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Home > Archives > Volume 20, No 11 (2022) > Article

DOI: 10.14704/nq.2022.20.11.NQ66268

A Spam Transformer Model for SMS Spam Detection

K Jaya Krishna, Dr. D Bujji Babu, Yamavarapu Venkata Sai Chadwika, Nuthalapati Yagnika, Nagumothu Naga Siva Sai Dinesh, Vemuri Lakshman

Abstract

In this research, we propose an adaptive Transformers model optimised for identifying SMS spam messages in order to investigate the potential of the Transformers model in this context. We conduct experiments on the SMS Spam Collection v.1 dataset and the UtkMl Twitter Spam Detection Competition dataset, comparing our suggested spam Transformers against a number of well-established machine learning classifiers and cutting-edge SMS spam detection methods. Our SMS spam detection testing demonstrate that the suggested improved spam Transformer provides the best results compared to the other alternatives. The suggested model also performs well on the UtkMl's Twitter dataset, suggesting it might be successfully adapted to other situations with comparable characteristics.

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