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Synthetic Data Generation for Machine Learning Models with Cognitive Agent Simulations

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Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins: The PAAMS Collection (PAAMS 2024)

Abstract

The use of synthetic data for training machine learning models (ML) in social media domains can address issues such as data availability and bias, but poses challenges, including properly reflecting causal relationships and matching the consistency of real data. In this paper, we explore the benefits and limitations of using synthetic data generated by cognitive agent simulations. By simulating human interactions and social media dynamics, these models can capture constraints and nuances of real-world scenarios. We report initial experiments that show that ML algorithms trained on real data augmented with synthetic data outperform those trained solely on original data, achieving up to 25% improvement in KS distance and RMSE metrics. This approach is applied to two domain problems: predicting code quality based on open-source code discussions and detecting and countering bot attacks on social media platforms. For code quality prediction, we used discussions and patches from the Linux Kernel Mailing List to predict patch reversions. In the bot attack detection problem, synthetic Reddit data helps create realistic social network environments to study interactions between influencers and bots under different conditions. The paper presents empirical evidence supporting the effectiveness of synthetic data in improving ML model performance and introduces an agent-based framework for generating realistic synthetic data for social media experiments. The findings suggest promising avenues for future research and highlight the potential of this approach.

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References

  1. Dash agent-based modeling framework. https://github.com/isi-usc-edu/dash/

  2. Assefa, S.A., Dervovic, D., Mahfouz, M., Tillman, R.E., Reddy, P., Veloso, M.: Generating synthetic data in finance: opportunities, challenges and pitfalls. In: Proceedings of the First ACM International Conference on AI in Finance. ICAIF 2020, Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3383455.3422554

  3. van Breugel, B., Kyono, T., Berrevoets, J., van der Schaar, M.: Decaf: generating fair synthetic data using causally-aware generative networks. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol. 34, pp. 22221–22233. Curran Associates, Inc. (2021)

    Google Scholar 

  4. Chavoshi, N., Hamooni, H., Mueen, A.: Debot: Twitter bot detection via warped correlation. In: Icdm, vol. 18, pp. 28–65 (2016)

    Google Scholar 

  5. Eken, B., Palma, F., Ayşe, B., Ayşe, T.: An empirical study on the effect of community smells on bug prediction. Software Qual. J. 29, 159–194 (2021)

    Article  Google Scholar 

  6. Feng, S., Wan, H., Wang, N., Li, J., Luo, M.: Twibot-20: a comprehensive twitter bot detection benchmark. In: Proceedings of the 30th ACM International Conference on Information Knowledge Management, pp. 4485–4494 (2021)

    Google Scholar 

  7. Fornacciari, P., Mordonini, M., Poggi, A., Sani, L., Tomaiuolo, M.: A holistic system for troll detection on Twitter. Comput. Hum. Behav. 89, 258–268 (2018). https://doi.org/10.1016/j.chb.2018.08.008

    Article  Google Scholar 

  8. Hansen, L., Seedat, N., van der Schaar, M., Petrovic, A.: Reimagining synthetic tabular data generation through data-centric AI: a comprehensive benchmark. Adv. Neural. Inf. Process. Syst. 36, 33781–33823 (2023)

    Google Scholar 

  9. Jaipuria, N., et al.: Deflating dataset bias using synthetic data augmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020)

    Google Scholar 

  10. Li, L., Fan, Y., Tse, M., Lin, K.Y.: A review of applications in federated learning. Comput. Ind. Eng. 149, 106854 (2020)

    Article  Google Scholar 

  11. de Melo, C.M., Torralba, A., Guibas, L., DiCarlo, J., Chellappa, R., Hodgins, J.: Next-generation deep learning based on simulators and synthetic data. Trends Cogn. Sci. 26(2), 174–187 (2022)

    Article  Google Scholar 

  12. Murić, G., et al.: Large-scale agent-based simulations of online social networks. Auton. Agent. Multi-Agent Syst. 36(2), 38 (2022)

    Article  Google Scholar 

  13. Murtaza, H., Ahmed, M., Khan, N.F., Murtaza, G., Zafar, S., Bano, A.: Synthetic data generation: state of the art in health care domain. Comput. Sci. Rev. 48, 100546 (2023). https://doi.org/10.1016/j.cosrev.2023.100546

    Article  Google Scholar 

  14. Nikolenko, S.I.: Synthetic Data for Deep Learning, vol. 174. Springer, Cham (2021)

    Google Scholar 

  15. Orozco Camacho, A.: A study of social media trolls via graph representation learning (2023)

    Google Scholar 

  16. Puri, R., Spring, R., Patwary, M., Shoeybi, M., Catanzaro, B.: Training question answering models from synthetic data. arXiv preprint arXiv:2002.09599 (2020)

  17. Tregubov, A., Abramson, J., Hauser, C., Hussain, A., Blythe, J.: Modeling cognitive workload in open-source communities via simulation. In: Nardin, L.G., Mehryar, S. (eds.) MABS 2023. LNCS, pp. 146–159. Springer, Cham (2024). https://doi.org/10.1007/978-3-031-61034-9_10

    Chapter  Google Scholar 

  18. Tsantarliotis, P., Pitoura, E., Tsaparas, P.: Defining and predicting troll vulnerability in online social media. Soc. Netw. Anal. Min. 7, 1–15 (2017)

    Article  Google Scholar 

  19. Uchôa, A., et al.: Predicting design impactful changes in modern code review: a large-scale empirical study. In: 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR), pp. 471–482. IEEE (2021)

    Google Scholar 

  20. Wei, F., Nguyen, U.T.: Twitter bot detection using bidirectional long short-term memory neural networks and word embeddings. In: 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), pp. 101–109. IEEE (2019)

    Google Scholar 

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Correspondence to Alexey Tregubov .

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Blythe, J., Tregubov, A. (2025). Synthetic Data Generation for Machine Learning Models with Cognitive Agent Simulations. In: Mathieu, P., De la Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins: The PAAMS Collection. PAAMS 2024. Lecture Notes in Computer Science(), vol 15157. Springer, Cham. https://doi.org/10.1007/978-3-031-70415-4_7

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  • DOI: https://doi.org/10.1007/978-3-031-70415-4_7

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  • Online ISBN: 978-3-031-70415-4

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