Statistical Features-Based Real-Time Detection of Drifted Twitter Spam
IEEE Transactions on Information Forensics and Security
Institute of Electrical and Electronics Engineers (IEEE)
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Twitter spam has become a critical problem nowadays. Recent works focus on applying machine learning techniques for Twitter spam detection, which make use of the statistical features of tweets. In our labeled tweets data set, however, we observe that the statistical properties of spam tweets vary over time, and thus, the performance of existing machine learning-based classifiers decreases. This issue is referred to as “Twitter Spam Drift”. In order to tackle this problem, we first carry out a deep analysis on the statistical features of one million spam tweets and one million non-spam tweets, and then propose a novel Lfun scheme. The proposed scheme can discover “changed” spam tweets from unlabeled tweets and incorporate them into classifier’s training process. A number of experiments are performed to evaluate the proposed scheme. The results show that our proposed Lfun scheme can significantly improve the spam detection accuracy in real-world scenarios.
This work was supported by the ARC Linkage Project under Grant LP120200266. The work of J. Zhang was supported by the National Natural Science Foundation of China under Grant 61401371.
This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.
Vol. 12, Iss. 4, April 2017, pp. 914 - 925