Event detection in social media platforms has become an important task. It facilities exploration and browsing of events with early plans for preventive measures. The main challenges in event detection lie in the characteristics of social media data, which are short/conversational, heterogeneous and live. Most of existing methods rely only on the textual information while ignoring the visual content as well as the intrinsic correlation among the heterogeneous social media data. In this paper, we propose an event detection method, which generates an intermediate semantic entity, named
(MC), to explore the highly correlated information among the noisy and short microblogs. The heterogeneous social media data is formulated as a hypergraph and the highly correlated ones are grouped to generate the MCs. Based on these MCs, a bipartite graph is constructed and partitioned to detect social events. The proposed method has been evaluated on the Brand-Social-Net dataset. Experimental results and comparison with state-of-the-art methods demonstrate the effectiveness of the proposed approach. Further evaluation has shown that the use of the visual content can significantly improve the event detection performance.