Abstract
Based on the prevalence and complexity of disinformation propagation on current social networks, this research aims to explore the propagation principles of cross-platform disinformation and construct a corresponding propagation model. First, we collected a typical disinformation topic on Weibo and analyzed the propagation characteristics of the topic. Then, a two-layer coupled social network model was constructed based on a positively correlated coupling strategy of node weights. Finally, we proposed a disinformation propagation model for coupled social networks, referred to as the CSN_SIR model. The CSN_SIR model integrates the classic framework of the SIR model with the coupling characteristics of social networks, enabling more accurate simulation and prediction of the disinformation propagation process. To validate the effectiveness of the model, we reconstructed the propagation graph of disinformation on Weibo and conducted simulation experiments of the model. The experimental results indicate that the CSN_SIR model can effectively replicate the propagation trends of disinformation. In addition, this study reveals the key features of disinformation propagation when spreading across platforms, providing new perspectives for understanding disinformation propagation in cross-platform social networks.
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Acknowledgement
This research was supported by the Guangxi Key Research and Development Funding (2019AB35004; AB20238030), the National Natural Science Foundation of China (No. 62062014), the grant from Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips (No. BCIC-23-Z1), and the Key Scientific Research Project of Guangxi Normal University (No. 2018ZD007).
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Chen, J., Jiang, M., He, F. (2024). Model Construction and Empirical Research on Cross-Platform Propagation of Disinformation. In: Huang, DS., Chen, W., Guo, J. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14870. Springer, Singapore. https://doi.org/10.1007/978-981-97-5606-3_34
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DOI: https://doi.org/10.1007/978-981-97-5606-3_34
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