ProvocationProbe: Instigating Hate Speech Dataset from Twitter

TL;DR

This paper introduces ProvocationProbe, a new Twitter dataset with around twenty thousand tweets, aimed at distinguishing instigating hate speech from general hate speech through detailed annotations and feature analysis.

Contribution

The paper presents a novel dataset and analysis that differentiate instigating hate speech from general hate speech, focusing on identifying unique features and providing comprehensive annotations.

Findings

Identified key features distinguishing instigating hate speech from general hate speech.

Created a dataset covering nine global controversies with detailed annotations.

Highlighted the importance of targeted identity attacks and reasons for hate.

Abstract

In the recent years online social media platforms has been flooded with hateful remarks such as racism, sexism, homophobia etc. As a result, there have been many measures taken by various social media platforms to mitigate the spread of hate-speech over the internet. One particular concept within the domain of hate speech is instigating hate, which involves provoking hatred against a particular community, race, colour, gender, religion or ethnicity. In this work, we introduce \textit{ProvocationProbe} - a dataset designed to explore what distinguishes instigating hate speech from general hate speech. For this study, we collected around twenty thousand tweets from Twitter, encompassing a total of nine global controversies. These controversies span various themes including racism, politics, and religion. In this paper, i) we present an annotated dataset after comprehensive examination of all the controversies, ii) we also highlight the difference between hate speech and instigating hate speech by identifying distinguishing features, such as targeted identity attacks and reasons for hate.