Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation

Large language models (LLMs) incorporated with Retrieval-Augmented Generation (RAG) have demonstrated powerful capabilities in generating counterspeech against misinformation. However, current studies rely on limited evidence and offer less control over final outputs. To address these challenges, we propose a Multi-agent Retrieval-Augmented Framework to generate counterspeech against health misinformation, incorporating multiple LLMs to optimize knowledge retrieval, evidence enhancement, and response refinement. Our approach integrates both static and dynamic evidence, ensuring that the generated counterspeech is relevant, well-grounded, and up-to-date. Our method outperforms baseline approaches in politeness, relevance, informativeness, and factual accuracy, demonstrating its effectiveness in generating high-quality counterspeech. To further validate our approach, we conduct ablation studies to verify the necessity of each component in our framework. Furthermore, human evaluations reveal that refinement significantly enhances counterspeech quality and obtains human preference.

Echoes of Discord: Forecasting Hater Reactions to Counterspeech

Hate speech (HS) erodes the inclusiveness of online users and propagates negativity and division. Counterspeech has been recognized as a way to mitigate the harmful consequences. While some research has investigated the impact of user-generated counterspeech on social media platforms, few have examined and modeled haters' reactions toward counterspeech, despite the immediate alteration of haters' attitudes being an important aspect of counterspeech. This study fills the gap by analyzing the impact of counterspeech from the hater's perspective, focusing on whether the counterspeech leads the hater to reenter the conversation and if the reentry is hateful. We compile the Reddit Echoes of Hate dataset (ReEco), which consists of triple-turn conversations featuring haters' reactions, to assess the impact of counterspeech. The linguistic analysis sheds insights on the language of counterspeech to hate eliciting different haters' reactions. Experimental results demonstrate that the 3-way classification model outperforms the two-stage reaction predictor, which first predicts reentry and then determines the reentry type. We conclude the study with an assessment showing the most common errors identified by the best-performing model.

Assessing the Human Likeness of AI-Generated Counterspeech

Counterspeech is a targeted response to counteract and challenge abusive or hateful content. It can effectively curb the spread of hatred and foster constructive online communication. Previous studies have proposed different strategies for automatically generated counterspeech. Evaluations, however, focus on the relevance, surface form, and other shallow linguistic characteristics. In this paper, we investigate the human likeness of AI-generated counterspeech, a critical factor influencing effectiveness. We implement and evaluate several LLM-based generation strategies, and discover that AI-generated and human-written counterspeech can be easily distinguished by both simple classifiers and humans. Further, we reveal differences in linguistic characteristics, politeness, and specificity.

Outcome-Constrained Large Language Models for Countering Hate Speech

Counterspeech that challenges or responds to hate speech has been seen as an alternative to mitigate the negative impact of hate speech and foster productive online communications. Research endeavors have been directed to using language models for the automatic generation of counterspeech to assist efforts in combating online hate. Existing research focuses on the generation of counterspeech with certain linguistic attributes, such as being polite, informative, and intent-driven. However, it remains unclear what impact the counterspeech might have in an online environment. We first explore methods that utilize large language models (LLM) to generate counterspeech constrained by potential conversation outcomes. We build two conversation outcome classifiers that predict the incivility level and the hater reentry behavior following replies to hate with Reddit data, then propose four methods to incorporate the desired outcomes, i.e., low conversation incivility and non-hateful hater reentry, into the text generation process, including Prompt with Instructions, Prompt and Select, LLM finetune, and LLM transformer reinforcement learning (TRL). Evaluation results show effective strategies to generate outcome-constrained counterspeech and the linguistic characteristics of texts generated by different methods.