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Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
[article]
2017
arXiv
pre-print
Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i.e., sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a
arXiv:1712.04443v1
fatcat:uumtbych65cebfdiydp675fjii