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.
A study by REF has shown that supplying hate-speech definitions to annotators can better align their view with the definition, but this does not positively affect annotation reliability.
Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis
Proceedings of NLP4CMC III: 3rd Workshop on Natural Language Processing for Computer-Mediated Communication (Bochum), Bochumer Linguistische Arbeitsberichte, vol. 17, sep 2016, pp. 6-9
Computer Science