Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation
Lorenzo Cima, Alessio Miaschi, Amaury Trujillo, Marco Avvenuti, Felice Dell'Orletta, Stefano Cresci
2024-12-11
Summary
This paper talks about contextualized counterspeech, a method for using AI to create tailored responses that help reduce online hate speech by promoting civil conversation.
What's the problem?
Online hate speech is a significant issue that can harm individuals and society. Current AI methods for generating counterspeech often use generic responses that do not consider the specific context of the hateful comments. This one-size-fits-all approach can lead to ineffective or even counterproductive replies, failing to encourage more positive interactions.
What's the solution?
The authors propose a new strategy that involves creating personalized counterspeech based on the context of the original hateful message. They trained a large language model (LLaMA2-13B) to generate these tailored responses by experimenting with different configurations and evaluating their effectiveness through both quantitative measures and human feedback. Their approach shows that contextualized counterspeech performs better than generic responses in being persuasive and relevant, thus effectively addressing the issues at hand.
Why it matters?
This research is important because it highlights how AI can be used to combat online toxicity more effectively by adapting responses to fit the specific situation. By improving the quality of counterspeech, this method can help foster healthier online discussions and reduce the prevalence of hate speech, ultimately contributing to a safer digital environment.
Abstract
AI-generated counterspeech offers a promising and scalable strategy to curb online toxicity through direct replies that promote civil discourse. However, current counterspeech is one-size-fits-all, lacking adaptation to the moderation context and the users involved. We propose and evaluate multiple strategies for generating tailored counterspeech that is adapted to the moderation context and personalized for the moderated user. We instruct an LLaMA2-13B model to generate counterspeech, experimenting with various configurations based on different contextual information and fine-tuning strategies. We identify the configurations that generate persuasive counterspeech through a combination of quantitative indicators and human evaluations collected via a pre-registered mixed-design crowdsourcing experiment. Results show that contextualized counterspeech can significantly outperform state-of-the-art generic counterspeech in adequacy and persuasiveness, without compromising other characteristics. Our findings also reveal a poor correlation between quantitative indicators and human evaluations, suggesting that these methods assess different aspects and highlighting the need for nuanced evaluation methodologies. The effectiveness of contextualized AI-generated counterspeech and the divergence between human and algorithmic evaluations underscore the importance of increased human-AI collaboration in content moderation.