Abstract:
Many mobile social network users share messages. Spammers post links to viruses and ads or follow many users, resulting in many misleading messages in mobile social networks. This paper proposes an adaptive social spammer detection (ASSD) model. We create a spammer classifier using a few labeled and unlabeled patterns. Compared to supervised learning, prediction accuracy is high. ASSD reduces the effort needed to label social members. An incremental learning method updates the spammer detection model adaptively without retraining because social spammers change their behavior to deceive it. ASSD is compared to other supervised and semi-supervised machine learning methods using the Social Honeypot Dataset. Experimental results show the proposed model outperforms baseline methods in recall and precision. By adapting to new social media data, ASSD maintains high detection accuracy.
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