Curing Discontent in Online Content Acquisition Nishanth Sastry Kings College London.

Curing Discontent in Online Content AcquisitionNishanth SastryKing’s College London

Early use of mass media

http://www.watfordobserver.co.uk/nostalgia/memories/10099510.Coronation_treat_as_community_gathers_around_the_only_TV/

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 PullSuperficially: 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?

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? 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

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ASE/IEEE Social Informatics’12

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

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’13Pinterest (8.5 million users), Dec’12last.fm (nearly 300K users)

All tweets leading up to London Olympics (1.2 million), Closing Ceremony (~0.5 million), London Fashion Week (168K tweets)

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*Certain data can be made available upon request

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?

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What users prefer to watch-I

• BBC proposes, consumer disposes!• Serials:~50% of content corpus; 80% of watched content!

Understanding and decreasing the Network Footprint of Catch-up TV-WWW’13

What users prefer to watch-II


What users prefer to watch-III


Abandoned

High preference for 30 and 60 min shows

Impact of pull on infrastructure


On-demand spreads load over time

Linear TV schedulers seem to do a good job of predicting popularity!

On-demand more suited to web/pull than linear TV

• 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


Still, can we decrease its footprint, please?

Yes, we can!

• DVRs have >50% penetration in US, UK• Many (e.g. YouView) don’t need cable• Could also use TV tuner and record on laptop

But, people don’t remember to record always


Can we help users record what they want to watch?


Speculative Content Offloading and Recording Engine

SCORE=predictor+optimiser

• 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


Performance evaluation

• SCORE saves ~40-60% of savings achieved by oracle• Green optimisation saves 40% more energy at expense of 5% more traffic


Compare SCORE relative to Oracle knowing future requests

Oracle saves:• Up to 97% of traffic• Up to 74% of energy

Not all of these savings come from predicting popular content

• Indiscriminately recording top n shows can lead to negative energy savings!

• Personalised approach necessary, despite popularity of “prime time” content


How To Tell Head From Tail in User-generated Content Corpora



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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


Likers of tail content are geographically more diverse


Niche interest content rather than merely unpopular?

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



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Is niche interest/unpopular content important to users?How do users find unpopular content they like?

Users help each other!

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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

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Why people curate content

Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM’13

Curation comes up when search stops working – Clay Shirky

Last.fm

Pinterest

Curation: of personal or social value?

• 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!!


Users mostly see it as personal effort, with exceptions

Despite unsynchronised personal effort, community synchronises on some topics!


Strong popularity skew, as in previous highlighting methods

Understanding how effective content curation happens

• 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…


Structured curation preferred for popularly curated items


How to curate: Consistent and regular updates attracts followers


The most important part of a curator’s job is to continually identify new content for their audience

-- Rohit Bhargava

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



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Summary Characterising on-demand content consumption via 6

million users of BBC iPlayer

If broadcast is efficient, we should find ways to use it!

SCORE: personalised content offloading engine



Social curation complements search; effective curators are consistent and have diverse interests

Near-instantaneous reranking scheme for high volume content sharing systems like Twitter

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Curing Discontent in Online Content AcquisitionNishanth Sastry

King’s College London

http://www.inf.kcl.ac.uk/staff/nrs

Curing Discontent in Online Content Acquisition Nishanth Sastry Kings College London.

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