Forecasting the Future: Leveraging RNN based Feature Concatenation for Tweet Outbreak Prediction

Cascade outbreak is a common phenomenon observed across different social networking platforms. Cascade outbreak might have severe implications in different scenarios like a fake news/rumour can spread across a significant number of people, or a hate news can be propagated, which may incite violence etc. Early prediction of cascade outbreak would help in taking proper remedial action and hence is an important research direction. Most of the existing approaches predicted the popularity of social networking post either by machine learning techniques or using statistical models. Simple machine learning based approaches may miss important features while statistical models use hard-coded functions which might not be suitable in a different scenario. With the availability of huge data, recently deep learning based models have also been applied in the prediction of cascade outbreak. This study identified the limitation of existing deep learning based approaches and proposed a Recurrent Neural Network based Hybrid Model with Feature Concatenation (RNN-HMFC) approach. RNN-HMFC captures important latent features of textual aspect and retweet information respectively by LSTM and GRU and also uses a set of handcrafted features like additional tweet information and user social information for prediction of virality. We achieve 2.7% - 6.45% higher accuracy compared to the state of the art methods on different datasets.