Who Will Share My Image? Predicting the Content Diffusion Path in Online Social Networks
This addresses content popularity prediction for social media users and hosts by modeling diffusion paths rather than single metrics, though it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of predicting the entire diffusion path of images in online social networks, proposing Diffusion-LSTM, which combines user social and image features with memory to achieve more accurate predictions compared to baselines and can generalize to new users.
Content popularity prediction has been extensively studied due to its importance and interest for both users and hosts of social media sites like Facebook, Instagram, Twitter, and Pinterest. However, existing work mainly focuses on modeling popularity using a single metric such as the total number of likes or shares. In this work, we propose Diffusion-LSTM, a memory-based deep recurrent network that learns to recursively predict the entire diffusion path of an image through a social network. By combining user social features and image features, and encoding the diffusion path taken thus far with an explicit memory cell, our model predicts the diffusion path of an image more accurately compared to alternate baselines that either encode only image or social features, or lack memory. By mapping individual users to user prototypes, our model can generalize to new users not seen during training. Finally, we demonstrate our model's capability of generating diffusion trees, and show that the generated trees closely resemble ground-truth trees.