Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Counterspeakers' Perspectives: Unveiling Barriers and AI Needs in the Fight against Online Hate

Jimin Mun, Cathy Buerger, Jenny T. Liang, Joshua Garland, Maarten Sap

TL;DR

The paper investigates how AI can support counterspeech against online hate while preserving the empathy and agency central to the practice. It employs a mixed-methods design with semi-structured interviews of 10 experienced counterspeakers and a large MTurk survey of 342 participants to identify barriers and envisioned AI roles. Findings reveal four barrier themes—limited resources, lack of training, unclear impact, and personal harms—alongside overarching concerns about authenticity, agency, and tool functionality, which inform design recommendations for transparent, mindful, and morally engaged AI aids. The work highlights a gap between current AI counterspeech research and user-centered needs, advocating participatory design to build empowering, safe, and culturally aware AI assistance that preserves meaning and human agency in counterspeech.

Abstract

Counterspeech, i.e., direct responses against hate speech, has become an important tool to address the increasing amount of hate online while avoiding censorship. Although AI has been proposed to help scale up counterspeech efforts, this raises questions of how exactly AI could assist in this process, since counterspeech is a deeply empathetic and agentic process for those involved. In this work, we aim to answer this question, by conducting in-depth interviews with 10 extensively experienced counterspeakers and a large scale public survey with 342 everyday social media users. In participant responses, we identified four main types of barriers and AI needs related to resources, training, impact, and personal harms. However, our results also revealed overarching concerns of authenticity, agency, and functionality in using AI tools for counterspeech. To conclude, we discuss considerations for designing AI assistants that lower counterspeaking barriers without jeopardizing its meaning and purpose.

Counterspeakers' Perspectives: Unveiling Barriers and AI Needs in the Fight against Online Hate

TL;DR

The paper investigates how AI can support counterspeech against online hate while preserving the empathy and agency central to the practice. It employs a mixed-methods design with semi-structured interviews of 10 experienced counterspeakers and a large MTurk survey of 342 participants to identify barriers and envisioned AI roles. Findings reveal four barrier themes—limited resources, lack of training, unclear impact, and personal harms—alongside overarching concerns about authenticity, agency, and tool functionality, which inform design recommendations for transparent, mindful, and morally engaged AI aids. The work highlights a gap between current AI counterspeech research and user-centered needs, advocating participatory design to build empowering, safe, and culturally aware AI assistance that preserves meaning and human agency in counterspeech.

Abstract

Counterspeech, i.e., direct responses against hate speech, has become an important tool to address the increasing amount of hate online while avoiding censorship. Although AI has been proposed to help scale up counterspeech efforts, this raises questions of how exactly AI could assist in this process, since counterspeech is a deeply empathetic and agentic process for those involved. In this work, we aim to answer this question, by conducting in-depth interviews with 10 extensively experienced counterspeakers and a large scale public survey with 342 everyday social media users. In participant responses, we identified four main types of barriers and AI needs related to resources, training, impact, and personal harms. However, our results also revealed overarching concerns of authenticity, agency, and functionality in using AI tools for counterspeech. To conclude, we discuss considerations for designing AI assistants that lower counterspeaking barriers without jeopardizing its meaning and purpose.
Paper Structure (53 sections, 3 figures, 7 tables)

This paper contains 53 sections, 3 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Hate Speech Responses
  4. Counterspeech and Counterspeakers
  5. AI Assistance in Counterspeech
  6. Methodology
  7. Interview Study
  8. Interview Procedure
  9. Participant Recruitment
  10. Survey Study
  11. Survey Questions
  12. Participant Demographics
  13. Analysis Methods
  14. Data Preparation
  15. Qualitative Analysis
  16. ...and 38 more sections

Figures (3)

  • Figure 1: Participant responses on hate speech and counterspeech experience questions and most commonly seen type of hate.
  • Figure 2: An overview of interactions between the themes surfaced in Section \ref{['sec:findings']}. The figure, from left to right, show an overall counterspeech experience surfaced from participant responses. Participants' intrinsic and extrinsic motivation encouraged participants to engage in the counterspeaking process broken down into three steps. Themes found in beneficial AI usage could be rooted in each barrier, shown by the arrows in the figure, and themes found in AI concerns were linked to different aspects of counterspeech including motivations, functionality of AI for counterspeech, and counterspeech as a whole.
  • Figure 3: Survey participant responses on quantitative questions about AI tools.