Towards Automatic Generation of Messages Countering Online Hate Speech and Microaggressions (2022)

Abstract: With the widespread use of social media, online hate is increasing, and microaggressions are receiving attention. We explore the potential for using pretrained 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. We also propose a human evaluation perspective; offensiveness, stance, and informativeness. After obtaining 306 counterspeech and 42 microintervention messages generated by GPT-2, 3, Neo, we conducted a human evaluation using Amazon Mechanical Turk. The results indicate the potential of using prompting in the proposed generation task. All the generated texts along with the annotation are published to encourage future research on countering hate and microaggressions online.

## introduction concomitant with social media becoming a major means of communication, online abusive language is increasing. as abusive language can be harmful, countering it is an important way to reduce the level of danger on the internet. hate speech is arguably the most well-studied form of abusive language across time and regions. ## conclusion this paper explored the possibility of using pretrained language models on the counternarrative generation task against hate speech and harmful social implications. we used three lms to generate counternarratives via prompting and conducted a human evaluation exercise to ascertain the quality of the generated counternarratives using "offensiveness," "stance," and "informativeness" as our evaluation criteria. furthermore, we analyzed the models' performance based on how many generated counternarratives are safe, informative, and opposing to abusive language input. the overall results show the potential of language models to generate controlled content using prompting, which requires only some examples of inputs and desired outputs, compared to finetuning, which is computation intensive. among the three lms we tested, gpt-3 performed the best in terms of generating safe, informative counternarratives that oppose abusive language input. however, some of the counternarratives considered informative contained misinformation or hallucinated contents. applying a fact-checking process to the generated contents is a possible future task.