Hatred Stems from Ignorance! Distillation of the Persuasion Modes in Countering Conversational Hate Speech

• URL: http://arxiv.org/abs/2403.15449v2
• Date: Tue, 16 Jul 2024 07:58:42 GMT
• Title: Hatred Stems from Ignorance! Distillation of the Persuasion Modes in Countering Conversational Hate Speech
• Authors: Ghadi Alyahya, Abeer Aldayel,
• Abstract summary: This study distills persuasion modes into reason, emotion, and credibility. It evaluates their use in two types of conversation interactions: closed (multi-turn) and open (single-turn) concerning racism, sexism, and religious bigotry.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• Behind the Counter: Exploring the Motivations and Barriers of Online Counterspeech Writing [6.790819952175892]
  Having been a target of online hate is a key driver of frequent online counterspeech engagement. People differ in their motivations and barriers towards engaging in online counterspeech across different demographic groups.
  arXiv  Detail & Related papers  (2024-03-25T18:56:35Z)
• Consolidating Strategies for Countering Hate Speech Using Persuasive Dialogues [3.8979646385036175]
  We explore controllable strategies for generating counter-arguments to hateful comments in online conversations. Using automatic and human evaluations, we determine the best combination of features that generate fluent, argumentative, and logically sound arguments. We share developed computational models for automatically annotating text with such features, and a silver-standard annotated version of an existing hate speech dialog corpora.
  arXiv  Detail & Related papers  (2024-01-15T16:31:18Z)
• DisCGen: A Framework for Discourse-Informed Counterspeech Generation [34.75404551612012]
  We propose a framework based on theories of discourse to study the inferential links that connect counter speeches to hateful comments. We present a process for collecting an in-the-wild dataset of counterspeech from Reddit. We show that by using our dataset and framework, large language models can generate contextually-grounded counterspeech informed by theories of discourse.
  arXiv  Detail & Related papers  (2023-11-29T23:20:17Z)
• Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language [18.560379338032558]
  We draw from psychology and philosophy literature to craft six psychologically inspired strategies to challenge the underlying stereotypical implications of hateful language. We show that human-written counterspeech uses strategies that are more specific to the implied stereotype, whereas machine-generated counterspeech uses less specific strategies. Our findings point to the importance of accounting for the underlying stereotypical implications of speech when generating counterspeech.
  arXiv  Detail & Related papers  (2023-10-31T21:33:46Z)
• CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network [52.85130555886915]
  CoSyn is a context-synergized neural network that explicitly incorporates user- and conversational context for detecting implicit hate speech in online conversations. We show that CoSyn outperforms all our baselines in detecting implicit hate speech with absolute improvements in the range of 1.24% - 57.8%.
  arXiv  Detail & Related papers  (2023-03-02T17:30:43Z)
• Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion [52.49981085431061]
  Enhancing people's ability to write persuasive arguments could contribute to the effectiveness and civility in online communication. We derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions. Persua is an interactive visual system that provides example-based guidance on persuasive strategies to enhance the persuasiveness of arguments.
  arXiv  Detail & Related papers  (2022-04-16T08:07:53Z)
• E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches [57.279044079196105]
  E-ffective is a visual analytic system allowing speaking experts and novices to analyze both the role of speech factors and their contribution in effective speeches. Two novel visualizations include E-spiral (that shows the emotional shifts in speeches in a visually compact way) and E-script (that connects speech content with key speech delivery information.
  arXiv  Detail & Related papers  (2021-10-28T06:14:27Z)
• Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes [50.569762345799354]
  We argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other's emotion from his or her utterance and (ii) reflecting those specific words in the response generation. Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label.
  arXiv  Detail & Related papers  (2021-09-18T04:22:49Z)
• Impact and dynamics of hate and counter speech online [0.0]
  Citizen-generated counter speech is a promising way to fight hate speech and promote peaceful, non-polarized discourse. We analyze 180,000 political conversations that took place on German Twitter over four years.
  arXiv  Detail & Related papers  (2020-09-16T01:43:28Z)
• "Notic My Speech" -- Blending Speech Patterns With Multimedia [65.91370924641862]
  We propose a view-temporal attention mechanism to model both the view dependence and the visemic importance in speech recognition and understanding. Our proposed method outperformed the existing work by 4.99% in terms of the viseme error rate. We show that there is a strong correlation between our model's understanding of multi-view speech and the human perception.
  arXiv  Detail & Related papers  (2020-06-12T06:51:55Z)
• What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process [78.4766663287415]
  This paper presents a study that automatically analyzes the key factors in argument persuasiveness. We propose a novel neural model that is able to track the changes of latent topics and discourse in argumentative conversations.
  arXiv  Detail & Related papers  (2020-02-10T04:27:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.