Predicting Cyberbullying on social media (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Cyberbullying is the use of Information and Communication Technology (ICT) by individuals tohumiliate, tease, embarrass, taunt, defame and disparage a target without any face-to-face contact.Social media is the “virtual playground” used by bullies with the upsurge of social networking sitessuch as Facebook, Instagram, YouTube, Twitter etc. It is critical to implement models and systems forautomatic detection and resolution of bullying content available online as the ramifications can lead toa societal epidemic. This research proffers a novel hybrid model for Cyberbullying detection in threedifferent modalities of social data, namely, textual, and info-graphic (text embedded along with animage). The architecture consists of a Deep Learning convolution neural network (DLCNN) forpredicting the textual bullying content. The info-graphic content is discretized by separating text fromthe image using Google Lens of Google Photos App. The processing of textual and visual componentsis carried out using the hybrid architecture and a Boolean system with a logical OR operation isaugmented to the architecture which validates and categorizes the output on the basis of text andimage bullying truth value. The model achieves a prediction accuracy of 98% which is acquired afterperforming tuning of different hyper-parameters. The simulation results show that the proposedmethod gives the better accuracy compared to the state of art approaches.