Detection of Cyber Bullying on Social Media Using Machine learning

Abstract: Cyberbullying has arisen as an unavoidable and concerning issue via virtual entertainment stages, influencing the psychological well-being and prosperity of people around the world. To resolve this issue, this study proposes a cyberbullying recognition framework utilizing the K-SVM calculation. Utilizing the force of AI, the framework means to consequently distinguish and signal occurrences of cyberbullying progressively web-based entertainment content. The improvement of the location framework starts with the assortment and naming of a thorough dataset containing instances of cyberbullying and non-cyberbullying posts or remarks. After pre-handling the text information by eliminating unessential data, changing message over completely to lowercase, and tokenizing it, significant highlights are removed utilizing the pack of-words or TF-IDF methods. These changed element vectors act as contributions for preparing the K-SVM classifier, which tries to find the ideal hyper plane for successfully recognizing cyberbullying from non-cyberbullying content. The K-SVM model's performance is evaluated using a distinct testing dataset, with metrics such as exactness, accuracy, review, F1-score, and ROC-AUC broken down to determine its feasibility in identifying cyberbullying situations. Model calibrating is led through trial and error with different K-SVM hyper boundaries and cross-approval methods to upgrade the framework's exhibition.

Keywords: Cyberbullying, Support vector machine, Machine learning, social media, Classification.