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An Adaptive Social Spammer Detection Model With Semi-Supervised Broad Learning
Qiu, Tie1,2; Liu, Xize3; Zhou, Xiaobo1,2; Qu, Wenyu1,2; Ning, Zhaolong4; Chen, C. L.Philip5,6,7
Source PublicationIEEE Transactions on Knowledge and Data Engineering

Mobile social networks include a large number of social members who forward messages cooperatively. However, spammers post links to viruses and advertisements, or follow a large number of users, which produces many misleading messages in mobile social networks. In this paper, we propose an adaptive social spammer detection (ASSD) model. We build a spammer classifier by using a small number of labeled patterns and some unlabeled patterns. The prediction accuracy is high compared with some conventional supervised learning methods. Moreover, the time and energy required to label the identity of social members are reduced by applying ASSD. Because social spammers frequently change their behavior to deceive the spammer detection model, an incremental learning method is designed to update the spammer detection model adaptively, without retraining. We evaluate ASSD by comparing it with other supervised and semi-supervised machine learning methods using the Social Honeypot Dataset. Experimental results show that the proposed model outperforms the baseline methods in terms of recall and precision. Additionally, ASSD maintains a high detection accuracy by adaptively updating the model with newly generated social media data.

KeywordBroad Learning Incremental Learning Mobile Social Network Semi-supervised Learning Spammer Detection
URLView the original
Indexed BySCIE
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000853844700005
Scopus ID2-s2.0-85099091493
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Cited Times [WOS]:9   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Affiliation1.Tianjin University, School of Computer Science and Technology, College of Intelligence and Computing, Tianjin, 300350, China
2.Tianjin Key Laboratory of Advanced Networking, Tianjin, 300350, China
3.Dalian University of Technology, School of Software, Dalian, 116620, China
4.Chongqing University of Posts and Telecommunications, School of Communication and Information Engineering, Chongqing, 400065, China
5.School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510641, China
6.University of Macau, Faculty of Science and Technology, 999078, Macao
7.Dalian Maritime University, Dalian, 116026, China
Recommended Citation
GB/T 7714
Qiu, Tie,Liu, Xize,Zhou, Xiaobo,et al. An Adaptive Social Spammer Detection Model With Semi-Supervised Broad Learning[J]. IEEE Transactions on Knowledge and Data Engineering,2022,34(10):4622-4635.
APA Qiu, Tie,Liu, Xize,Zhou, Xiaobo,Qu, Wenyu,Ning, Zhaolong,&Chen, C. L.Philip.(2022).An Adaptive Social Spammer Detection Model With Semi-Supervised Broad Learning.IEEE Transactions on Knowledge and Data Engineering,34(10),4622-4635.
MLA Qiu, Tie,et al."An Adaptive Social Spammer Detection Model With Semi-Supervised Broad Learning".IEEE Transactions on Knowledge and Data Engineering 34.10(2022):4622-4635.
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