This study evaluates the effectiveness of ChatGPT, an advanced AI model for natural language processing, in identifying targeting and inappropriate language in online comments. With the increasing challenge of moderating vast volumes of user-generated content on social network sites, the role of AI in content moderation has gained prominence. We compared ChatGPT's performance against crowd-sourced annotations and expert evaluations to assess its accuracy, scope of detection, and consistency. Our findings highlight that ChatGPT performs well in detecting inappropriate content, showing notable improvements in accuracy through iterative refinements, particularly in Version 6. However, its performance in targeting language detection showed variability, with higher false positive rates compared to expert judgments. This study contributes to the field by demonstrating the potential of AI models like ChatGPT to enhance automated content moderation systems while also identifying areas for further improvement. The results underscore the importance of continuous model refinement and contextual understanding to better support automated moderation and mitigate harmful online behavior.

Today, there are a large number of online discussion fora on the internet which are meant for users to express, discuss and exchange their views and opinions on various topics. In such fora, it has been often observed that user conversations sometimes quickly derail and become inappropriate such as hurling abuses, passing rude and discourteous comments on individuals or certain groups/communities. Similarly, some virtual agents or bots have also been found to respond back to users with inappropriate messages. As a result, inappropriate messages or comments are turning into an online menace slowly degrading the effectiveness of user experiences. Hence, automatic detection and filtering of such inappropriate language has become an important problem for improving the quality of conversations with users as well as virtual agents.