An Intelligent Machine Learning Approach to Detect Spam in Social Media

Machine learning based intelligent approach is applied to finding spam in YouTube videos. Spam comments are ones that are promotional or unrelated. A growing number of users have been drawn to the idea of making money by placing advertisements on popular social networking platforms over time. These remarks damage a channel's reputation. YouTube has addressed the issue with a limited set of solutions that concentrate on preventing comments from including links. Such heuristics are now futile since spammers have found ways to get around them. Despite the fact that common machine learning classification techniques have demonstrated considerable success, new methods can still increase accuracy. Therefore, this research attempt to identify such statements using machine learning techniques like Random Forest, SVM, and Naive Bayes. In order to convey a clear concept, graphs are used to illustrate data cleaning, data analysis, text preparation, and model building in this research.

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Publication Details

Published
2024-11-18
DOI
https://doi.org/10.1109/icdici62993.2024.10810782
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
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article

An Intelligent Machine Learning Approach to Detect Spam in Social Media

S. Nathiya, R Kaviyaraj, D. S. John Deva Prasanna, R. Mohandas et al.
Spam and Phishing Detection
article

An Intelligent Machine Learning Approach to Detect Spam in Social Media

S. Nathiya, R Kaviyaraj, D. S. John Deva Prasanna, R. Mohandas, N. Meenakshi, Naveen Arivazhagan
article en

Abstract

Machine learning based intelligent approach is applied to finding spam in YouTube videos. Spam comments are ones that are promotional or unrelated. A growing number of users have been drawn to the idea of making money by placing advertisements on popular social networking platforms over time. These remarks damage a channel's reputation. YouTube has addressed the issue with a limited set of solutions that concentrate on preventing comments from including links. Such heuristics are now futile since spammers have found ways to get around them. Despite the fact that common machine learning classification techniques have demonstrated considerable success, new methods can still increase accuracy. Therefore, this research attempt to identify such statements using machine learning techniques like Random Forest, SVM, and Naive Bayes. In order to convey a clear concept, graphs are used to illustrate data cleaning, data analysis, text preparation, and model building in this research.

SRM Institute of Science and Technology (IN)
Openalex Percentile: Top 61%
Spam and Phishing Detection
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