Main Article Content
The proliferation of digital communication platforms has led to an alarming increase in spam messages across different languages, posing a significant threat to user experience and online security.Context-based multilingual spam detection is a promising new approach to spam detection. it takes into account the context of the message, such as the sender, recipient, and surrounding messages. Traditional spam filters often rely on static rule-based or language-specific approaches, which are inadequate in dealing with the evolving tactics employed by spammers. This paper presents a context-based multilingual spam detection framework that leverages advanced natural language processing techniques to effectively identify and filter spam messages in multiple languages.context-based multilingual spam detection is a more accurate and versatile way to detect spam, especially in a globalized world.It is important because 1)It improves the accuracy of spam detection, especially for new and emerging types of spam.2) Reduce the number of false positives. 3)Detect spam messages in multiple languages. 4)Be adapted to new languages and cultures as they emerge. The suggested context-based multilingual spam detection framework furnishes a viable alternative for dealing with the increasingly complex and diverse landscape of spam across different languages. Its adaptive and context-aware method not only improves spam detection effectiveness but also reduces the danger of false positives and negatives, ultimately improving the quality of online communication and user experience.