Deep learning to filter SMS spam

Pradeep Kumar Roy, Jyoti Prakash Singh, Snehasish Banerjee

Research output: Contribution to journalArticlepeer-review


The popularity of short message service (SMS) has been growing over the last decade. For businesses, these text messages are more effective than even emails. This is because while 98% of mobile users read their SMS by the end of the day, about 80% of the emails remain unopened. The popularity of SMS has also given rise to SMS Spam, which refers to any irrelevant text messages delivered using mobile networks. They are severely annoying to users. Most existing research that has attempted to filter SMS Spam has relied on manually identified features. Extending the current literature, this paper uses deep learning to classify Spam and Not-Spam text messages. Specifically, Convolutional Neural Network and Long Short-term memory models were employed. The proposed models were based on text data only, and self-extracted the feature set. On a benchmark dataset consisting of 747 Spam and 4,827 Not-Spam text messages, a remarkable accuracy of 99.44% was achieved.
Original languageEnglish
Pages (from-to)524-533
Number of pages10
JournalFuture generation computer systems
Early online date4 Sept 2019
Publication statusPublished - Jan 2020

Bibliographical note

© 2019 Elsevier B.V. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.


  • spam detection
  • sms
  • machine learning
  • Deep learning
  • LSTM
  • convolutional neural network

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