A STUDY ON DEEP LEARNING ALGORITHMS FOR MULTIMODAL AND MULTILINGUAL CYBERBULLYING DETECTION

Abstract

With the increased utilization of the internet and social media platforms, can foster destructive or harmful behaviors such as cyberbullying. Cyberbullying poses signicant threat to physical and mental health of the victims. There is a demand for automatic detection and prevention of cyberbullying. In Social networks, there is a big challenge to detect the cyber bullying event and to control all the cyberbullying content and languages that users post. Due to complexity of multiple languages and cross-mix languages used in cyberbullying, the detection has remained only mildly satisfying. And also recently, images and videos dominate the social feeds in addition to text messages and comments. Machine learning and deep learning techniques can be helpful to detect the bullies and can generate a model to automatically detect multi-lingual cyberbullying actions. Deep neural architectures are useful to model, learn and fuse multi-modal data for cyber bullying detection. This paper proposes a detailed review on machine and deep learning approach for detecting and preventing multimodal and multilingual cyberbullying.