Countering hate on social media

Study: Countering hate on social media
Figure 1 from the paper: Examples of Twitter conversations (reply trees) with labeled hate (red), counter (blue), and neutral speech (white). The root node is shown as a large square. Credit: Garland et al, EMNLP 2020

The rise of online hate speech is a disturbing, growing trend in countries around the world, with serious psychological consequences and the potential to impact, and even contribute to, real-world violence. Citizen-generated counter speech may help discourage hateful online rhetoric, but it has been difficult to quantify and study. Until recently, studies have been limited to small-scale, hand-labeled endeavors.

A new paper published in the proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) offers a framework for studying the dynamics of online hate and counter speech. The paper offers the first large-scale classification of millions of such interactions on Twitter. The authors developed a learning algorithm to assess data

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