Identification of cyberbullying on multi‐modal social media posts using genetic algorithm
Kirti Kumari, Jyoti Prakash Singh
Abstract
Abstract Cyberbullying is one of the detrimental effects, social media is facing nowadays. With the increasing use of photo sharing and text comments, the severity of cyberbullying has increased many folds. Automated tools to detect these events have become necessary to make this platform healthy and secure. Sometimes innocent‐looking images and text also convey bullying messages when posted together. So, the separate systems for processing text and images may not work properly to identify all cases of cyberbullying. In this research, we have tried to extract combined features of text and images to identify different cases of cyberbullying. We used a pre‐trained VGG‐16 network and convolutional neural network to extract the features from images and text, respectively. These features are further optimized using genetic algorithm to increase the efficiency of the whole system. Our proposed model is validated with a dataset containing text and image to achieve an F1‐score 78% which shows an improvement of 9% over earlier reported results on the same dataset.