Contextualized Counterspeech: Strategies for Adaptation,
  Personalization, and Evaluation
Authors: name: Lorenzo Cima, name: Alessio Miaschi, name: Amaury Trujillo, name: Marco Avvenuti, name: Felice Dell'Orletta, name: Stefano Cresci

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.