On Detecting Messaging Abuse in Short Text Messages using Linguistic and Behavioral patterns

The use of short text messages in social media and instant messaging has become a popular communication channel during the last years. This rising popularity has caused an increment in messaging threats such as spam, phishing or malware as well as other threats. The processing of these short text message threats could pose ad-ditional challenges such as the presence of lexical variants, SMS-like contractions or advanced obfuscations which can degrade the performance of traditional filtering so-lutions. By using a real-world SMS data set from a large telecommunications oper-ator from the US and a social media cor-pus, in this paper we analyze the effective-ness of machine learning filters based on linguistic and behavioral patterns in order to detect short text spam and abusive users in the network. We have also explored dif-ferent ways to deal with short text message challenges such as tokenization and entity detection by using text normalization and substring clustering techniques. The ob-tained results show the validity of the pro-posed solution by enhancing baseline ap-proaches.