Review of Spam Message Detection

A message is a piece of information exchanged for personal or business purposes. However, spammers often target these messages, resulting in financial losses. Spam messages have become increasingly prevalent in various fields. In the past, machine-learning-based techniques have been used to detect spam. However, only a few review works are available on spam detection techniques in the fields of SMS, email, Twitter, and online reviews. Additionally, these studies have limitations, as they only review a limited number of techniques from the machine learning field. Moreover, an in-depth evaluation of each suggested technique's performance is missing. Therefore, this paper provides a detailed review of spam message detection techniques in five domains: SMS, email, Twitter, Instagram, and online reviews. Based on the state-of-the-art reviews in these five domains, a generalized model for spam message detection is presented. Furthermore, the paper also provides a thorough review of past research in the field and presents a detailed analysis. Finally, the paper concludes with future trends that can be used for message spam detection soon.

Keywords:

Spam detection , Supervised Learning , Feature Extraction , Text Preprocessing