A Multimodal Dataset of Images and Text to Study Abusive Language

In this paper, we present a novel dataset composed of images and comments in Italian, created with teenagers in classes using a simulated scenario to raise awareness on cyberbullying phenomena. Potentially offensive comments have been collected for more than 1,000 images and manually assigned to a semantic category. Our analysis shows that the presence of human subjects, as well as the gender of the people present in the pictures trigger different types of comment, and provides novel insight into the connection between images posted on social media and offensive messages. We also compare our corpus with a similar one obtained with WhatsApp, showing that comments to images show different characteristics compared to text-only interactions.