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Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
ISBN: 978-1-4673-5164-5
pp: 882-885
ABSTRACT
Users of today's information networks need to digest large amounts of data. Therefore, tools that ease the task of filtering the relevant content are becoming necessary. One way to achieve this is to identify the users who generate content in a certain topic of interest. However, due to the diversity and ambiguity of the shared information, assigning users to topics in an automatic fashion is challenging. In this demo, we present Topick, a system that leverages state of the art techniques and tools to automatically distill high-level topics for a given user. Topick exploits both the user stream and her profile information to accurately identify the most relevant topics. The results are synthesised as a set of stars associated to each topic, designed to give an intuition about the topics encompassed in the user streams and the confidence in the results. Our prototype achieves a precision of 70% or more, with a recall of 60%, relative to manual labeling. Topick is available at http://topick.alexandra.olteanu.eu.
INDEX TERMS
Twitter, Biological system modeling, Labeling, Data models, Real-time systems, Computational modeling, Prototypes, Twitter, Information networks, User classification, Topic models, Profile data
CITATION
Anton Dimitrov, Alexandra Olteanu, Luke Mcdowell, Karl Aberer, "Topick: Accurate Topic Distillation for User Streams", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 882-885, doi:10.1109/ICDMW.2012.47
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