  The evolution of social media popularity exhibits rich temporality, i.e.,
popularities change over time at various levels of temporal granularity. This
is influenced by temporal variations of public attentions or user activities.
For example, popularity patterns of street snap on Flickr are observed to
depict distinctive fashion styles at specific time scales, such as season-based
periodic fluctuations for Trench Coat or one-off peak in days for Evening
Dress. However, this fact is often overlooked by existing research of
popularity modeling. We present the first study to incorporate multiple
time-scale dynamics into predicting online popularity. We propose a novel
computational framework in the paper, named Multi-scale Temporalization, for
estimating popularity based on multi-scale decomposition and structural
reconstruction in a tensor space of user, post, and time by joint low-rank
constraints. By considering the noise caused by context inconsistency, we
design a data rearrangement step based on context aggregation as preprocessing
to enhance contextual relevance of neighboring data in the tensor space. As a
result, our approach can leverage multiple levels of temporal characteristics
and reduce the noise of data decomposition to improve modeling effectiveness.
We evaluate our approach on two large-scale Flickr image datasets with over 1.8
million photos in total, for the task of popularity prediction. The results
show that our approach significantly outperforms state-of-the-art popularity
prediction techniques, with a relative improvement of 10.9%-47.5% in terms of
prediction accuracy.
