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Detection of emerging topics are now receiving renewed interest motivated bythe rapid growth of social networks. Conventional term-frequency-basedapproaches may not be appropriate in this context, because the informationexchanged are not only texts but also images, URLs, and videos. We focus on thesocial aspects of theses networks. That is, the links between users that aregenerated dynamically intentionally or unintentionally through replies,mentions, and retweets. We propose a probability model of the mentioningbehaviour of a social network user, and propose to detect the emergence of anew topic from the anomaly measured through the model. We combine the proposedmention anomaly score with a recently proposed change-point detection techniquebased on the Sequentially Discounting Normalized Maximum Likelihood (SDNML), orwith Kleinberg's burst model. Aggregating anomaly scores from hundreds ofusers, we show that we can detect emerging topics only based on thereply/mention relationships in social network posts. We demonstrate ourtechnique in a number of real data sets we gathered from Twitter. Theexperiments show that the proposed mention-anomaly-based approaches can detectnew topics at least as early as the conventional term-frequency-based approach,and sometimes much earlier when the keyword is ill-defined.

Discovering Emerging Topics in Social Streams via Link Anomaly Detection