Cross-Platform Event Popularity Analysis via Dynamic Time Warping and Neural Prediction

Nowadays, the primary media for information dissemination is shifting to online media. Events usually burst online through multiple modern online media. Therefore, predicting event popularity trends becomes crucial for online platforms to track pubic concerns and make appropriate decisions. However, few researches focus on events popularity prediction from a cross-platform perspective. Challenges origin from vast diversity from events and media, limited access to aligned datasets across different platforms and the great deal of noise in datasets. In this paper, we solve the cross-platform event popularity prediction problem by proposing a model named DancingLines, which is mainly composed of the following three parts. First, we propose TF-SW, a semantic-aware popularity quantification model based on Term Frequency with Semantic Weight, obtaining the event popularity based on Word2Vec and TextRank and generating Event Popularity Time Series(EPTS). Then, we propose DTW-CD, a pairwise time series alignment model derived from DTW with Compound Distance, aligning the EPTS on several platforms. Finally, we aggregate two time series and propose a neural based prediction model implementing Long Short-Term Memory with attention mechanism to obtain accurate predictions. Evaluation results based on large scale real-world datasets demonstrate that DancingLines can efficiently characterize, align, and predict event popularity on cross-platform.