Building Prediction Model for Detecting Cyberbullying using TikTok Comments
Bunga Aura Prameswari, Haliza Syafa Oktaviani, Titus Rangga Wicaksono, Biben Pieter Leonard, Said Achmad, Rhio Sutoyo · 2023
Easily accessible Internet and the need for communication led to the widespread use of social media platforms, e.g., TikTok. Social media platforms offer opportunities for social interaction and enter- tainment but also introduce risks such as fake news, fraud, and cyberbullying. The latter severely threatens the victim’s mental health and overall well-being. This research collects, annotates, and analyzes 1,508 TikTok comments concerning cyberbullying behavior. Then, the comments were used to build a Deep Learning BERT (Bidirectional Encoder Representations from Transformers) architecture prediction model. This experiment will use pretrained model BERT and fine-tuning BERT. After fine-tuning, the prediction model achieves 0.63 validation accuracy. This research high- lights the importance of building a prediction model to detect cyberbullying and contribute to a better understanding and prevention of this behavior on social media platforms.