustness. This paper proposes a robustness evaluation method: Attribute Random Iteration-Fast Gradient Sign Method (ARI-FGSM) and uses a simplified adversarial training to improve the robustness of social bot detection. Specifically, this study performs robustness evaluations of five bot detection models on two datasets under both black-box and white-box scenarios. The white-box experiments achieve a minimum attack success rate of 86.23%, while the black-box experiments achieve a minimum attack success rate of 45.86%. This shows that the social bot detection model is vulnerable to adversarial attacks. Moreover, after executing our robustness improvement method, the robustness of the detection model increased by up to 86.98%.

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Data Availability

No datasets were generated or analysed during the current study.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. U21B2024, 62202329).

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Authors and Affiliations

  1. Tianjin University, Tianjin, 300072, China

    Anan Liu, Yanwei Xie & Lanjun Wang

  2. State Key Laboratory of Communication Content Cognition, Beijing, 100026, China

    Guoqing Jin, Junbo Guo & Jun Li

Authors
  1. Anan Liu
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  2. Yanwei Xie
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  3. Lanjun Wang
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  4. Guoqing Jin
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  6. Jun Li
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Contributions

Material preparation, data collection, and analysis were performed by JL, GJ and JG. Conceptualization and methodology were performed by AL, YX and LW. The first draft of the manuscript was written by YX and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Lanjun Wang.

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Communicated by B. Bao.

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Liu, A., Xie, Y., Wang, L. et al. Social bot detection on Twitter: robustness evaluation and improvement. Multimedia Systems 30, 167 (2024). https://doi.org/10.1007/s00530-024-01364-2

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