Automatic Youtube Comment Spam Detection using Navie Bayes and Logistic Regression

During recent years, the volume and relevance of user-generated content on platforms such as YouTube have created a predicament in spam comment detection and filtering. This paper proposes an automated system for spam comment detection using Naive Bayes and Logistic Regression algorithms. After preprocessing, the spam comments go through tokenization, stop-word removal and stemming, while feature extraction is done using TF-IDF (Term Frequency-Inverse Document Frequency), which translates the comments into a numerical representation. Labeled datasets of YouTube comments introduce spam or non-spam categories for Naive Bayes and Logistic Regression model training. Performance is evaluated using generalized metrics like accuracy, precision, recall, and F1-score. Both models detect spam effectively, while Logistic Regression performs at a clasp higher level than Naive Bayes, (Support vector machine),KNN(k-nearest neighbors). The resulting machine might belong to YouTube’s comment moderation scheme, filtering spam automatically in real-time, optimizing user experience to engage in content-rich communication as against annoying content.