Detecting Cyberbullying on Social Media Using Support Vector Machine: A Case Study on Twitter
Al-Khowarizmi Al-Khowarizmi, Indah Purnama Sari, Halim Maulana · International Journal of Safety and Security Engineering · 2023
Cyberbullying, a prevalent issue in digital media, particularly social media, poses a significant concern owing to its pervasive nature and potential harm.Social media platforms permit users to exchange opinions freely, which, while fostering open discourse, can also trigger instances of cyberbullying.This study focusses on Twitter discourses related to Indonesia's contentious public policy, "Cipta Kerja".The inherent polarity of views towards this policy has given rise to instances of cyberbullying.An extensive dataset comprising 2400 tweets was meticulously assembled, employing the keyword "Cipta Kerja".This dataset was subsequently partitioned into training and testing subsets to facilitate cyberbullying detection through computational algorithms.Sentiment analysis played a crucial role in this process, with the Support Vector Machine (SVM) method demonstrating remarkable reliability in classifying sentiment-related issues and, therefore, detecting cyberbullyings.The SVM method, using a linear kernel function, achieved a commendable accuracy rate of 92.7% in cyberbullying detection.This study's results underscore the effectiveness of SVM in identifying instances of cyberbullying on social media platforms, offering new promise for safeguarding digital spaces.