Social media topic detection aims to mine latent topic information from large-scale short posts. It is a challenging task as posts are short in form and informal in expression and user interactions in social media are complex and diverse. Previous studies only consider the textual content of posts or simultaneously model social contexts in homogeneous situations, ignoring the heterogeneity of social networks. However, different types of user interactions, such as forwarding and commenting, could suggest different behavior patterns and interest preferences and reflect different attention to the topic and understanding of the topic. In addition, different users have different influences on the development and evolution of the same topic. Specifically, compared with ordinary users, the leading authoritative users in a community play a more important role in topic inference. For the above reasons, this study proposes a novel multi-view topic model (MVTM) to infer more complete and coherent topics by encoding heterogeneous social contexts in the microblog conversation network. For this purpose, an attributed multiplex heterogeneous conversation network is built according to the interaction relationships among users and decomposed into multiple views with different interaction semantics. Then, the embedded representation of specific views is obtained by leveraging neighbor-level and interaction-level attention mechanisms, with due consideration given to different types of interactions and the importance of different users. Finally, a multi-view neural variational inference method is designed to capture the deep correlations among different views and adaptively balance their consistency and independence, thereby obtaining more coherent topics. Experiments are conducted on a Sina Weibo dataset covering three months, and the results reveal the effectiveness of the proposed method.