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Who Will Share My Image? Predicting the Content Diffusion Path in Online Social Networks

Title
Who Will Share My Image? Predicting the Content Diffusion Path in Online Social Networks
Author
한진영
Issue Date
2018-02
Publisher
ACM
Citation
WSDM '18 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, Page. 252-260
Abstract
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.
URI
https://dl.acm.org/citation.cfm?id=3159705http://repository.hanyang.ac.kr/handle/20.500.11754/105832
ISBN
978-1-4503-5581-0
DOI
10.1145/3159652.3159705
Appears in Collections:
COLLEGE OF COMPUTING[E] > MEDIA, CULTURE, AND DESIGN TECHNOLOGY(ICT융합학부) > Articles
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