document: Mining correlated bursty topic patterns from coordinated text streams Previous work on text mining has almost exclusively focused on a single stream . However , we often have available multiple text streams indexed by the same set of time points ( called coordinated text streams ) , which offer new opportunities for text mining . For example , when a major event happens , all the news articles published by different agencies in different languages tend to cover the same event for a certain period , exhibiting a correlated bursty topic pattern in all the news article streams . In general , mining correlated bursty topic patterns from coordinated text streams can reveal interesting latent associations or events behind these streams . In this paper , we define and study this novel text mining problem . We propose a general probabilistic algorithm which can effectively discover correlated bursty patterns and their bursty periods across text streams even if the streams have completely different vocabularies ( e.g. , English vs Chinese ) . Evaluation of the proposed method on a news data set and a literature data set shows that it can effectively discover quite meaningful topic patterns from both data sets : the patterns discovered from the news data set accurately reveal the major common events covered in the two streams of news articles ( in English and Chinese , respectively ) , while the patterns discovered from two database publication streams match well with the major research paradigm shifts in database research . Since the proposed method is general and does not require the streams to share vocabulary , it can be applied to any coordinated text streams to discover correlated topic patterns that burst in multiple streams in the same period .
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