  Predicting popularity of social media videos before they are published is a
challenging task, mainly due to the complexity of content distribution network
as well as the number of factors that play part in this process. As solving
this task provides tremendous help for media content creators, many successful
methods were proposed to solve this problem with machine learning. In this
work, we change the viewpoint and postulate that it is not only the predicted
popularity that matters, but also, maybe even more importantly, understanding
of how individual parts influence the final popularity score. To that end, we
propose to combine the Grad-CAM visualization method with a soft attention
mechanism. Our preliminary results show that this approach allows for more
intuitive interpretation of the content impact on video popularity, while
achieving competitive results in terms of prediction accuracy.
