"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."
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.
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