Detecting events from massive social media data in social networks can facilitate browsing, search, and monitoring of real-time events by corporations, governments, and users. The short, conversational, heterogeneous, and real-time characteristics of social media data bring great challenges for event detection. The existing event detection approaches rely mainly on textual information, while the visual content of microblogs and the intrinsic correlation among the heterogeneous data are scarcely explored. To deal with the above challenges, we propose a novel real-time event detection method by generating an intermediate semantic level from social multimedia data, named microblog clique (MC), which is able to explore the high correlations among different microblogs. Specifically, the proposed method comprises three stages. First, the heterogeneous data in microblogs is formulated in a hypergraph structure. Hypergraph cut is conducted to group the highly correlated microblogs with the same topics as the MCs, which can address the information inadequateness and data sparseness issues. Second, a bipartite graph is constructed based on the generated MCs and the transfer cut partition is performed to detect the events. Finally, for new incoming microblogs, incremental hypergraph is constructed based on the latest MCs to generate new MCs, which are classified by bipartite graph partition into existing events or new ones. Extensive experiments are conducted on the events in the Brand-Social-Net dataset and the results demonstrate the superiority of the proposed method, as compared to the state-of-the-art approaches.