Drifted Twitter Spam Classification Using Multiscale Detection Test on K-L Divergence

Document Type

Article

Publication Date

1-1-2019

Abstract

Twitter spam classification is a tough challenge for social media platforms and cyber security companies. Twitter spam with illegal links may evolve over time in order to deceive filtering models, causing disastrous loss to both users and the whole network. We define this distributional evolution as a concept drift scenario. To build an effective model, we adopt K-L divergence to represent spam distribution and use a multiscale drift detection test (MDDT) to localize possible drifts therein. A base classifier is then retrained based on the detection result to gain performance improvement. Comprehensive experiments show that K-L divergence has highly consistent change patterns between features when a drift occurs. Also, the MDDT is proved to be effective in improving final classification result in both accuracy, recall, and f-measure.

Identifier

85071155136 (Scopus)

Publication Title

IEEE Access

External Full Text Location

https://doi.org/10.1109/ACCESS.2019.2932018

e-ISSN

21693536

First Page

108384

Last Page

108394

Volume

7

Grant

51775385

Fund Ref

National Natural Science Foundation of China

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