Natural Language Processing Techniques for Automated Content Moderation

Abstract

Automated content moderation has gained considerable attention in recent years due to the exponential growth of user-generated content on online platforms. The surge in social media usage and digital communication channels has made it essential to develop systems that can effectively detect, filter, and manage harmful or inappropriate content in real-time. Manual moderation is labor-intensive, time-consuming, and prone to errors, creating a need for robust automated solutions. Natural Language Processing (NLP) has emerged as a key technology to address the challenges associated with content moderation by enabling systems to process, analyze, and interpret text. This paper provides an in-depth examination of various NLP techniques applied to automated content moderation, including classical machine learning approaches, deep learning architectures, and recent advances in transformer-based models. The paper also presents a detailed methodology and framework for building an automated content moderation system, along with a comparative analysis of selected models and their performance. Findings indicate that transformer-based architectures such as BERT and GPT demonstrate superior accuracy and robustness, although they necessitate high computational resources. The results highlight the importance of interpretability, fairness, and context-awareness in designing and implementing automated content moderation systems. This research contributes new insights into how advanced NLP techniques can be integrated into moderation pipelines to create safer online communities while respecting freedom of expression.