Talk presented for the "Like at first sight" at 2017 Conference on social informatics at Oxford

1. Like At First Sight:
Understanding User
Engagement with Micro-
Videos
Sagar Joglekar, Nishanth Sastry, Miriam Redi
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2. 2

3. 3
Understand how users engage and what drives
the engagement with this new form of social
media

4. Why ?
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5. The Questions
• How do users engage with these
short videos ?
• How micro videos gain popularity ?
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6. TL;DR
User engagement in Micro Videos:
Faces engage us
First 2 seconds matter
Social influence matters, but not at the top
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7. The Data
• Vine:
• ~ 15k Popular videos for ground
truth of popularity
• ~ 120k videos across the
population
• Almost all videos crawled for 4
weeks
• Likes, Reposts, Comments, Social
interactions
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8. Experiment steps:
Extract Features
Evaluate importance of
each feature for
predicting engagement
(like, repost)
Figure out do they
correlate temporaly ?
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9. Features Frame
Sentiment*
Sound
Aesthetics
Visual
Aesthetics
Social
Effect
Engagement
9* Borth, Damian, et al. "Sentibank: large-scale ontology and classifiers for detecting sentiment and emotions in visual content." Proceedings of the 21st ACM international conference on Multimedia. ACM, 2013.

10. Variation of feature importance
f1
f2
f3
.
.
.
.
.
f27
Video
Dataset
Random Forrest
Classifier
Dichotomize
for
Engagement
metric
C11
C21
C31
.
.
.
.
.
C271
C1N
C2N
C3N
.
.
.
.
.
C27N
…
Harden
Engagement
Threshold
Feat.
Importance

11. Social doesn't matter at the top:
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Content
takes over

12. Amongst content: Faces matter
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13. Faces Engage us:
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%age of frames
with faces

14. TL;DR
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16. Repercussions
Rise in predictive advertising
Rise of Innovative creative methods
Informed UI/UX design
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17. In summary
User engagement in Micro Videos:
Faces engage us
First 2 seconds matter
Social influence matters, but not at the top
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Thank You!!

18. Appendix 1: Classifier Performance
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19. Appendix 2:
What About
Instagram?
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20. The
* https://techcrunch.com/2017/06/27/facebook-2-billion-users/
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