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With the overwhelming information from social media networks and news portals, it is crucial to provide users a complete package of visual and textual information with popular interests automatically. To this concern, we present a news detection and pushing system, called Me-Digger (Multimedia News Digger), which not only effectively detects emerging topics from social streams but also provides the corresponding information in multiple modalities. Me-digger is the first systematic effort to leverage three sources of data, that is, Twitter, Flickr and Google news, to output with vivid visual and textual contents on emerging topics. Enabled by a novel general-structured high-order co-clustering approach, it has a more accurate detection of emerging topics compared to the existing methods on micro-blog social streams.
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Bao et al. (2012) studied this question.
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