Lifecycle of a video
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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

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LifeCycle of a video

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Lifecycle of a video

LifeCycle of a video

Tracking the Popularity of online videos

Samantha Anderson


Popularity factors

  • Number of Likes / Dislikes

  • Number of Videos

  • Average View Count

  • Popular tags

  • User subscriptions

Popularity Factors


What is the most important factor

What is the most important factor?


Experiment

  • 3 topics

    • Card Tricks

    • Sledding

    • Minecraft Mods

  • Collected view counts from video birth. (WebNumbr.com)

  • Collected popularity factor data

  • Compared Videos

  • Examined outliers

Experiment


Set backs

  • View count freezes

  • Inconsistent view counts

  • Accidental suggested video manipulation

Set Backs


High view counts

  • Commonalities

    • Many tags

    • High percentage of likes

    • Many subscribers

  • Leveled off at a fraction of their subscribers close to average

High View Counts


Best fit logarithm

Best Fit: Logarithm

View Count


Middle view counts

  • Commonalities

    • Many tags

    • High percentage of likes

    • Some subscribers

  • Leveled off at a fraction of their subscribers not close to average

Middle View Counts


Best fit power

Best Fit: Power

View Count


Middle vs high

  • Very similar popularity factor data

  • Less subscribers

  • Haven’t made a name for themselves yet.

  • Less popular topic

Middle vs High


Low view counts

  • Commonalities

    • Less tags

    • Few likes/ dislikes

    • Few subscribers

  • Low average view counts

Low View Counts


Best fit linear

Best Fit: Linear

View Count

Time(hours)


Why so low

  • Low averages

  • Over shadowed by other popular videos

Why so low?


Spurty view counts

  • Strange jumps in data

  • Very little in common with each other

  • Outliers

Spurty View Counts


Best fit none

Best Fit: None

View Count

Time(hours)


Why so jumpy

  • Different for each video

    • Some subscribers

    • High average view count

    • Recommended video

    • False tagging

Why so jumpy?


Summary

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


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