  The increasing popularity of social media platforms makes it important to
study user engagement, which is a crucial aspect of any marketing strategy or
business model. The over-saturation of content on social media platforms has
persuaded us to identify the important factors that affect content popularity.
This comes from the fact that only an iota of the humongous content available
online receives the attention of the target audience. Comprehensive research
has been done in the area of popularity prediction using several Machine
Learning techniques. However, we observe that there is still significant scope
for improvement in analyzing the social importance of media content. We propose
the DFW-PP framework, to learn the importance of different features that vary
over time. Further, the proposed method controls the skewness of the
distribution of the features by applying a log-log normalization. The proposed
method is experimented with a benchmark dataset, to show promising results. The
code will be made publicly available at
https://github.com/chaitnayabasava/DFW-PP.
