DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity
This addresses the challenge of cross-platform event analysis for researchers and analysts, though it is incremental as it builds on existing methods like Word2Vec and Dynamic Time Warping.
The paper tackles the problem of analyzing event popularity across multiple online platforms by introducing DancingLines, a scheme that quantifies and aligns event popularity time series between pairwise media, achieving validated effectiveness on 18 real-world event datasets.
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. Challenges come from the vast diversity of events and media, limited access to aligned datasets across different media and a great deal of noise in the datasets. 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. We also propose three metrics to interpret event popularity trends between pairwise social platforms. Experimental results on eighteen real-world event 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 the knowledge of various aspects related to events and different media.