Abstract
While relatively rare in face–to–face interactions, social media platforms have recently seen an increase in the occurrence of hate speech discourse. Most methods rely on word blacklists and other text level features such as n-grams. While this approach is effective for flagging hate speech content, the discourse is not limited to a specific vocabulary as users are constantly adopting new terms. In this work we develop a graph based approach that incorporates conventional word window contexts along with syntactic dependency contexts in order to learn the hidden meaning of hate speech code words that have relatively unknown associations to hate speech. Our proposal utilizes the different types of contexts in which words are utilized with the goal being to identify new code words, thus expanding the hate speech lexicon and improving the accuracy of future classification systems.