Hate speech in the form of racist and sexist remarks are a common occurrence on social media. For that reason, many social media services address the problem of identifying hate speech, but the definition of hate speech varies markedly and is largely a manual effort (BBC, 2015; Lomas, 2015). We provide a list of criteria founded in critical race theory, and use them to annotate a publicly available corpus of more than 16k tweets. We analyze the impact of various extra-linguistic features in conjunction with character n-grams for hate-speech detection. We also present a dictionary based the most indicative words in our data.

This paper introduces Linguistica 5, a software for unsupervised learning of linguistic structure. It is a descendant of Goldsmith's (2001, 2006) Linguistica. Open-source and written in Python, the new Linguistica 5 is both a graphical user interface software and a Python library. While Linguistica 5 inherits its predecessors' strength in unsupervised learning of natural language morphology, it incorporates significant improvements in multiple ways. Notable new features include tools for data visualization as well as straightforward extensions for both its components and embedding in other programs.