  Harmful content is pervasive on social media, poisoning online communities
and negatively impacting participation. A common approach to address this issue
is to develop detection models that rely on human annotations. However, the
tasks required to build such models expose annotators to harmful and offensive
content and may require significant time and cost to complete. Generative AI
models have the potential to understand and detect harmful content. To
investigate this potential, we used ChatGPT and compared its performance with
MTurker annotations for three frequently discussed concepts related to harmful
content: Hateful, Offensive, and Toxic (HOT). We designed five prompts to
interact with ChatGPT and conducted four experiments eliciting HOT
classifications. Our results show that ChatGPT can achieve an accuracy of
approximately 80% when compared to MTurker annotations. Specifically, the model
displays a more consistent classification for non-HOT comments than HOT
comments compared to human annotations. Our findings also suggest that ChatGPT
classifications align with provided HOT definitions, but ChatGPT classifies
"hateful" and "offensive" as subsets of "toxic." Moreover, the choice of
prompts used to interact with ChatGPT impacts its performance. Based on these
in-sights, our study provides several meaningful implications for employing
ChatGPT to detect HOT content, particularly regarding the reliability and
consistency of its performance, its understand-ing and reasoning of the HOT
concept, and the impact of prompts on its performance. Overall, our study
provides guidance about the potential of using generative AI models to moderate
large volumes of user-generated content on social media.
