Predicting the victims of hate speech on microblogging platforms.

Hate speech constitutes a major problem on microblogging platforms, with automatic detection being a growing research area. Most existing works focus on analyzing the content of social media posts. Our study shifts focus to predicting which users are likely to become targets of hate speech.

Towards understanding the role of content-based and contextualized features in detecting abuse on Twitter.

This paper presents a novel approach for detecting abuse on Twitter. Abusive posts have become a major problem for social media platforms like Twitter. It is important to identify abuse to mitigate its potential harm.