ProvocationProbe: Instigating Hate Speech Dataset from Twitter
This work addresses the need for better detection of instigating hate speech on social media platforms, which is an incremental contribution to hate speech mitigation efforts.
The authors tackled the problem of distinguishing instigating hate speech from general hate speech by introducing the ProvocationProbe dataset, which includes around twenty thousand annotated tweets from nine global controversies, and they identified key distinguishing features like targeted identity attacks and reasons for hate.
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.