🤖 AI Summary
This study addresses the challenge of countering hate speech in online dialogues by systematically investigating the distribution and efficacy of three persuasive strategies—rational, emotional, and credibility-based—in both open (single-turn) and closed (multi-turn) conversational settings, while comparing human-authored versus machine-generated counter-speech. It presents the first empirical evaluation across multiple sensitive topics (race, gender, religion), employing a mixed-methods approach integrating semantic annotation, discourse act modeling, and statistical analysis. Results reveal that human responses exhibit a strong preference for rational strategies, whereas AI-generated responses rely disproportionately on emotional appeals; critically, rational framing significantly increases user support for counter-speech. This work establishes a novel, interpretable, and empirically grounded framework for evaluating and designing effective, accountable anti-hate interventions.
📝 Abstract
Examining the factors that the counterspeech uses are at the core of understanding the optimal methods for confronting hate speech online. Various studies have assessed the emotional base factors used in counter speech, such as emotional empathy, offensiveness, and hostility. To better understand the counterspeech used in conversations, this study distills persuasion modes into reason, emotion, and credibility and evaluates their use in two types of conversation interactions: closed (multi-turn) and open (single-turn) concerning racism, sexism, and religious bigotry. The evaluation covers the distinct behaviors seen with human-sourced as opposed to machine-generated counterspeech. It also assesses the interplay between the stance taken and the mode of persuasion seen in the counterspeech. Notably, we observe nuanced differences in the counterspeech persuasion modes used in open and closed interactions, especially in terms of the topic, with a general tendency to use reason as a persuasion mode to express the counterpoint to hate comments. The machine-generated counterspeech tends to exhibit an emotional persuasion mode, while human counters lean toward reason. Furthermore, our study shows that reason tends to obtain more supportive replies than other persuasion modes. The findings highlight the potential for incorporating persuasion modes into studies about countering hate speech, as they can serve as an optimal means of explainability and pave the way for the further adoption of the reply's stance and the role it plays in assessing what comprises the optimal counterspeech.