Year: 2025
Type: article
Abstract: YouTube faces growing challenges with spam in comments, likes, and subscriptions, disrupting user experience and content integrity. Traditional rule-based spam detection methods are ineffective against evolving spam tactics. This study explores Convolutional Neural Networks (CNNs) for spam detection, leveraging their ability to process large-scale text and image data with high accuracy. The proposed CNN-based model achieves impressive accuracy rates up to 98.57%. Additionally, the research reviews various deep learning models, emphasizing adaptability, real-time implementation, and scalability for platforms like YouTube. The findings highlight CNNs' potential to enhance spam detection systems, ensuring more secure and reliable online environments.
Institution: Savitribai Phule Pune University
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408758343