An attention-based neural popularity prediction model for social media events

Online interaction behavior between web users often makes some events go viral. Popularity prediction of events is a key task in many security related applications. It forecasts how widely events would spread based on the information of evolution at an early stage. Existing methods either rely on careful feature engineering, or solely consider time series, ignoring rich information of user and text content. In this paper, we attempt to extract and fuse the rich information of text content, user and time series in a data-driven fashion. To this end, we design a popularity prediction model based on deep neural networks, which uses three encoders to extract high-level representation of text content, users and time series respectively. In addition, we incorporate attention mechanism to make our model focus on important features. Experiments on real world dataset show the effectiveness of our proposed model.