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Measuring the Influence from User-Generated Content to News via Cross-dependence Topic Modeling

  • Lei HouEmail author
  • Juanzi Li
  • Xiao-Li Li
  • Yu Su
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9049)

Abstract

Online news has become increasingly prevalent as it helps the public access timely information conveniently. Meanwhile, the rapid proliferation of Web 2.0 applications has enabled the public to freely express opinions and comments over news (user-generatxlli@i2r.a-star.edu.sged content, or UGC for short), making the current Web a highly interactive platform. Generally, a particular event often brings forth two correlated streams from news agencies and the public, and previous work mainly focuses on the topic evolution in single or multiple streams. Studying the inter-stream influence poses a new research challenge. In this paper, we study the mutual influence between news and UGC streams (especially the UGC-to-news direction) through a novel three-phase framework. In particular, we first propose a cross-dependence temporal topic model (CDTTM) for topic extraction, then employ a hybrid method to discover short and long term influence links across streams, and finally introduce four measures to quantify how the unique topics from one stream affect or influence the generation of the other stream (e.g. UGC to news). Extensive experiments are conducted on five actual news datasets from Sina, New York Times and Twitter, and the results demonstrate the effectiveness of the proposed methods. Furthermore, we observe that not only news triggers the generation of UGC, but also UGC conversely drives the news reports.

Keywords

News stream User-generated content Cross dependence Influence Response 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Institute for Infocomm Research, A*STARSingaporeSingapore
  3. 3.Communication Technology Bureau, Xinhua News AgencyBeijingChina

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