NLP for Safer Platforms: Automated Moderation Techniques

Abstract

Automated content moderation has become increasingly critical in recent years due to the rapid rise of user-generated content across online platforms. The growing use of social media and digital communication has underscored the need for efficient systems capable of detecting, filtering, and managing harmful or inappropriate content in real-time. Traditional manual moderation is resource-intensive, error-prone, and slow, highlighting the demand for robust automated solutions. Natural Language Processing (NLP) has emerged as a powerful tool for tackling the challenges of content moderation by enabling systems to process, analyze, and interpret text. This paper explores various NLP techniques employed in automated content moderation, covering classical machine learning methods, deep learning models, and the latest advancements in transformer-based architectures. It also outlines a comprehensive methodology and framework for developing automated moderation systems, alongside a comparative analysis of selected models and their performance. The findings suggest that transformer-based models like BERT and GPT offer superior accuracy and resilience, albeit with significant computational requirements. Key considerations in the design of these systems include interpretability, fairness, and context-awareness. This research offers valuable insights into how advanced NLP techniques can be integrated into content moderation workflows to foster safer online environments while safeguarding freedom of expression.