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In order to study online hate speech, the availability of datasets containingthe linguistic phenomena of interest are of crucial importance. However, whenit comes to specific target groups, for example teenagers, collecting such datamay be problematic due to issues with consent and privacy restrictions.Furthermore, while text-only datasets of this kind have been widely used,limitations set by image-based social media platforms like Instagram make itdifficult for researchers to experiment with multimodal hate speech data. Wetherefore developed CREENDER, an annotation tool that has been used in schoolclasses to create a multimodal dataset of images and abusive comments, which wemake freely available under Apache 2.0 license. The corpus, with Italiancomments, has been analysed from different perspectives, to investigate whetherthe subject of the images plays a role in triggering a comment. We find thatusers judge the same images in different ways, although the presence of aperson in the picture increases the probability to get an offensive comment.

Creating a Multimodal Dataset of Images and Text to Study Abusive Language