Abstract
The rise of social media has brought a wave of automated programs known as social media bots. These bots pose significant challenges by disseminating misinformation, shaping fake trends, and manipulating public opinion. This also leads to a loss of trust in the platform and creates an environment of suspicion. The advent of the Large Language Model has further exacerbated this issue, as bots now generate content that is increasingly indistinguishable from human posts, making bot detection more difficult. Despite extensive research in social media bot detection, very few studies specifically focus on bots leveraging Generative AI technology. To bridge this research gap, in this paper, we introduce a novel enhanced version of the BotRGCN model, called Intent-Spectrum BotTracker, by incorporating three innovative features: user intention, posting message topic, and posting message entropy. Extensive experiments have been conducted, and the results demonstrate that the integration of intention, topics, and entropy metrics significantly enhances the performance of various baseline models, with our enhanced BotRGCN model exhibiting the most superior social media detection capabilities.
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References
Abou Daya, A., Salahuddin, M.A., Limam, N., Boutaba, R.: BotChase: Graph-based bot detection using machine learning. IEEE Trans. Netw. Serv. Manage. 17(1), 15–29 (2020)
Alothali, E., Zaki, N., Mohamed, E.A., Alashwal, H.: Detecting social bots on Twitter: a literature review. In: 2018 International Conference on Innovations in Information Technology (IIT), pp. 175–180. IEEE (2018)
Antypas, D., Ushio, A., Camacho-Collados, J., Neves, L., Silva, V., Barbieri, F.: Twitter topic classification. arXiv preprint arXiv:2209.09824 (2022)
Bessi, A., Ferrara, E.: Social bots distort the 2016 U.S. presidential election online discussion. First monday 21(11) (2016). https://doi.org/10.5210/fm.v21i11.7090
Broniatowski, D.A., et al.: Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate. Am. J. Public Health 108(10), 1378–1384 (2018)
Busbridge, D., Sherburn, D., Cavallo, P., Hammerla, N.Y.: Relational graph attention networks. arXiv preprint arXiv:1904.05811 (2019)
Chiu, T.K.: The impact of generative ai (GenAI) on practices, policies and research direction in education: a case of ChatGPT and midjourney. Interact. Learn. Environ. 1–17 (2023)
Chowdhury, S., et al.: Botnet detection using graph-based feature clustering. J. Big Data 4(1), 1–23 (2017). https://doi.org/10.1186/s40537-017-0074-7
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)
Feng, S., Wan, H., Wang, N., Luo, M.: BotRGCN: Twitter bot detection with relational graph convolutional networks. In: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 236–239 (2021)
Gai, L., Xing, M., Chen, W., Zhang, Y., Qiao, X.: Comparing CNN-based and transformer-based models for identifying lung cancer: which is more effective? Multimed. Tools Appl. 83(20), 59253–59269 (2023). https://doi.org/10.1007/s11042-023-17644-4
Gorwa, R., Guilbeault, D.: Unpacking the social media bot: a typology to guide research and policy. Policy Internet 12(2), 225–248 (2020)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst. 30 (2017)
Heidari, M., James Jr, H., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1–5. IEEE (2021)
Kaubiyal, J., Jain, A.K.: A feature based approach to detect fake profiles in Twitter. In: Proceedings of the 3rd International Conference on Big Data and Internet of Things, pp. 135–139 (2019)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Kudugunta, S., Ferrara, E.: Deep neural networks for bot detection. Inf. Sci. 467, 312–322 (2018)
Liu, Y., et al.: Roberta: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Lu, H., Gong, D., Li, Z., Liu, F., Liu, F.: BotCS: a lightweight model for large-scale Twitter bot detection comparable to GNN-based models. In: ICC 2023-IEEE International Conference on Communications, pp. 2870–2876. IEEE (2023)
Orabi, M., Mouheb, D., Al Aghbari, Z., Kamel, I.: Detection of bots in social media: a systematic review. Inf. Process. Manag. 57(4), 102250 (2020)
Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(1), 5485–5551 (2020)
Rodríguez-Ruiz, J., Mata-Sánchez, J.I., Monroy, R., Loyola-Gonzalez, O., López-Cuevas, A.: A one-class classification approach for bot detection on Twitter. Comput. Secur. 91, 101715 (2020)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Sharevski, F., Jachim, P., Florek, K.: To tweet or not to tweet: Covertly manipulating a Twitter debate on vaccines using malware-induced misperceptions. In: Proceedings of the 15th International Conference on Availability, Reliability and Security, pp. 1–12 (2020)
Shi, Y., Huang, Z., Feng, S., Zhong, H., Wang, W., Sun, Y.: Masked label prediction: Unified message passing model for semi-supervised classification. arXiv preprint arXiv:2009.03509 (2020)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
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)
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)
Yang, Z., Xue, J., Yang, X., Wang, X., Dai, Y.: VoteTrust: Leveraging friend invitation graph to defend against social network sybils. IEEE Trans. Dependable Secure Comput. 13(4), 488–501 (2015)
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This work was supported by the Jilin Provincial Department of Education Science and Technology Research Projects under Grant No. JJKH20220537KJ.
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Duan, J., Li, Z., Wang, X., Li, W., Bai, Q., Nguyen, M. (2025). Intent-Spectrum BotTracker: Tackling LLM-Based Social Media Bots Through an Enhanced BotRGCN Model with Intention and Entropy Measurement. In: Wu, S., Su, X., Xu, X., Kang, B.H. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2024. Lecture Notes in Computer Science(), vol 15372. Springer, Singapore. https://doi.org/10.1007/978-981-96-0026-7_5
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