Generating Counter Narratives against Online Hate Speech: Data and Strategies

By Serra Sinem Tekiroğlu, Yi-Ling Chung, Marco Guerini. 2020

128 citations recorded by the source corpus.

Recently research has started focusing on avoiding undesired effects that come with content moderation, such as censorship and overblocking, when dealing with hatred online. The core idea is to directly intervene in the discussion with textual responses that are meant to counter the hate content and prevent it from further spreading. Accordingly, automation strategies, such as natural language generation, are beginning to be investigated. Still, they suffer from the lack of sufficient amount of quality data and tend to produce generic/repetitive responses. Being aware of the aforementioned limitations, we present a study on how to collect responses to hate effectively, employing large scale unsupervised language models such as GPT-2 for the generation of silver data, and the best annotation strategies/neural architectures that can be used for data filtering before expert validation/post-editing.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Helena Bonaldi
2. Sara Dellantonio
3. Marco Guerini
4. Serra Sinem Tekiroğlu

Citations in the corpus

• Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering (2022, methodology): “A more recent study proposes a hybrid approach where an LM is trained to generate HS/CN pairs that are validated and post-edited by annotators (Tekiro˘glu et al., 2020).”