Degree based Classification of Harmful Speech using Twitter Data

TL;DR

This paper introduces a new ontological classification system for harmful speech on Twitter, creating a dataset and a supervised model to recognize different degrees of hateful intent.

Contribution

It presents a novel ontological framework for classifying harmful speech and provides a new annotated Twitter dataset along with a supervised classification approach.

Findings

Created a new dataset of tweets with ontological harmful speech labels

Developed a supervised classifier for harmful speech detection

Demonstrated effective recognition of different harmful speech degrees

Abstract

Harmful speech has various forms and it has been plaguing the social media in different ways. If we need to crackdown different degrees of hate speech and abusive behavior amongst it, the classification needs to be based on complex ramifications which needs to be defined and hold accountable for, other than racist, sexist or against some particular group and community. This paper primarily describes how we created an ontological classification of harmful speech based on degree of hateful intent, and used it to annotate twitter data accordingly. The key contribution of this paper is the new dataset of tweets we created based on ontological classes and degrees of harmful speech found in the text. We also propose supervised classification system for recognizing these respective harmful speech classes in the texts hence.