  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 additional challenges such as the presence of lexical
variants, SMS-like contractions or advanced obfuscations which can degrade the
performance of traditional filtering solutions. By using a real-world SMS data
set from a large telecommunications operator from the US and a social media
corpus, in this paper we analyze the effectiveness 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 different ways to deal
with short text message challenges such as tokenization and entity detection by
using text normalization and substring clustering techniques. The obtained
results show the validity of the proposed solution by enhancing baseline
approaches.
