Assessing the Human Likeness of AI-Generated Counterspeech

• URL: http://arxiv.org/abs/2410.11007v1
• Date: Mon, 14 Oct 2024 18:48:47 GMT
• Title: Assessing the Human Likeness of AI-Generated Counterspeech
• Authors: Xiaoying Song, Sujana Mamidisetty, Eduardo Blanco, Lingzi Hong,
• Abstract summary: Counterspeech is a targeted response to counteract and challenge abusive or hateful content. Previous studies have proposed different strategies for automatically generated counterspeech. We investigate the human likeness of AI-generated counterspeech, a critical factor influencing effectiveness.
• Score: 10.434435022492723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Counterspeech is a targeted response to counteract and challenge abusive or hateful content. It can effectively curb the spread of hatred and foster constructive online communication. Previous studies have proposed different strategies for automatically generated counterspeech. Evaluations, however, focus on the relevance, surface form, and other shallow linguistic characteristics. In this paper, we investigate the human likeness of AI-generated counterspeech, a critical factor influencing effectiveness. We implement and evaluate several LLM-based generation strategies, and discover that AI-generated and human-written counterspeech can be easily distinguished by both simple classifiers and humans. Further, we reveal differences in linguistic characteristics, politeness, and specificity.

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