A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Content based SMS spam filtering
Proceedings of the 2006 ACM symposium on Document engineering
Linguistic correlates of style: authorship classification with deep linguistic analysis features
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Feature engineering for mobile (SMS) spam filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Spam filtering for short messages
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A behavior-based SMS antispam system
IBM Journal of Research and Development
Content-based mobile spam classification using stylistically motivated features
Pattern Recognition Letters
Review: SMS spam filtering: Methods and data
Expert Systems with Applications: An International Journal
SMSAssassin: crowdsourcing driven mobile-based system for SMS spam filtering
Proceedings of the 12th Workshop on Mobile Computing Systems and Applications
$100,000 prize jackpot. call now!: identifying the pertinent features of SMS spam
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Simple SMS spam filtering on independent mobile phone
Security and Communication Networks
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Content-based approaches to detecting mobile spam to date have focused mainly on analyzing the topical aspect of a SMS message (what it is about) but not on the stylistic aspect (how it is written). In this paper, as a preliminary step, we investigate the utility of commonly used stylistic features based on shallow linguistic analysis for learning mobile spam filters. Experimental results show that the use of stylistic information is potentially effective for enhancing the performance of the mobile spam filters.