The performance of multiple language models in identifying offensive
  language on social media
Hao Li Brandon Bennett
Text classification is an important topic in the field of natural language
processing. It has been preliminarily applied in information retrieval, digital
library, automatic abstracting, text filtering, word semantic discrimination
and many other fields. The aim of this research is to use a variety of
algorithms to test the ability to identify offensive posts and evaluate their
performance against a variety of assessment methods. The motivation for this
project is to reduce the harm of these languages to human censors by automating
the screening of offending posts. The field is a new one, and despite much
interest in the past two years, there has been no focus on the object of the
offence. Through the experiment of this project, it should inspire future
research on identification methods as well as identification content.