I Tube, You Tube, Everybody Tubes

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I Tube, You Tube, Everybody TubesPablo
RodriguezTelefonica ResearchBarcelona
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YouTube Video Example
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Content is NOT king
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Content Explosion
How to search content?
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Aggregation and Recommendation
Infinite Choice Overwhelming Confusion
Filters required to connect users with content
that appeal to their interests
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Video and Social Networks

• Trends in video services
• Users generate new videos
• Users help each other finding videos
• Need to understand users and contents
• Video characteristics in YouTube
• User-behavior and potential for recommendations

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Particularities of

• bite-size bits for high-speed munching
• Wired mag. Mar 2007
• Plethora of YouTube clones
• UGC is very different
• How different?

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UGC vs. Non-UGC

• Massive production scale
• 15 days in YouTube to produce 120-yr worth of
  movies in IMDb!
• Extreme publishers
• 1000 uploads over few years vs. 100 movies over
  50 years
• Short video length
• 30 sec5 min vs. 100 min movies in LoveFilm
• the rest consumption patterns

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User Participation/Finding Videos

• Despite Web 2.0 features, user participation
  remains low
• Only 0.16-0.22 viewers rate videos/comment.
• 47 videos have pointers from external sites
• But requests from such sites account for less
  than 3 of the total views

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Goals and Data

• Potential for recommendation systems?
• Popularity evolution
• Content Duplication
• Crawled YouTube and other UGC systems
• metadata video ID, length, views
• 1.6M Entertainment, 250KScience videos

Goals
Data
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Part1 Popularity Distribution

• Static popularity characteristics
• Underlying mechanism

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Pareto Principle

• 10 popular videos account for 80 total views

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Dominant Power-Law Behavior

• Richer-get-richer principle
• If video has K views, then users will watch the
  video with rate K

• word frequency- citations of papers - scale of
  earthquakes
• web hits

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UGC Video Distribution

• Straight-line waists and truncated both ends

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Focusing on Popular Videos

• Why popular videos deviate from power-law?
• Fetch-at-most-once SOSP2003
• Behavior of fetching immutable objects oncecf.
  visiting popular web sites many times

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Why the Unpopular Tail Falls Off

• Natural shape is curved
• Sampling bias or pre-filters
• Publishers tend to upload interesting videos
• Information filtering or post-filters
• Search results or suggestions favor popular items

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Impact of Post-Filters

• Videos exposed longer to filtering effect
  appear more truncated

video rank
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Is it Naturally Curved?

• Matlab curve fitting for Science

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Is it Naturally Curved?

• Matlab curve fitting for Science

Zipf is scale-free, while exponential is scaled
underlying mechanism is Zipf and truncation
is due to bottlenecks
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Implication of Our Findings

• Latent demand for products that is suppressed by
  bottlenecks in the system
• Chris Anderson, The Long Tail

40 additional views! How?
Personalized
recommendation Enriched metadataAbundant videos
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Part2 Popularity Evolution

• Relationship between popularity and age

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Popularity Evolution

• So far, we focused on static popularity
• Now focus on popularity dynamics
• How requests on any given day are distributed
  across the video age?
• 6-day daily trace of Science videos
• Step1- Group videos requested at least once by
  age
• Step2- Count request volume per age group

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Request Volume Across Age
User preference relatively insensitive to age
--gt 80 requests on videos older than a month
The probability of a video being watched is 43,
18, 17 and 14 for the first 24 hours, 6 days,
3 weeks, and 1 month accordingly
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Part4 Content Duplication

• Level of duplication
• Birth of duplicates

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Content Duplication

• Alias- identical or similar copies of the same
  content
• Aliases dilute popularity of a single event
• Views distributed across multiple copies
• Difficulty in recommendation ranking systems
• Test with 51 volunteers
• Find alias using keyword search
• Identified 1,224 aliases for 184 original videos

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The Level of Popularity Dilution

• Popularity diluted up to few-orders magnitude

• Often aliases got more requests than original
• (e.g. alias got gt1000 times more requests)

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How Late Aliases Appear?

• Significant aliases appear within one week

• Within the first day of posting the original
  video, sometimes you get more than 80 aliases

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Conclusions

• UGC is a new form of video social interaction
• User interaction remains low
• Lots of potential for social recommendations

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Questions?

• Dataset available at http//an.kaist.ac.kr/trace
  s/IMC2007.html