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An Adaptive Social Spammer Detection Model With Semi-Supervised Broad Learning
- Source :
- IEEE Transactions on Knowledge and Data Engineering. 34:4622-4635
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- 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.
- Subjects :
- Honeypot
business.industry
Computer science
Small number
Supervised learning
Machine learning
computer.software_genre
Computer Science Applications
Spamming
ComputingMethodologies_PATTERNRECOGNITION
Computational Theory and Mathematics
Classifier (linguistics)
Identity (object-oriented programming)
Social media
Artificial intelligence
business
Precision and recall
computer
Information Systems
Subjects
Details
- ISSN :
- 23263865 and 10414347
- Volume :
- 34
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Knowledge and Data Engineering
- Accession number :
- edsair.doi...........413773547166703f869e48b531810f4e