Curing Discontent in
Online Content
Acquisition
Nishanth Sastry
King’s College London
http://www.watfordobserver.co.uk/nostalgia/memories/10099510.Coronation_treat_as_community_gathers_around_the_only_TV
/
Early use of mass media
Picture from the TV broadcast of the Coronation of Elizabeth II in 1953, Watford
Today’s “TV” viewing
With Digital Media Convergence, TV is just another video app, accessed on-demand on the Web
What changed: Push Pull
 Superficially: audience to TV set ratio has decreased
 At a fundamental level:
 audience per “broadcast” is lower
 “Broadcast” time is chosen by the consumer
 Traditional mass media pushed content to consumer
 Current dominant model has changed to pull
Generalizes to other mass media as well
Implications of the pull model
 Traditionally, “editors” decided what content got pushed when
 Linear TV schedulers use complex analytics to decide “primetime”
 Users get more choice with the pull model
 When to consume
 What to consume (from large catalogue)
 Unpopular/niche interest content also gets a distribution channel,
not just what editors decide to showcase/bless as “publishable”
 Cheaper to stream over the Web to a single user than to broadcast
(e.g. to operate/maintain equipment like high power TV transmitters)
 BUT: Cost of broadcast can be amortized across millions of consumers
 Could be cheaper per user to broadcast than to stream
Research questions
 How does pull model impact delivery infrastructure?
 Can additional load of on-demand pulls be reduced by
reusing scheduled pushes?
WWW’13
 How do users make use of flexibility afforded to them?
 Were/are editors good at predicting popularity?
 Is niche interest/unpopular content important to users?
 How do users find unpopular content they like?
ICWSM’12
 Users help each other!
 Understanding how and why users share their loves
 Designing infrastructure to help users find most influential
users for their topics of interest
ICWSM’13
ASE/IEEE Social
Informatics’12
*Certain
data can be made available upon request
Data to answer the questions*
 Nearly 6 million users of BBC iPlayer across the UK
 32.6 million streams, >37K distinct content items
 25% sample of BBC iPlayer access over 2 months
WWW’13
 Five years of vimeo data (Feb’05 – Mar’10)
 Goes back to within 3 months of founding date
 443K videos, 2.5 million likes, 200K users, 700K links
 All content curation activity, Jan’13Pinterest (8.5
million users), Dec’12last.fm (nearly 300K users)
 All tweets leading up to London Olympics (1.2
million), Closing Ceremony (~0.5 million), London
Fashion Week (168K tweets)
ICWSM’12
ICWSM’13
ASE/IEEE Social
Informatics’12
Understanding and
decreasing the network
footprint of Catch-up TV
 How does pull model impact delivery infrastructure?
 Can additional load of on-demand pulls be reduced
by reusing scheduled pushes?
 How do users make use of flexibility afforded to them?
 Were/are editors good at predicting popularity?
WWW’13
Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13
• BBC proposes, consumer disposes!
• Serials:~50% of content corpus; 80% of watched content!
What users prefer to watch-I
Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13
What users prefer to watch-II
Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13
What users prefer to watch-III
Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13
On-demand spreads load over time
Linear TV schedulers seem to do a
good job of predicting popularity!
Impact of pull on
infrastructure
Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13
• BUT: iPlayer traffic is close to 6% of UK peak traffic
• Second only to YouTube in traffic footprint
• Compare to adult video, a traditional heavy hitter. Most popular
adult video streaming sites have <0.2% traffic share
• BUT: amortized per-user, broadcast greener than streaming*
(using Baliga et al.’s energy model for the Internet)
*All
channels except BBC Parliament, which has few viewers
On-demand more suited
to web/pull than linear TV
Still, can we decrease its footprint, please?
Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13
• DVRs have >50% penetration in US, UK
• Many (e.g. YouView) don’t need cable
• Could also use TV tuner and record on laptop
Yes, we can!
But, people don’t remember to record always
Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13
Speculative Content Offloading and
Recording Engine
Can we help users record
what they want to watch?
Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13
• Predict using user affinity for
• Episodes of same programme
• Favourite genres
• We can optimise for decreasing traffic or carbon footprint
• Decreasing carbon decreases traffic, but not vice versa
• Turns out we only take 5-15% hit by focusing on carbon
SCORE=predictor+optimiser
Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13
Oracle saves:
• Up to 97% of traffic
• Up to 74% of energy
• SCORE saves ~40-60% of savings achieved by oracle
• Green optimisation saves 40% more energy at expense of 5% more traffic
Performance evaluation
Compare SCORE relative to Oracle knowing future requests
Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13
• Indiscriminately recording top n shows can lead to
negative energy savings!
