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AnchorMF: towards effective event context identification

Published: 27 October 2013 Publication History

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.

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      cover image ACM Conferences
      CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management
      October 2013
      2612 pages
      ISBN:9781450322638
      DOI:10.1145/2505515
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 27 October 2013

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      Author Tags

      1. event context identification
      2. matrix factorization
      3. twitter

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      October 27 - November 1, 2013
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