Detecting latent topics in social media texts is a meaningful task, and the short and informal posts will cause serious data sparsity. Additionally, models based on variational auto-encoders (VAEs) ignore the social relationships among users during topic inference and VAE assumes that each input data point is independent. This results in the lack of correlation information between the inferred latent topic variables and incoherent topics. Social network structure information can not only provide clues for aggregating contextual messages but also indicate topic correlation among users. Therefore, this study proposes to utilize the microblog topic model based on message passing and graph prior distribution. This model can encode richer context information by graph convolution network (GCN) and integrate the interactive relationship of users by graph prior distribution during VAE topic inference to better understand the complex correlation among multiple data points and mine social media topic information. The experiments on three actual datasets validate the effectiveness of the proposed model.