Pay Attention to Virality: Understanding Popularity of Social Media Videos with the Attention Mechanism

Adam Bielski , Tomasz Trzciński


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.
Author Adam Bielski - [Warsaw University of Technology (PW)]
Adam Bielski,,
- Politechnika Warszawska
, Tomasz Trzciński (FEIT / IN)
Tomasz Trzciński,,
- The Institute of Computer Science
Publication size in sheets0.3
Book O’Conner Lisa (eds.): Proc. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPRW 2018), 2018, Institute of Electrical and Electronics Engineers, ISBN 978-1-5386-6100-0, 2450 p.
ProjectDevelopment of new algorithms in the areas of software and computer architecture, artificial intelligence and information systems and computer graphics . Project leader: Arabas Jarosław, , Phone: +48 22 234 7432, start date 01-06-2017, end date 31-10-2018, II/2017/DS/1, Completed
WEiTI Działalność statutowa
Languageen angielski
cvpr18-workshop-pay.pdf 1.53 MB
Score (nominal)200
Score sourceconferenceList
ScoreMinisterial score = 200.0, 17-06-2020, ChapterFromConference
Publication indicators WoS Citations = 0; Scopus Citations = 4
Citation count*5 (2020-07-16)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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