Towards Automatic Generation of Messages Countering Online Hate Speech and Microaggressions

Abstract

Warning: This paper discusses and contains content that may be deemed offensive or upsetting.With the widespread use of social media, online hate is increasing, and microaggressions, unintentional offensive remarks in everyday life (Sue et al., 2007), are receiving attention. We explore the possibility of using pre-trained language models to automatically generate messages that combat the associated offensive texts. Specifically, we focus on using prompting to steer model generation as it requires less data and computation than fine-tuning and shows the potential for using prompting in the proposed generation task. We also propose a human evaluation perspective; offensiveness, stance, and informativeness. After obtaining 306 counterspeech and 42 micro intervention messages generated by GPT-2, textscGPT-Neo, and textscGPT-3, we conducted a human evaluation using Amazon Mechanical Turk and found that GPT-3 produces messages of the highest quality among three systems. Also, We discuss the pros and cons of using our evaluation perspectives. We release a corpus of countering hate speech and microaggressions (CHASM), annotated machine-generated counternarratives along with the annotation to promote further research on automatic counternarrative generation and its evaluation.

Accountability Issues, Online Covert Hate Speech, and the Efficacy of Counter‐Speech

“…A message that responds to the stereotyping that is core to hate speech offers readers another point of view and a chance to "take back" cyberspace. In fact, it may be our responsibility as forum participants to address such issues rather than let them pass unattended, as the use of automatic bots to respond to hate speech is a very recent solution to counter the massive number of message exchanges (Ashida & Komachi, 2022).…”