title: Contextualized Counterspeech: Strategies for Adaptation,
    Personalization, and Evaluation
summary: 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.
paper_authors._id: 67596ab7d8405153709c28cf
paper_authors.name: Lorenzo Cima
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-11T14:00:33.127Z
paper_authors._id: 655f1923324de022a39223ee
paper_authors.avatarUrl: /avatars/a7bdca0b554a379daed493cc5ab1359a.svg
paper_authors.fullname: Lorenzo Cima
paper_authors.type: user
paper_authors.user: LCima
paper_authors._id: 67596ab7d8405153709c28d0
paper_authors.name: Alessio Miaschi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-11T13:39:12.918Z
paper_authors._id: 62e3fe792a8df5b22feed15e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62e3fe792a8df5b22feed15e/iy8J4oGhW8Nr9QAgoH8ES.jpeg
paper_authors.fullname: Alessio Miaschi
paper_authors.type: user
paper_authors.user: alemiaschi
paper_authors._id: 67596ab7d8405153709c28d1
paper_authors.name: Amaury Trujillo
paper_authors._id: 67596ab7d8405153709c28d2
paper_authors.name: Marco Avvenuti
paper_authors._id: 67596ab7d8405153709c28d3
paper_authors.name: Felice Dell'Orletta
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-11T14:01:31.477Z
paper_authors._id: 663ca4637527788a57f4d73f
paper_authors.avatarUrl: /avatars/797e68ee21492a753df82a9f7c2e800e.svg
paper_authors.fullname: Felice Dell'Orletta
paper_authors.type: user
paper_authors.user: Falice10
paper_authors._id: 67596ab7d8405153709c28d4
paper_authors.name: Stefano Cresci
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-13T15:23:10.049Z
paper_authors._id: 675bf5ec1042a88249a8283d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/HC-7URJWGf0RQwWrTSQdk.png
paper_authors.fullname: Stefano Cresci
paper_authors.type: user
paper_authors.user: scresci
paper_title: Contextualized Counterspeech: Strategies for Adaptation,
    Personalization, and Evaluation
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: LLaMA2-13B contextualized counterspeech generic counterspeech human evaluations crowdsourcing experiment content moderation