Content-based mobile spam classification using stylistically motivated features

Dae Neung Sohn, Jung Tae Lee, Kyoung Soo Han, Hae Chang Rim

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


The feature of brevity in mobile phone messages makes it difficult to distinguish lexical patterns to identify spam. This paper proposes a novel approach to spam classification of extremely short messages using not only lexical features that reflect the content of a message but new stylistic features that indicate the manner in which the message is written. Experiments on two mobile phone message collections in two different languages show that the approach outperforms previous content-based approaches significantly, regardless of language.

Original languageEnglish
Pages (from-to)364-369
Number of pages6
JournalPattern Recognition Letters
Issue number3
Publication statusPublished - 2012 Feb 1


  • Mobile spam classification
  • Stylistic features
  • Text messaging service

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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