html5-img
1 / 0

LifeCycle of a video

LifeCycle of a video. Tracking the Popularity of online videos. Samantha Anderson. Number of Likes / Dislikes Number of Videos Average View Count Popular tags User subscriptions. Popularity Factors. What is the most important factor?. 3 topics Card Tricks Sledding Minecraft Mods

doris
Télécharger la présentation

LifeCycle of a video

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. LifeCycle of a video Tracking the Popularity of online videos Samantha Anderson
  2. Number of Likes / Dislikes Number of Videos Average View Count Popular tags User subscriptions Popularity Factors
  3. What is the most important factor?
  4. 3 topics Card Tricks Sledding Minecraft Mods Collected view counts from video birth. (WebNumbr.com) Collected popularity factor data Compared Videos Examined outliers Experiment
  5. View count freezes Inconsistent view counts Accidental suggested video manipulation Set Backs
  6. Commonalities Many tags High percentage of likes Many subscribers Leveled off at a fraction of their subscribers close to average High View Counts
  7. Best Fit: Logarithm View Count
  8. Commonalities Many tags High percentage of likes Some subscribers Leveled off at a fraction of their subscribers not close to average Middle View Counts
  9. Best Fit: Power View Count
  10. Very similar popularity factor data Less subscribers Haven’t made a name for themselves yet. Less popular topic Middle vs High
  11. Commonalities Less tags Few likes/ dislikes Few subscribers Low average view counts Low View Counts
  12. Best Fit: Linear View Count Time(hours)
  13. Low averages Over shadowed by other popular videos Why so low?
  14. Strange jumps in data Very little in common with each other Outliers Spurty View Counts
  15. Best Fit: None View Count Time(hours)
  16. Different for each video Some subscribers High average view count Recommended video False tagging Why so jumpy?
  17. 1. Get subscribers Offer incentives Requests in videos 2. Get one popular video 3. Use tags (careful for fraud) 4. Get it recommended 5. Don’t worry too much about likes Summary
More Related