International Journal of Science, Engineering and TechnologyAuthors: Kapil Dev, Ankit Raj, Apurv Pal, Renu Saini
Abstract: Digital harassment has become one of the most serious challenges in modern online communication systems. The rapid growth of social media platforms, online gaming communities, messaging applications, and digital forums has increased the spread of cyberbullying, hate speech, abusive communication, trolling, identity-based attacks, and online threats. Traditional moderation methods are no longer sufficient because millions of user-generated posts, comments, and messages are uploaded every minute. Artificial Intelligence (AI) has emerged as an effective solution for automated content moderation and harassment prevention. This research paper examines the role of Artificial Intelligence models in detecting and preventing digital harassment. The study analyzes various Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Transformer-based models used for toxicity detection and online safety. Models such as Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and GPT-based systems are compared based on accuracy, contextual understanding, scalability, and real-time performance. The paper also discusses AI-powered moderation systems, sentiment analysis, multilingual processing, toxicity detection, behavioral analysis, and ethical concerns related to automated moderation. The study highlights major limitations including algorithmic bias, false positives, privacy concerns, and challenges in understanding sarcasm, humor, and cultural context. The findings show that Transformer-based AI models significantly improve harassment detection accuracy compared to traditional ML approaches. However, responsible AI development and human oversight remain necessary for fair and transparent moderation systems.
