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Social Media Cyberbullying Detector

Author(s) G. Naveen Kumar, P. Monika, K. Soumya, J. Haveela
Country India
Abstract This paper introduces an intelligent Social Media Cyberbullying Detector, a framework designed to strengthen online safety by identifying toxic interactions in real-time. In high-activity digital communities, harmful content can proliferate rapidly, causing significant emotional and psychological distress for users. Traditional manual moderation is increasingly ineffective due to the sheer volume of data, while existing tools often lack the speed required for instantaneous intervention. To address this gap, our system leverages the Reddit API as a live data source, maintaining a continuous stream of user-generated posts and comments for deep-text analysis.
The proposed architecture integrates a real-time data processing pipeline with robust Natural Language Processing (NLP) and machine learning models to distinguish between harmless discourse and genuine harassment. A defining feature of this system is its capacity to recognize offensive language patterns and contextual intent the moment they appear. Upon detection, the system autonomously flags and archives harmful content in a dedicated dataset for further administrative review. To assist moderators in community health assessment, the system also generates live visual analytics, providing a clear statistical breakdown of bullying versus non-bullying content.
Keywords Cyberbullying Detection, Social Media Analysis, Natural Language Processing (NLP), Machine Learning, Reddit API, Real-Time Monitoring, Text Classification, Online Harassment Detection, Offensive Language Identification, LSTM, Logistic Regression, Data Preprocessing, Feature Extraction, Digital Safety, Toxic Comment Detection, Content Filtering, Cyber Safety Systems, AI-based Monitoring.
Field Engineering
Published In Volume 7, Issue 4, April 2026
Published On 2026-04-06
DOI https://doi.org/10.70528/IJLRP.v7.i4.2081
Short DOI https://doi.org/hbv76s
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