ML-DRIVEN ONLINE SAFETY: AN AUTOMATED DETECTION OF CHILD PREDATORS AND CYBER HARASSERS

Abstract

Social media has become a vital component of our existence, facilitating global connections between individuals. While these platforms provide numerous advantages, they have also exposed vulnerable individuals, particularly children, to online risks. Individuals who prey on children and engage in online harassment utilize the anonymity and wide reach of social media platforms to inflict harm upon others. Previously, these dangers were addressed through the use of manual reporting and human moderators. Users reported suspicious behavior, prompting human moderators to review the content for compliance with platform requirements. Nevertheless, this responsive approach frequently resulted in a delay in taking action, so enabling the spread of harmful content. Researchers have employed machine learning, a type of artificial intelligence that enables computers to learn from data and make predictions, to develop more proactive and effective solutions.