Cyberbullying Detection And Prevention In Social Networks

Abstract

The Rapid Growth Of Social Media Platforms Has Significantly Increased The Spread Of Harmful Online Content, Including Cyberbullying And Hate Speech. These Forms Of Communication Negatively Impact Individuals And Communities, Often Leading To Psychological Distress And Social Conflicts. Existing Content Filtering Systems Are Limited In Their Ability To Effectively Detect And Prevent Such Behavior In Real Time. This Paper Proposes An Intelligent Cyberbullying Detection And Prevention System Using Natural Language Processing (NLP) And The VADER Sentiment Analysis Technique. The System Analyzes User-generated Content, Classifies Sentiments, And Filters Offensive Messages Based On Predefined Rules. A Blacklist Mechanism Is Implemented To Identify Repeat Offenders, While Real-time Alerts Notify Users And Administrators Of Harmful Activity. The Proposed System Enhances Online Safety By Providing An Adaptive, Efficient, And User-friendly Solution For Managing Social Media Interactions.

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