Unveiling disguised toxicity: A novel pre-processing module for enhanced content moderation

Abstract

This study introduces “Specialis Revelio,” a sophisticated text pre-processing module aimed at enhancing the detection of disguised toxic content in online communications. Through a blend of conventional and novel pre-processing methods, this module significantly improves the accuracy of existing toxic text detection tools, addressing the challenge of content that is deliberately altered to evade standard detection methods.•Integration with Existing Systems: “Specialis Revelio” is designed to augment popular toxic text classifiers, enhancing their ability to detect and filter toxic content more effectively.•Innovative Pre-processing Methods: The module combines traditional pre-processing steps like lowercasing and stemming with advanced strategies, including the handling of adversarial examples and typo correction, to reveal concealed toxicity.•Validation through Comparative Study: Its effectiveness was validated via a comparative analysis against widely used APIs, demonstrating a marked improvement in the detection of various toxic text indicators.