STeller: An approach for context-aware story detection using different similarity metrics and dense subgraph mining

The real-time information on the Web changes dynamically and surge quickly, which cause considerable difficulty in access to interested information. How to mine hot events, how to analyze the correlation of events and how to organize information structurally are challenging tasks. In this paper, to address these problems, we propose STeller, an approach to mine context-aware story — a series of correlated events. Firstly, we cluster similar pieces of information text into a meme—a piece of information and all its variants. This is also the process of information flow tracking. We view a meme as a fine-grained event. Then we use three novel efficient similarity metrics to measure content similarity and correlation of events. The social stream can be transformed into co-occurrence graph and we define the context-aware story as a novel dense subgraph type called (λ,d)-Clique. Lastly, two corresponding dense subgraph mining algorithms are developed to extract (λ,d)-Clique structure. We also perform detailed experiments on real news data and the results demonstrate the value of our work.