Pay Attention to Virality: Understanding Popularity of Social Media Videos with the Attention Mechanism
Adam Bielski , Tomasz Trzciński
AbstractPredicting 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.
|Publication size in sheets||0.3|
|Book||O’Conner Lisa (eds.): Proc. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPRW 2018), 2018, CPS, ISBN 978-1-5386-6100-0, 2450 p.|
|Score|| = 15.0, 08-08-2018, BookChapterMatConf|
= 15.0, 08-08-2018, BookChapterMatConf
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.