Automated Spam Detection in Short Text Messages

Abstract

Increase in the popularity and reach of short text messages has led to their usage in propagating unsolicited advertising, promotional offers, and other unwarranted material to users. This has led to a high influx of such spam messages. In order to protect the interests of the user, several countermeasures have been deployed by telecommunication companies to hinder the volume of such spam. However, some volume of spam messages still manage to avoid these measures and cause varying degree of annoyance to users. In this chapter, an automated spam detection algorithm is proposed to deal with the particular problem of short text message spam. The proposed algorithm performs the two class (spam, ham) classification using stylistic and text features specific to short text messages. The algorithm is evaluated on three databases belonging to diverse demographic settings. Experimental results indicate that the proposed algorithm is highly accurate in detecting spam in short messages and can be utilized by a wide variety of users to reduce the volume of spam messages.