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| Research on Terror-Related Sensitive Entity Recognition Model of a Heterogeneous Social Network Based on Broad Learning |
| Huang Wei, Tong Qingyun, Li Yuefeng |
| School of Economics and Management, Hubei University of Technology, Wuhan 430064 |
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Abstract Currently, sensitive entities are active in the social network system and perform behaviors such as using social networks to spread extremism and contacting target groups. The primary issue in cyber-security governance is to identify sensitive entities. Therefore, a model based on broad learning is proposed for the identification of sensitive entities in heterogeneous social networks, which can provide strategies for China s practice of network information security governance in the new era. Two large experimental data sets of heterogeneous social networks (Twitter and Facebook), that is the user nodes and tweet nodes, are combined after they are processed by broad learning-based network embedding technology, and are embedded into the same low-dimensional feature space. The results are integrated into the matrix factorization framework to achieve the identification of multi-source heterogeneous sensitive entities. By comparing the experimental results of multiple data sets with those of a single data set, it can be concluded that the model proposed in this paper has better performance.
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Received: 21 November 2019
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