Contextualized Counterspeech: Strategies for Adaptation,
  Personalization, and Evaluation
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.
67596ab7d8405153709c28cf
Lorenzo Cima
admin_assigned
2024-12-11T14:00:33.127Z
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/avatars/a7bdca0b554a379daed493cc5ab1359a.svg
Lorenzo Cima
user
LCima
67596ab7d8405153709c28d0
Alessio Miaschi
claimed_verified
2024-12-11T13:39:12.918Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/62e3fe792a8df5b22feed15e/iy8J4oGhW8Nr9QAgoH8ES.jpeg
Alessio Miaschi
user
alemiaschi
67596ab7d8405153709c28d1
Amaury Trujillo
67596ab7d8405153709c28d2
Marco Avvenuti
67596ab7d8405153709c28d3
Felice Dell'Orletta
admin_assigned
2024-12-11T14:01:31.477Z
663ca4637527788a57f4d73f
/avatars/797e68ee21492a753df82a9f7c2e800e.svg
Felice Dell'Orletta
user
Falice10
67596ab7d8405153709c28d4
Stefano Cresci
claimed_verified
2024-12-13T15:23:10.049Z
675bf5ec1042a88249a8283d
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/HC-7URJWGf0RQwWrTSQdk.png
Stefano Cresci
user
scresci
Contextualized Counterspeech: Strategies for Adaptation,
  Personalization, and Evaluation
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.
Contextualized AI-generated counterspeech, using an LLaMA2-13B model with various configurations, outperforms generic counterspeech in terms of adequacy and persuasiveness, as shown through human evaluations, suggesting the need for nuanced evaluation methods and increased human-AI collaboration in content moderation.
LLaMA2-13B contextualized counterspeech generic counterspeech human evaluations crowdsourcing experiment content moderation