Leveraging LSTM Neural Networks for Advanced Harassment Detection: Insights into Insults and Defamation in the U-Tapis Module

The prevalence of online harassment necessitates sophisticated automated systems that can accurately classify offensive content. In this work, we present a text classification system based on Long Short-Term Memory (LSTM) networks to categorize text into Neutral, Insult, and Defamation classes, thereby providing a more granular understanding of abusive behavior in digital environments. The system was evaluated using two labeled datasets—150 samples generated by ChatGPT and 1000 samples from internet sources—achieving an accuracy of 85% on both. Notably, the model demonstrated strong performance in identifying Defamation, exhibiting high precision and recall. These findings underscore the effectiveness of LSTM networks in capturing complex linguistic features, highlighting their potential for improving content moderation tools and curbing online harassment.