Mitigating Cyberbullying in Social Media: A Deep Contextual Learning Approach for Severity Level Classification in Textual Data
Prashant Agrawal, Awanit Kumar, Arun Kumar Tripathi · 2024
This research introduces a novel approach that integrates Deep Contextual Learning (DCL), specifically the DCL-256-32 model with an embedding model to accurately classify offense levels within the textual data. The DCL-256-32 model employs a SoftMax function to assign probabilities to distinct severity classes, ranging from critical to negligible. The proposed model incorporates two endpoints: an embedding model for generating semantic representations of input text and a set of pre-trained DCL-256-32 models for predicting offense levels. By averaging these predictions and associating them with humanreadable labels, this study proposes a robust and scalable framework for real-time text analysis. The proposed model demonstrates high performance compared to existing methods, contributing to the advancement of Natural Language Processing (NLP) and classification. This research study offers a practical solution for enhancing digital safety and combating online harassment.