Predicting Cyberbullying on social media

Abstract

Cyberbullying is the use of Information and Communication Technology (ICT) by individuals to
humiliate, 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 sites
such as Facebook, Instagram, YouTube, Twitter etc. It is critical to implement models and systems for
automatic detection and resolution of bullying content available online as the ramifications can lead to
a societal epidemic. This research proffers a novel hybrid model for Cyberbullying detection in three
different modalities of social data, namely, textual, and info-graphic (text embedded along with an
image). The architecture consists of a Deep Learning convolution neural network (DLCNN) for
predicting the textual bullying content. The info-graphic content is discretized by separating text from
the image using Google Lens of Google Photos App. The processing of textual and visual components
is carried out using the hybrid architecture and a Boolean system with a logical OR operation is
augmented to the architecture which validates and categorizes the output on the basis of text and
image bullying truth value. The model achieves a prediction accuracy of 98% which is acquired after
performing tuning of different hyper-parameters. The simulation results show that the proposed
method gives the better accuracy compared to the state of art approaches.