Creating a Multimodal Dataset of Images and Textto Study Abusive Language
Table 3: Subject types for pictures annotated with ‘Yes’ (i.e. triggering a comment) and ‘No’
['[EMPTY]', '% Yes', '% No']
We perform the same manual classification on a set of 3,200 pictures randomly taken from the images that were tagged with ‘No’, assigning each of them to one of the four subject categories. In particular, pictures with no human subject are less likely to get an offensive comment, while those with a female subject are the most commented ones. Also male subjects, however, trigger offensive comments very frequently, while they are only present in 19% of the images which were not commented by users.