  Fostering a collaborative and inclusive environment is crucial for the
sustained progress of open source development. However, the prevalence of
negative discourse, often manifested as toxic comments, poses significant
challenges to developer well-being and productivity. To identify such
negativity in project communications, especially within large projects,
automated toxicity detection models are necessary. To train these models
effectively, we need large software engineering-specific toxicity datasets.
However, such datasets are limited in availability and often exhibit imbalance
(e.g., only 6 in 1000 GitHub issues are toxic), posing challenges for training
effective toxicity detection models. To address this problem, we explore a
zero-shot LLM (ChatGPT) that is pre-trained on massive datasets but without
being fine-tuned specifically for the task of detecting toxicity in
software-related text. Our preliminary evaluation indicates that ChatGPT shows
promise in detecting toxicity in GitHub, and warrants further investigation. We
experimented with various prompts, including those designed for justifying
model outputs, thereby enhancing model interpretability and paving the way for
potential integration of ChatGPT-enabled toxicity detection into developer
communication channels.
