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DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity

  • Tianxiang Gao
  • Weiming Bao
  • Jinning Li
  • Xiaofeng Gao
  • Boyuan Kong
  • Yan Tang
  • Guihai Chen
  • Xuan Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11029)

Abstract

Nowadays, events usually burst and are propagated online through multiple modern media like social networks and search engines. There exists various research discussing the event dissemination trends on individual medium, while few studies focus on event popularity analysis from a cross-platform perspective. In this paper, we design DancingLines, an innovative scheme that captures and quantitatively analyzes event popularity between pairwise text media. It contains two models: TF-SW, a semantic-aware popularity quantification model, based on an integrated weight coefficient leveraging Word2Vec and TextRank; and \(\omega \)DTW-CD, a pairwise event popularity time series alignment model matching different event phases adapted from Dynamic Time Warping. Experimental results on eighteen real-world datasets from an influential social network and a popular search engine validate the effectiveness and applicability of our scheme. DancingLines is demonstrated to possess broad application potentials for discovering knowledge related to events and different media.

Keywords

Cross-platform analysis Data mining Time series alignment 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Tianxiang Gao
    • 1
  • Weiming Bao
    • 1
  • Jinning Li
    • 1
  • Xiaofeng Gao
    • 1
  • Boyuan Kong
    • 2
  • Yan Tang
    • 3
  • Guihai Chen
    • 1
  • Xuan Li
    • 4
  1. 1.Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.University of CaliforniaBerkeleyUSA
  3. 3.Hohai UniversityNanjingChina
  4. 4.Baidu, Inc.BeijingChina

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