Cyber Shield: Protecting the Digital Space from Bullies

Abstract

Introduction:  The cyberbullying detection system combines Artificial Intelligence (AI), Natural Language Processing (NLP), and deep learning to offer a sophisticated solution for detecting toxic online interactions. In contrast to conventional keyboard-based filtering, which tends to misclassify harmless content or miss implicit bullying, this system uses BERT (Bidirectional Encoder Representations from Transformers) to better comprehend the context and meaning of text. Cyberbullying may have catastrophic impacts on the victim, including, anxiety, depression and social exclusion. The requirement for an automated and smart detection system has become imperative as social media keeps evolving into a central mode of communication

Objectives: Cyber shield creates a highly precise and content sensitive cyberbullying detection system based on deep learning techniques. The system will improve social media surveillance by minimizing false positives and negatives in detecting bullying. It also targets real-time content categorization, which ensure that offending interactions are reported in real-time for intervention. The second major objective is to enhance detection of sarcasm, implicit bullying, and developing slang words that tend to outsmart conventional filtering methods. Finally, the system is scalable and flexible to ensure it can be used on other social media websites and languages.

Methods: The cyberbullying identification system operates within an organized method, beginning from data preprocessing to preprocess and normalize text data. Preprocessing involves tokenization, removing stop words, and lemmatization to normalize text input. After this, the system uses BERT to make its feature extraction and context understanding. BERT is going to understand the context of a word based on its relationship with the surrounding words in some sentence. Then we further train it on labelled data that has bullying and non-bullying text samples. The model was trained on comments and some performance indicators such as accuracy, precision, recall and F1-score were used to evaluate the model.

Results: This approach enhances the detection of cyberbullying beyond what traditional models can do. The context-aware processing improves the detection of some discrete and implicit form of bullying and offensive content that are incorporated in the neutral language text. Also, the real time processing abilities allows harmful content to be identified immediately and allowing timely intervention.

Conclusions: Cyber shield is extremely useful for identifying inappropriate content on social media through real-time monitoring with contextual understanding. Further enhancement could include multilingual processing, it will improve detection accuracy and increase the availability of this system. We can also add the detection of multimedia content such as videos, audios…