Toward Multi-Modal Approach For Identification And Deletion Of Cyberbullying In Social Networks

Abstract

The widespread use of social networking platforms has significantly increased online communication, but it has also led to the rise of cyberbullying, which poses serious psychological and social risks to individuals. Cyberbullying involves the use of digital platforms to harass, threaten, or harm others through offensive language, hate speech, or targeted attacks. Traditional methods of detecting such behavior are often manual, time-consuming, and ineffective in handling the large volume of user-generated content. This paper presents an automated approach for the identification and detection of cyberbullying in social networks using machine learning and natural language processing techniques.The proposed system analyzes textual data from social media platforms to identify abusive, offensive, and harmful content. It utilizes feature extraction methods such as sentiment analysis, keyword detection, and linguistic patterns, combined with classification algorithms like Support Vector Machines, Naïve Bayes, and deep learning models. The system is capable of detecting different forms of cyberbullying with improved accuracy while reducing false positives. Experimental results demonstrate that the approach effectively identifies harmful content in real time, enabling timely intervention and contributing to safer and more responsible online environments.