A Novel Embedding Method for News Diffusion Prediction
Abstract
News diffusion prediction aims to predict a sequence of news sites which will quote a particular piece of news. Most of previous propagation models make efforts to estimate propagation probabilities along observed links and ignore the characteristics of news diffusion processes, and they fail to capture the implicit relationships between news sites. In this paper, we propose an algorithm to model the news diffusion processes in a continuous space and take the attributes of news into account. Experiments performed on a real-world news dataset show that our model can take advantage of news’ attributes and predict news diffusion accurately.
Cite
Text
Liu et al. "A Novel Embedding Method for News Diffusion Prediction." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12161Markdown
[Liu et al. "A Novel Embedding Method for News Diffusion Prediction." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/liu2018aaai-novel/) doi:10.1609/AAAI.V32I1.12161BibTeX
@inproceedings{liu2018aaai-novel,
title = {{A Novel Embedding Method for News Diffusion Prediction}},
author = {Liu, Ruoran and Li, Qiudan and Wang, Can and Wang, Lei and Zeng, Daniel Dajun},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2018},
pages = {8111-8112},
doi = {10.1609/AAAI.V32I1.12161},
url = {https://mlanthology.org/aaai/2018/liu2018aaai-novel/}
}