Learning sequential features for cascade outbreak prediction

• Published in: Knowledge and information systems Vol. 57; no. 3; pp. 721 - 739
• Main Authors: Gou, Chengcheng, Shen, Huawei, Du, Pan, Wu, Dayong, Liu, Yue, Cheng, Xueqi
• Format: Journal Article
• Language: English
• Published: London Springer London 01.12.2018; Springer Nature B.V
• Subjects: Cascades; Computer Science; Data Mining and Knowledge Discovery; Database Management; Information Storage and Retrieval; Information systems; Information Systems and Communication Service; Information Systems Applications (incl.Internet); IT in Business; Neural networks; Outbreaks; Predictions; Recurrent neural networks; Short Paper; Social networks; Social network; Sequential feature; LSTM; Popularity prediction; Outbreak prediction
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-017-1143-0

Information cascades are ubiquitous in various online social networks. Outbreak of cascades could cause huge and unexpected effects. Therefore, predicting the outbreak of cascades at early stage is of vital importance to avoid potential bad effects and take relevant actions. Existing methods either adopt regression or classification technique with exhaustive feature engineering or predict cascade dynamics via modeling the stochastic process of cascades using a hard-coded diffusion–reaction function. One salient issue of these methods is that these methods heavily depend on human-defined knowledge, features or functions. In this paper, we propose to use recurrent neural network with long short-term memory to directly learn sequential patterns from information cascades, working in a fully data-driven manner. With the learned sequential patterns, the outbreak of cascade could be accurately predicted. Extensive experiments on both Twitter and Sina Weibo datasets demonstrate that our method significantly outperforms state-of-the-art methods at the prediction of cascade outbreaks.