Topic detection in social media is a hot yet challenging issue in social computing given most data there are heterogeneous, time-evolving and linguistically ambiguous. In this paper, we explore the idea of achieving this goal through complex network modeling which has demonstrated excellent interpretability of the real world. Specifically, a complex network was constructed based on pre-processed topic words where two parameters, namely the emergency and correlation coefficients, were also introduced to allow us to filter social data through the network as well as determine their possible correlations. This approach was then applied to analyze 600,000 messages by teenager users in Weibo.com to identify overlapping communities with the help of the well-established algorithm EAGLE. It was demonstrated that, compared to other popular approaches such as CONGO and Peacock a much better Q-value results has been obtained by the method proposed here.
