Paper Info
Title
FEBDNN: fusion embedding-based deep neural network for user retweeting behavior prediction on social networks
Abstract
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.
Year
DOI
Venue
2022
10.1007/s00521-022-07174-9
Neural Computing and Applications
Keywords
DocType
Volume
Retweeting prediction, Deep neural network, Convolutional neural network, Dual auto-encoder, Social network
Journal
34
Issue
ISSN
Citations 
16
0941-0643
1
PageRank 
References 
Authors
0.36
12
5
Name
Order
Citations
PageRank
Lidong Wang110.70
Yin Zhang23492281.04
Jie Yuan310.36
Keyong Hu410.70
Shihua Cao521.40