Detecting Cyberbullying in the Age of Visual social media: A Hybrid Text-Image Approach

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 proposes a novel hybrid model for cyberbullying detection in three different modalities of social data, namely, textual and infographic (text embedded along with an image). The architecture consists of a deep learning convolutional neural network (DLCNN) for predicting textual bullying content. The infographic content is discretized by separating text from the image using Google Lens in the 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 values. The model achieves a prediction accuracy of 98%, which is acquired after tuning different hyper-parameters.  The simulation results show that the proposed method has better accuracy compared to state-of-the-art approaches.