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

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Database and Expert Systems Applications (DEXA 2018)

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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.

This work has been supported in part by the Program of International S&T Cooperation (2016YFE0100300), the China 973 project (2014CB340303), the National Natural Science Foundation of China (Grant number 61472252, 61672353), the Shanghai Science and Technology Fund (Grant number 17510740200), CCF-Tencent Open Research Fund (RAGR20170114), and Key Technologies R&D Program of China (2017YFC0405805-04).

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Gao, T. et al. (2018). DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11029. Springer, Cham.

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