The article examines methodologies for the detection and generation of counterspeech, highlighting the evolution of automated classifiers primarily focused on binary classification and multi-label tasks. These classifiers analyze social media interactions concerning abusive language. Furthermore, the generation of counterspeech leverages transformer-based models to optimize effectiveness across a multilingual context while emphasizing factors such as politeness and grammatical diversity. The research indicates a growing integration of computational approaches in managing hate speech on digital platforms.
Automated classifiers in counterspeech detection largely focus on binary classification, determining if a text is counterspeech, with efforts extending to multi-label tasks.
The automation of counterspeech generation is facilitated by transformer-based language models, which are fine-tuned for various aspects like multilinguality and politeness.
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