Abstract: Some users of social media are spreading racist, sexist, and otherwise hateful content. For the purpose of training a hate speech detection system, the reliability of the annotations is crucial, but there is no universally agreed-upon definition. We collected potentially hateful messages and asked two groups of internet users to determine whether they were hate speech or not, whether they should be banned or not and to rate their degree of offensiveness. One of the groups was shown a definition prior to completing the survey. We aimed to assess whether hate speech can be annotated reliably, and the extent to which existing definitions are in accordance with subjective ratings. Our results indicate that showing users a definition caused them to partially align their own opinion with the definition but did not improve reliability, which was very low overall. We conclude that the presence of hate speech should perhaps not be considered a binary yes-or-no decision, and raters need more detailed instructions for the annotation.

Introduction: Social media are sometimes used to disseminate hateful messages. In Europe, the current surge in hate speech has been linked to the ongoing refugee crisis. Lawmakers and social media sites are increasingly aware of the problem and are developing approaches to deal with it, for example promising to remove illegal messages within 24 hours after they are reported BIBREF0 . This raises the question of how hate speech can be detected automatically. Such an automatic detection method could be used to scan the large amount of text generated on the internet for hateful content and report it to the relevant authorities. It would also make it easier for researchers to examine the diffusion of hateful content through social media on a large scale. From a natural language processing perspective, hate speech detection can be considered a classification task: given an utterance, determine whether or not it contains hate speech. Training a classifier requires a large amount of data that is unambiguously hate speech. This data is typically obtained by manually annotating a set of texts based on whether a certain element contains hate speech. The reliability of the human annotations is essential, both to ensure that the algorithm can accurately learn the characteristics of hate speech, and as an upper bound on the expected performance BIBREF1 , BIBREF2 . As a preliminary step, six annotators rated 469 tweets. We found that agreement was very low (see Section 3). We then carried out group discussions to find possible reasons. They revealed that there is considerable ambiguity in existing definitions. A given statement may be considered hate speech or not depending on someone's cultural background and personal sensibilities. The wording of the question may also play a role. We decided to investigate the issue of reliability further by conducting a more comprehensive study across a large number of annotators, which we present in this paper. Our contribution in this paper is threefold:

Question: Which embeddings do they detect biases in?
Answer: Word embeddings trained on GoogleNews and Word embeddings trained on Reddit dataset