NDLI: AnchorMF: towards effective event context identification

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AnchorMF: towards effective event context identification

Content Provider	ACM Digital Library
Author	Gu, Hansu Gartrell, Mike Zhang, Liang Lv, Qin Grunwald, Dirk
Abstract	Online social networks (OSNs) such as Twitter provide a good platform for event discussions. Recent research [26][25] as shown that event discussions in OSNs are diverse and innovative and encourage public engagement in events. Although much research has been conducted in OSNs to track and detect events, there has been limited research on detecting or understanding the event context. Event context helps to better predict users' participation in events, identify relations among events, and recommend friends who share similar event context. In this work, we have developed AnchorMF, a matrix factorization based technique that aims to identify event context by leveraging a prevalent feature in OSNs, the anchor information. Our AnchorMF work makes three key contributions: (1) a formal definition of the event context identification problem; (2) anchor selection and incorporation into the matrix factorization process for effective event context identification; and (3) demonstration of applying event context for user-event participation prediction, relevant events retrieval, and friendship recommendation. Evaluation based on 1.1 million Twitter users over a one-month data collection period shows that AnchorMF achieves a 20.0% improvement in terms of user-event participation prediction.
Starting Page	629
Ending Page	638
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505548
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Event context identification Twitter Matrix factorization
Content Type	Text
Resource Type	Article

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