Youtube Spam Detection Scheme Using Stacked Ensemble Machine Learning Model

This work introduced a method to identify English spam comments on YouTube, a platform that has experienced a significant increase in spam activity. Despite YouTube's efforts to implement its own spam filtering system, it still struggles to effectively block spam comments. To address such an issue, we analyzed existing research on YouTube spam comment detection and performed classification experiments using an ensemble model that combines various techniques. A stacked ensemble model combines the predictions of multiple individual models to improve the accuracy and robustness of spam detection in social media applications. It leverages the strengths of different models and captures a broader range of features, leading to a more accurate classification of spam and non-spam content. It has then implemented a web extension to hide the spam comments from the page by modifying the CSS (Cascading Style Sheets). The comment data utilized in experiments was sourced from popular music videos by artists such as Psy, Katy Perry, LMFAO, Eminem, and Shakira.