Inappropriate Speech Detection in Social Networks using Machine Learning

Detecting inappropriate speech has become essential to fostering safe and respectful social media and digital communication environments. This paper explores various machine learning techniques for identifying inappropriate content, including offensive, threatening, and abusive language. A comparative analysis is conducted between traditional machine learning algorithms, such as Support Vector Machines (SVM), Naïve Bayes, and Random Forest, and advanced deep learning models like Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. A labeled dataset of text samples, categorized as inappropriate or neutral, was used for training and evaluating the models. The deep learning methods, particularly BiLSTM, demonstrate superior performance in capturing the context, semantics, and subtle nuances of inappropriate speech compared to traditional methods. This paper provides a detailed examination of the technical implementation, model architectures, and experimental results, along with insights into potential future enhancements, such as improved contextual understanding, multilingual support, and realtime processing capabilities.