Authors :
Anil Kumar Jatra; Kusum Sharma
Volume/Issue :
Volume 9 - 2024, Issue 12 - December
Google Scholar :
https://tinyurl.com/44yhrepb
Scribd :
https://tinyurl.com/yxtmfhru
DOI :
https://doi.org/10.5281/zenodo.14609293
Abstract :
Spam detection in the era of big data requires
scalable and efficient techniques, particularly when
dealing with large datasets containing diverse languages.
Traditional methods struggle to address the multilingual
nature of spam, as language-specific approaches may not
generalize well across different languages. This paper
explores the establishment of a spam block method that
leverages large, diverse datasets encompassing multiple
languages. We employ advanced machine-learning
techniques to handle the complexities of linguistic
variations. By incorporating cross-lingual embeddings,
transfer learning, and ensemble models, our system aims
to detect spam content across various languages
accurately. We highlight the importance of feature
extraction, text preprocessing, and model adaptation in
achieving robust multilingual spam detection. The
proposed approach demonstrates improved performance
in detecting spam messages while maintaining scalability
and adaptability to new languages, providing a
foundational framework for combating spam globally.
Keywords :
Spam Detection, Multilingual Spam, Machine Learning, Cross-Lingual Embeddings, Transfer Learning, Ensemble Methods, Feature Extraction, Text Preprocessing, Model Adaptation, Large Datasets, and Language- Independent Spam Detection.
References :
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Spam detection in the era of big data requires
scalable and efficient techniques, particularly when
dealing with large datasets containing diverse languages.
Traditional methods struggle to address the multilingual
nature of spam, as language-specific approaches may not
generalize well across different languages. This paper
explores the establishment of a spam block method that
leverages large, diverse datasets encompassing multiple
languages. We employ advanced machine-learning
techniques to handle the complexities of linguistic
variations. By incorporating cross-lingual embeddings,
transfer learning, and ensemble models, our system aims
to detect spam content across various languages
accurately. We highlight the importance of feature
extraction, text preprocessing, and model adaptation in
achieving robust multilingual spam detection. The
proposed approach demonstrates improved performance
in detecting spam messages while maintaining scalability
and adaptability to new languages, providing a
foundational framework for combating spam globally.
Keywords :
Spam Detection, Multilingual Spam, Machine Learning, Cross-Lingual Embeddings, Transfer Learning, Ensemble Methods, Feature Extraction, Text Preprocessing, Model Adaptation, Large Datasets, and Language- Independent Spam Detection.