arXiv:2605.22435v1 Announce Type: new Abstract: Hate speech and misinformation frequently co-occur online, amplifying prejudice and polarization. Given their scale, using Large Language Models (LLMs) to assist expert counterspeech (CS) writing has gained interest, yet prior work has addressed these phenomena separately. We bridge this gap by studying CS generation in contexts where both hate and misinformation co-occur. We test three knowledge-driven generation strategies: first we prompt an LLM with fact-checkers' guidelines and fact-checking articles; secondly, with NGOs' guidelines and repo
The proliferation of hate speech and misinformation online, coupled with advancements in Large Language Models (LLMs), has created an urgent need for automated countermeasures.
This research outlines a method to leverage AI in combating online toxicity and misinformation, crucial for maintaining societal cohesion and the integrity of digital public spaces.
The approach shifts from separately addressing hate speech and misinformation to a unified strategy leveraging LLMs for nuanced counterspeech generation, potentially improving efficacy and scalability.
- · Social Media Platforms
- · Organizations combating online hate/misinformation
- · LLM developers
- · Fact-checking organizations
- · Purveyors of hate speech and misinformation
- · AI models without ethical safeguards
Assisted counterspeech writing becomes more efficient and scalable, enabling quicker responses to harmful online content.
Improved online discourse and reduced polarization could foster healthier digital communities and potentially influence real-world social dynamics.
The development of sophisticated counterspeech LLMs could lead to an 'arms race' between content generation and moderation AI, prompting new ethical and technical challenges.
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