Creating a Multimodal Dataset of Images and Textto Study Abusive Language
Table 2: Distribution of the triggers over the subject types.
['[EMPTY]', '[BOLD] Females', '[BOLD] Males', '[BOLD] Mixed', '[BOLD] Nobody']
In order to analyse whether the subject of a picture has an impact on the choice to write an offensive message, we perform two different analyses. We first assign manually the subject of each image having at least 1 comment to one of the following categories: male-only subject(s), female-only subject(s), mixed group, no human subject. The graphical representation shows that the main differences between pictures with male and female subjects concern the ‘Facial expression’ and ‘Clothing’ categories: the first is more frequently associated with male subjects, while the second seems to be more related to female subjects. When there are multiple subjects with different genders, instead, no particular differences are observed. As expected, when no person is portrayed in the picture, ‘Location’, ‘Activity’ and ‘Other’ are prevalent. In some cases, ‘Pose’ or ‘Expression’ are selected, because of the presence of animals or drawings in the images.