• Personalised approach necessary, despite popularity of
“prime time” content
Not all of these savings come from
predicting popular content
How To Tell Head From Tail in
User-generated Content
Corpora
AAAI
ICWSM’12
 Is niche interest/unpopular content important to users?
 How do users find unpopular content they like?
 Users help each other!
ICWSM’12
How to tell head from tail in User-generated Content Corpora- AAAI ICWSM’12
The tail is heavy in users,
not accesses
How to tell head from tail in User-generated Content Corpora- AAAI ICWSM’12
Like sets of many users are
dense in tail items
How to tell head from tail in User-generated Content Corpora- AAAI ICWSM’12
Likers of tail content are
geographically more diverse
Niche interest content rather than merely unpopular?
How to tell head from tail in User-generated Content Corpora- AAAI ICWSM’12
How do users find tail items?
Non-viral access predominates in popular items
Sharing the Loves:
Understanding the how and
why of online content curation
AAAI
ICWSM’13
Is niche interest/unpopular content important to users?
How do users find unpopular content they like?
Users help each other!
 Understanding how and why users share their loves
ICWSM’13
Sharing the Loves:
Understanding the how and
why of online content curation
 Data reminder:
 All (38 million) Repins, (~20 million) Likes on Pinterest Jan 13
 All (90 million) Loves, (~60 million) Tags on last.fm Dec 12
 Survey respondents: 30 for Pinterest, 270 for last.fm
AAAI
ICWSM’13
Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM’13
Why people curate content
Curation comes up when search stops working – Clay Shirky
Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM’13
• Pinterest: (30 respondents, allow multiple answers)
• 85% use it as a personal collection or scrapbook
• 48% uses the site to display their content to others
• Last.fm: (279 respondents, allow multiple answers)
• 39% tags tracks for personal classification
• 39% tags to create a global classification (genres).
• The majority of respondents shared this view (last.fm):
• “I find the social aspect more useful and interesting with
people I know, rather than developing new interactions
based on music taste. ”
• BUT: one couple met on last.fm, started going to gigs
together and are now happily married!!
Curation: of personal or social value?
Users mostly see it as personal effort, with exceptions
Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM’13
Despite unsynchronised personal effort,
community synchronises on some topics!
Strong popularity skew, as in previous highlighting methods
Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM’13
• Unstructured curation: Actions that simply highlight an item
• e.g., love, like, ban, comment, shout
• Structured Curation: Actions that also organise item onto
user-specific lists
• e.g., pinning an item onto a user’s board,
• attaching a user’s tag to a track
• Characteristics of effective curators: consistency, diversity…
Understanding how effective
content curation happens
Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM’13
Structured curation preferred
for popularly curated items
Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM’13
The most important part of a curator’s job is to
continually identify new content for their audience
-- Rohit Bhargava
How to curate: Consistent and
regular updates attracts followers
Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM’13
How to curate: Diversity of
interests attracts followers
IARank: Ranking Users on
Twitter in Near Real-time,
Based on their Information
Amplification Potential
 Effective content curation is a highly demanding task
 Consumers still need to find the best “editors” they want
 Naturally self-limiting when it comes to high-volume events
 Olympics closing ceremony: 400K tweets in just over 3 hours
 We can rank the most influential users e.g., PageRank
 PageRank takes time to converge  Ranks can change before!
 IARank:ranks users by Information Amplification potential
 “Buzz” factor: how likely to be retweeted
 “structural advantage”: how good is your immediate neighbourhood
 Understanding how and why users share their loves
ASE/IEEE Social
Informatics’12
Summary
 Characterising on-demand content consumption via 6
million users of BBC iPlayer
 If broadcast is efficient, we should find ways to use it!
WWW’13
 SCORE: personalised content offloading engine
 Is niche interest/unpopular content important to users?
 How do users find unpopular content they like?
ICWSM’12
 Users help each other!
 Social curation complements search; effective curators
are consistent and have diverse interests
 Near-instantaneous reranking scheme for high volume
content sharing systems like Twitter
ICWSM’13
ASE/IEEE Social
Informatics’12
Curing
Discontent in
Online Content
Acquisition
Nishanth Sastry
King’s College London
http://www.inf.kcl.ac.uk/staff/nrs