NewsMiner: Multifaceted news analysis for event search

Online news has become increasingly prevalent due to its convenience for information acquisition, and meanwhile the rapid development of social applications enables news generate and spread through various ways at an unprecedented rate. How to organize and integrate news from multiple sources, and how to analyze and present news to users are two challenging problems. In this article, we represent news as a link-centric heterogeneous network and formalize news analysis and mining task as link discovery problem. More specifically, we propose a co-mention and context based knowledge linking method and a topic-level social content alignment method to establish the links between news and external sources (i.e. knowledge base and social content), and introduce a unified probabilistic model for topic extraction and inner relationship discovery within events. We further present a multifaceted ranking strategy to rank the linked events, topics and entities simultaneously. Extensive experiments demonstrate the advantage of the proposed approaches over baseline methods and the online system we developed (i.e. NewsMiner) has been running for more than three years.