$100,000 prize jackpot. call now!: identifying the pertinent features of SMS spam

Authors:
Henry Tan;Nazli Goharian;Micah Sherr
Affiliations:
Georgetown University, Washington, DC, USA;Georgetown University, Washington, DC, USA;Georgetown University, Washington, DC, USA
Venue:
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Year:
2012

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Abstract

Mobile SMS spam is on the rise and is a prevalent problem. While recent work has shown that simple machine learning techniques can distinguish between ham and spam with high accuracy, this paper explores the individual contributions of various textual features in the classification process. Our results reveal the surprising finding that simple is better: using the largest spam corpus of which we are aware, we find that using simple textual features is sufficient to provide accuracy that is nearly identical to that achieved by the best known techniques, while achieving a twofold speedup.