WokeGPT: Improving Counterspeech Generation Against Online Hate Speech by Intelligently Augmenting Datasets Using a Novel Metric

Halim, Sadaf Md; Irtiza, Saquib; Hu, Yibo; Khan, Latifur; Thuraisingham, Bhavani

doi:10.1109/ijcnn54540.2023.10191114

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Our study participants described many different barriers to counterspeech at various stages of engagement as discussed in Section 4.1. However, previous works in AI have focused on a narrow set of challenges for assistance and automation: automatic counterspeech generation [53,55,108,149] and analysis and detection of both hate speech and counterspeech [43,63]. Our findings paint a broader picture, especially through the theory of counterspeech engagement, and highlight where tools and resources would encourage bystander intervention towards countering hate.…”

Section: Possible Tools To Address Barriers To Counterspeechmentioning

confidence: 79%

“…Automatic detection [147] and computational analysis of large scale counterspeech [43] have been used to understand its characteristics and to inform effective content. Prior works on automatic generation of counterspeech [91,149] relied on curated or scraped datasets [25,63] and evaluation metrics based on correct countering claims [55] or emotion and politeness [108]. Some methods used limited response intent such as question, denouncing, and humor in dataset [25] and as part of the generation method [53].…”

Section: Ai Assistance In Counterspeechmentioning

confidence: 99%

See 1 more Smart Citation

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

Mun,

Buerger,

Liang

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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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. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI.

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