Content moderation is a common intervention strategy for reviewing user-generated content on social media platforms. Engaging users in content moderation is promising for making ethical and fair moderation decisions. A few studies that have considered user engagement in content moderation have primarily focused on classifying user-generated comments, rather than leveraging the information of user engagement to make a moderation decision on user-generated posts. Moreover, how to extract information from user engagement to enhance content moderation remains unclear. To address the above-mentioned limitations, this study proposes a framework for user engagement-enhanced moderation of user-generated posts. Specifically, it incorporates the credibility and stance of user-generated content into graph learning. Our empirical evaluation shows that the models based on our proposed framework outperform the stateof-the-art deep learning models in making moderation decisions for user-generated posts. The findings of this study have implications for augmenting the moderation of social media content and for improving the safety and success of online communities.