cyberbullying detection in social media using hybrid learning approaches
Mohan G,N Vishnu,C J Santhosh Kumar,Akash P
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 31 March,2026 Pages:2068-2074
Abstract
Cyberbullying has become a major concern in online communication platforms due to the increasing usage of social media networks. Harmful comments, abusive language, and threatening messages can negatively affect individuals and communities. Manual monitoring of such content is difficult because of the large volume of data generated every day. Therefore, an automated system is required to identify cyberbullying activities effectively.This paper proposes a cyberbullying detection system that uses machine learning techniques to analyze textual data and identify harmful messages. The dataset used in this research contains labeled bullying and non- bullying text collected from online sources. The data is preprocessed and transformed into suitable features for classification. The classification process is performed using the Support Vector Machine algorithm, which is widely used for text classification problems. The system automatically detects abusive messages and allows administrators to take appropriate actions such as flagging or deleting harmful content. The proposed system improves the efficiency of cyberbullying detection and helps in maintaining a safer online environment.
Kewords
Cyberbullying Detection, Machine Learning, Text Classification, Natural Language Processing, Support Vector Machine.
Reference
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