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

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Presentation on theme: "Curing Discontent in Online Content Acquisition Nishanth Sastry Kings College London."— Presentation transcript:

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

2 Early use of mass media / Picture from the TV broadcast of the Coronation of Elizabeth II in 1953, Watford

3 Todays TV viewing With Digital Media Convergence, TV is just another video app, accessed on-demand on the Web

4 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

5 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

6 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 WWW13 ICWSM12 ICWSM13 ASE/IEEE Social Informatics12

7 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 (Feb05 – Mar10) Goes back to within 3 months of founding date 443K videos, 2.5 million likes, 200K users, 700K links All content curation activity, Jan13 Pinterest (8.5 million users), Dec12 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) WWW13 ICWSM12 ICWSM13 ASE/IEEE Social Informatics12 * Certain data can be made available upon request

8 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? WWW13

9 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-WWW13

10 What users prefer to watch-II Understanding and decreasing the Network Footprint of Catch-up TV-WWW13

11 What users prefer to watch-III Understanding and decreasing the Network Footprint of Catch-up TV-WWW13

12 Impact of pull on infrastructure Understanding and decreasing the Network Footprint of Catch-up TV-WWW13 On-demand spreads load over time Linear TV schedulers seem to do a good job of predicting popularity!

13 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 Understanding and decreasing the Network Footprint of Catch-up TV-WWW13 Still, can we decrease its footprint, please?

14 Yes, we can! DVRs have >50% penetration in US, UK Many (e.g. YouView) dont need cable Could also use TV tuner and record on laptop DVRs have >50% penetration in US, UK Many (e.g. YouView) dont need cable Could also use TV tuner and record on laptop But, people dont remember to record always Understanding and decreasing the Network Footprint of Catch-up TV-WWW13

15 Can we help users record what they want to watch? Understanding and decreasing the Network Footprint of Catch-up TV-WWW13 Speculative Content Offloading and Recording Engine

16 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 Understanding and decreasing the Network Footprint of Catch-up TV-WWW13

17 Performance evaluation SCORE saves ~40-60% of savings achieved by oracle Green optimisation saves 40% more energy at expense of 5% more traffic Understanding and decreasing the Network Footprint of Catch-up TV-WWW13 Compare SCORE relative to Oracle knowing future requests Oracle saves: Up to 97% of traffic Up to 74% of energy Oracle saves: Up to 97% of traffic Up to 74% of energy

18 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 Understanding and decreasing the Network Footprint of Catch-up TV-WWW13

19 How To Tell Head From Tail in User-generated Content Corpora Is niche interest/unpopular content important to users? How do users find unpopular content they like ? Users help each other! ICWSM12 AAAI ICWSM12

20 The tail is heavy in users, not accesses How to tell head from tail in User-generated Content Corpora- AAAI ICWSM12

21 Like sets of many users are dense in tail items How to tell head from tail in User-generated Content Corpora- AAAI ICWSM12

22 Likers of tail content are geographically more diverse How to tell head from tail in User-generated Content Corpora- AAAI ICWSM12 Niche interest content rather than merely unpopular?

23 How do users find tail items? How to tell head from tail in User-generated Content Corpora- AAAI ICWSM12 Non-viral access predominates in popular items

24 Sharing the Loves: Understanding the how and why of online content curation ICWSM13 Is niche interest/unpopular content important to users? How do users find unpopular content they like ? Users help each other! AAAI ICWSM13

25 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 ICWSM13

26 Why people curate content Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM13 Curation comes up when search stops working – Clay Shirky

27 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!! Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM13 Users mostly see it as personal effort, with exceptions

28 Despite unsynchronised personal effort, community synchronises on some topics! Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM13 Strong popularity skew, as in previous highlighting methods

29 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 users board, attaching a users tag to a track Characteristics of effective curators: consistency, diversity… Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM13

30 Structured curation preferred for popularly curated items Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM13

31 How to curate: Consistent and regular updates attracts followers Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM13 The most important part of a curators job is to continually identify new content for their audience -- Rohit Bhargava

32 How to curate: Diversity of interests attracts followers Sharing the loves: Understanding the how and why of online content curation- AAAI ICWSM13

33 IARank: Ranking Users on Twitter in Near Real-time, Based on their Information Amplification Potential ASE/IEEE Social Informatics12

34 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 Is niche interest/unpopular content important to users? How do users find unpopular content they like? 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 WWW13 ICWSM12 ICWSM13 ASE/IEEE Social Informatics12

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


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