ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (7): 1490-1503.

Special Issue: 2021虚假信息检测专题

### Information Propagation Prediction and Specific Information Suppression in Social Networks

Cao Jiuxin1, Gao Qingqing1, Xia Rongqing2, Liu Weijia1, Zhu Xuelin1, Liu Bo2

1. 1（School of Cyber Science and Engineering, Southeast University, Nanjing 211189);2(School of Computer Science and Engineering, Southeast University, Nanjing 211189)
• Online:2021-07-01
• Supported by:
This work was supported by the National Natural Science Foundation of China (61772133, 61972087), the National Social Science Foundation of China (19@ZH014), the Jiangsu Key Research and Development Program (BE2018706), the Natural Science Foundation of Jiangsu Province (SBK2019022870), the Jiangsu Key Laboratory of Computer Networking Technology, the Jiangsu Provincial Key Laboratory of Network and Information Security (BM2003201), and the Key Laboratory of Computer Network and Information Integration of Ministry of Education of China (93K-9).

Abstract: In recent years, with the increasing number of users in social networks such as Twitter, Facebook and Sina Weibo, the amount of information has rapidly expanded. The spread of untrue information hidden in massive information has brought adverse effects. How to regulate or suppress the spread of specific information is a technical challenge faced by network information management. In order to solve this problem, first of all, the independent information forwarding prediction mechanism based on machine learning method, which does not depend on the propagation model is proposed, so as to predict the information propagation. Secondly, based on the independent cascade model, considering the particularity of the scenario in this paper, the asynchronous information unequal competition propagation model is proposed as the competitive propagation mechanism of specific information and immune information. Finally, three selection algorithms of seed nodes are proposed and the immune information is widely spread in the network by injecting immune information into the seed nodes, so as to suppress the spread of specific information. Experiments based on real social network data show that the information propagation prediction model and the selection algorithms of seed nodes proposed have good effects on the regulation and suppression of specific information propagation.

CLC Number: