A Novel Approach for Spam Comment Detection on YouTube Using Graph-Enhanced Hierarchical Attention Network (GE-HAN)

Abstract

Abstract Spam comments pose a significant challenge in maintaining the quality of online platforms, including YouTube. In this paper, we propose a novel approach, the Graph-Enhanced Hierarchical Attention Network (GE-HAN), for spam comment detection. Our approach leverages the content of comments, the hierarchical structure of conversations, and user interactions to accurately classify comments as spam or non-spam. The GE-HAN model combines attention mechanisms to capture important textual features within comments and graph convolutional networks to analyze user interactions and detect coordinated spamming efforts. We trained and evaluated the model using a labeled dataset of spam comments, incorporating user names along with comment content and adjacency matrix representing user interactions. Experimental results demonstrate that the GE-HAN model achieves superior performance in spam detection, outperforming traditional methods. By considering both content and user dynamics, the model effectively identifies spam comments in YouTube, providing a robust solution to combat spam. Our research contributes to the field by showcasing the potential of graph-enhanced attention mechanisms in capturing complex patterns within comments and user interactions.