Hatred Stems from Ignorance! Distillation of the Persuasion Modes in Countering Conversational Hate Speech
<|reference_start|>Hatred Stems from Ignorance! Distillation of the Persuasion Modes in Countering Conversational Hate Speech: 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.<|reference_end|>
@article{alyahya2024hatred,
    title={Hatred Stems from Ignorance! Distillation of the Persuasion Modes in
  Countering Conversational Hate Speech},
    author={Ghadi Alyahya, Abeer Aldayel},
    journal={arXiv preprint arXiv:2403.15449},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15449},
    primaryClass={cs.CL cs.AI}
}