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

Nowadays, the primary media for information dissemination is shifting to online platforms. Events usually burst online through multiple modern online media. Therefore, predicting event popularity trends becomes crucial for online platforms to track public concerns and make appropriate decisions. However, little research focuses on event popularity prediction from a cross platform perspective. Challenges stem from the vast diversity of events and media, limited access to aligned datasets across different platforms, and a considerable amount 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 three parts. First, we propose TF-SW, a semantic-aware popularity quantification model based on Term Frequency with Semantic Weight. TF-SW obtains the event popularity based on Word2Vec and TextRank, and generates Event Popularity Time Series (EPTS). Then, we propose $\omega$?DTW-CD, a pairwise time series alignment model derived from Dynamic Time Wrapping (DTW) with Compound Distance (CD) for aligning the EPTS on several platforms. Finally, we aggregate two time series and propose a neural-based prediction model implementing Long Short-Term Memory (LSTM) with attention mechanism to obtain accurate event popularity predictions. Evaluation results based on large scale real-world datasets demonstrate that DancingLines can efficiently characterize, align, and predict event popularity in a cross-platform manner.