This is the Source code of the paper 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. DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity. International Conference on Database and Expert Systems Applications (DEXA), 2018.
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 wDTW-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.