FEBDNN: fusion embedding-based deep neural network for user retweeting behavior prediction on social networks

Due to the fast growing amount of user generated content (UGC) on social networks, the prediction of retweeting behavior is attracting significant attention in recent years. However, the existing studies tend to ignore the influence of implicit social influence and group retweeting factor factors. Also, it is still challenging to consider all related factors into a unified framework. To solve the above disadvantages, we propose a novel deep neural network fusion embedding-based deep neural network (FEBDNN) through the perspective of user embedding and tweets embedding for the author and the user's historical tweets. Firstly, we propose dual auto-encoder (DAE) network for user embedding by integrating user's basic features, explicit and implicit social influence and group retweeting factor. Then, we utilize the attention-based F_BLSTM_CNN(A_F_BLSTM_CNN) model for historical tweets' representative embedding based on the combination of convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM). Finally, we concatenate these embedding features into a vector and design a hidden layer and a fully connected softmax layer to predict the retweeting label. The experimental results demonstrate that the FEBDNN model compares favorably performance against the state-of-the-art methods.