Spam Detection using Large Datasets with Mult...

Spam Detection using Large Datasets with Multilingual Support

Author Name : Anil Kumar Jatra, Kusum Sharma

DOI: https://doi.org/10.56025/IJARESM.2025.1302251756

ABSTRACT Spam detection has become a critical task in modern digital communication, particularly with the exponential growth of multilingual data from emails, social media, and messaging platforms. Traditional spam detection systems often struggle to handle linguistic diversity and scale efficiently to large datasets. This research proposes a novel approach leveraging ensemble models to enhance the accuracy and scalability of spam detection across multiple languages. By incorporating advanced machine learning techniques, including cross-lingual embeddings, the proposed system addresses key challenges such as class imbalances and evolving spam patterns. The experimental evaluation demonstrates the effectiveness of ensemble models, including boosting, in improving performance metrics like accuracy, precision, recall, and F1 score. This work provides a robust framework for global, multilingual spam detection, with a focus on adaptability, real-time deployment, and scalability to combat spam on a worldwide scale.