The Hateful Memes Challenge:Detecting Hate Speech in Multimodal Memes
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['Protected category', '%']
That is, creators and distributors of hateful memes aim to incite hatred, enmity or humiliation of groups of people based on the grounds of gender, nationality, etc. and if that group falls in one of the protected categories, it constitutes hate speech under our definition. We analyzed the dev set based on these protected categories. (right). One meme can attack multiple protected categories. We observe that race and religion-based hate are the most prevalent. Note that this set of protected categories is by no means complete or exhaustive, but it is useful to get a better sense of the dataset properties. Hateful memes employ different types of attacks for different protected categories. In our analysis, we classified these attack types into different classes, together with a catch-all “other” class. One of the major types of attack is dehumanization, in which groups of people are compared with non-human things, such as animals or objects. We break this down into different types of dehumanization found in the data. Our findings show that the types of attack are broadly distributed, with comparison with criminals (e.g. terrorists), negative stereotypes (e.g. saying that certain demographics are sexually attracted to children/animals) and mocking hate crimes (e.g. the holocaust) being the most frequent categories. Again, note that this set of attack types is by no means complete or exhaustive, but it helps us understand what types of attack a classifier needs to detect if it is to be successful on the task.