Research on sensitive content detection in social networks

Author:

Meng Xuyang,Xu Yabin

Funder

National Natural Science Foundation of China

Beijing Key Laboratory of Internet Culture and Digital Dissemination Research

Key Lab of Information Network Security, Ministry of Public Security

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference19 articles.

1. Ali, S.H.A., Ozawa, S., Nakazato, J., et al. An autonomous online malicious spam email detection system using extended RBF network. In: International Joint Conference on Neural Networks, pp. 1–7. IEEE (2015)

2. Caruana, R.: Multi-Task Learning, pp. 5–50. School of Computer Science, Carnegie Mellon University, Pitttsburgh (1997)

3. Deng, Y.G., Wu, G.Y.: Information filtering algorithm for sensitive word decision tree based on text content. J. Comput. Eng. 40(9), 300–304 (2014)

4. Duan, L., Tang, C.J., Zuo, J., et al.: Two level filtering chinese text content self-learning algorithm in web real-time environment. J. Front. Comput. Sci. Technol. 05(8), 695–706 (2011)

5. Jenkins, B.: A hash function for hash table lookup. Dr Doob’s (1997)

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1. Efficient Detection of Generative Chinese Statements Using Feature Compression Methods;2024 2nd International Conference On Mobile Internet, Cloud Computing and Information Security (MICCIS);2024-04-19

2. Sensitive Word Recognition Scheme Based on Text-RCNN Model;2023 International Conference on Data Science and Network Security (ICDSNS);2023-07-28

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