Research on Social Robots with Multi-Platform Collaboration Mechanisms to Amplify the impact of Specific Information

Ruohan Li

doi:10.54691/3462ae12

Authors

Ruohan Li

DOI:

https://doi.org/10.54691/3462ae12

Keywords:

SIR model, robot detection, cooperative amplification effect.

Abstract

This study systematically explores the amplifying effect of cross-platform user coupling on the scale of information diffusion driven by social robots. By crawling user and content data related to "ja morant" from Instagram and Twitter, a joint judgment method based on multiple behavioral features is adopted to classify 60 core accounts (including 12 robot accounts) with high accuracy. Subsequently, a multi-layer network model reflecting the synergistic effect of multiple social platforms was constructed, and a parameterized SIR (Susceptible-Infected-Recovered) diffusion simulation was introduced, comprehensively considering factors such as intra-platform structure, inter-layer user coupling, and robot-driven diffusion enhancement. The experimental results show that multi-platform synergy significantly expands the scale of robot-driven information infection: the total number of infected individuals in the synergistic network is 2.07 times that of a single platform, with cross-platform user coupling contributing 51% of the newly added infections. Sensitivity analysis further reveals a highly linear correlation between coupling probability and the synergistic amplification effect. These findings provide a solid theoretical basis and empirical support for the quantitative evaluation of cross-platform risk governance and joint defense strategies against social robots.

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References

[1] Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2013). Multilayer Networks. arXiv. https://doi.org/10.48550/ARXIV.1309.7233

[2] Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., & Zanin, M. (2014). The structure and dynamics of multilayer networks. arXiv. https://doi.org/10.48550/ARXIV.1407.0742

[3] Pastor-Satorras, R., Castellano, C., Van Mieghem, P., & Vespignani, A. (2014). Epidemic processes in complex networks. arXiv. https://doi.org/10.48550/ARXIV.1408.2701

[4] Keuschnigg, M. (2018). Granovetter (1978): Threshold Models of Collective Behavior. In Netzwerkforschung (pp. 239–242). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-21742-6_54

[5] Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2014). The Rise of Social Bots. arXiv. https://doi.org/10.48550/ARXIV.1407.5225

[6] Bessi, A., & Ferrara, E. (2016). Social bots distort the 2016 U.S. Presidential election online discussion. First Monday. https://doi.org/10.5210/fm.v21i11.7090

[7] Davis, C. A., Varol, O., Ferrara, E., Flammini, A., & Menczer, F. (2016). BotOrNot: A System to Evaluate Social Bots. arXiv. https://doi.org/10.48550/ARXIV.1602.00975

[8] Stella, M., Ferrara, E., & De Domenico, M. (2018). Bots increase exposure to negative and inflammatory content in online social systems. Proceedings of the National Academy of Sciences, 115(49), 12435–12440. https://doi.org/10.1073/pnas.1803470115

[9] Li, B., & Saad, D. (2023). Infection-induced Cascading Failure–Impact and Mitigation. arXiv. https://doi.org/10.48550/ARXIV.2307.16767

[10] Li, Y., Yang, G., Ren, Y., Shi, L., Ma, R., van der Mei, H. C., & Busscher, H. J. (2020). Applications and Perspectives of Cascade Reactions in Bacterial Infection Control. Frontiers in Chemistry, 7. https://doi.org/10.3389/fchem.2019.00861

[11] Browne, C. A., Amchin, D. B., Schneider, J., & Datta, S. S. (2021). Infection percolation: A dynamic network model of disease spreading. arXiv. https://doi.org/10.48550/ARXIV.2103.09691

[12] Colizza, V., Barthélemy, M., Barrat, A., & Vespignani, A. (2007). Epidemic modeling in complex realities. Comptes Rendus. Biologies, 330(4), 364–374. https://doi.org/10.1016/j.crvi.2007.02.014