Social media user partitioning based on ensemble clustering

In Web2.0 era, social media platforms are bearing huge customer base and excessively abundant information resources. On one hand, information consumers spend a lot of time in information search. On the other hand, information providers are seeking effective methods to recognize potential customers, push targeting advertising and provide personalized information services. Generally, mining user-generated content (UGC) to discover user preferences becomes the main channel for user modeling and customer partitioning. However, on social media platforms, user preferences were often manifested in the user-defined tags, online social behaviors as well as the UGC texts. The paper proposed a social-media user partitioning model based on heterogeneous information fusion and ensemble clustering. In the model, online social behaviors and user-defined interest tags are combined with UGC texts respectively to generate basic partitions of social media users. Then, basic partitions are fused into a consensus partition based on the voting mechanism for the final user partitioning. Experiments on real world data sets demonstrate the effectiveness of the proposed model.