Can Couch Potatoes be Collaborators?

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Keynote at CollaborateCom 2010. Invited talk at OSU-CETI

Text of Can Couch Potatoes be Collaborators?

  • 1. Can Couch Potatoes be Collaborators? Can Couch Potatoes be Collaborators? Venu Vasudevan, PhD| Senior Director | Betaworks| Motorola Mobility Adjunct Faculty | ECE Department | Rice University

2. Collaboration : about intersections in interactions Community Who Content On What Context Why Computation How FacetedCollaboration .. collaborativecomputingand collaborativemediaare just facets 3. Collaborative Computing =Orchestratedinteractions Community Who Content On What Context Why Computation How Drive Efficiency Machines assist People to increase Productivity 4. Collaborative Media =Orchestratedinteractions Community Who Content On What Context Why Computation How Facilitate Emergence People assist Machines to increase Knowledge 5. Collaborative Media driven byParticipatoryusers Image used under Creative Commons Licensing, http://www.flickr.com/photos/briansolis/2735401175/ Social Networking = Humancommunications Social Media = Human (generated)content Social Search = Humancomputation 6. X-shiftingexpands Collaboration opportunities http://www.flaii.com/userguide/newimages/web-20-1p2.jpg Commercial vs. User-generated content Real-time vs. Time-shifted consumption Home vs. Mobile Interstitial vs. Intentional 7. Collaborative MediaSocial Web people-driven + consumer + organic Collaborative Computing Information Web machine-driven + enterprise + orchestrated volume attention expertise 8. What does a collaborator look like Depth of engagement Diversity of participation 9. Rest of this talk Can TV be a collaborative media platform Can couch potatoes be collaborators? 10. Reducing collaboration to an Activity Format actor verbtarget context who did what (intent| relevance| engagement) to what under whatcircumstance 11. Elements of successful web collaborative media

  • Lots of interesting & accessible socialtargets
  • Enable interestingverbs(annotate | tag | chat)
  • Frictionless interaction . Make it dead easy to enact these verbs (e.g. tag|like)
    • Attentionis a finite resource
  • Layer .
    • Expose higher level .. That arise from verb patterns
      • Tag similarity|affinity
      • Expertise
      • Object popularity
    • Create higher level verbs around higher level patterns (e.g. ask)
  • Gamify . Makeparticipationaddictive (urgency | competition | symmetry | persistence)

tag annotate chat ask 12. COLLABORATIVE MEDIA ON TV :THREE STORY LINES

  • Realizable?(engineering | usability)
  • Relevant? (business models | adoption)

Youre sharing similar thoughtsand youre sharing them live while they happen ..so youre enhancing the experience Before going into this I thought, What would I ever use this for?But it was a totally different experience actually doing it Im alone a lot.There are times when it would be nice to have someone to talk to. 13. If collaboration is the answer, what is the question?

  • Converging to IP | web | digital
  • Exploding content. Fragmenting user attention
  • New Wave Media. Old school user search habits
  • TV has a knowledge discovery problem.
    • forcontent
    • withincontent
    • duringcontent
  • Wherecollaborative| social is a desirable approach

TV STUDIOS NEW AGGREGATORS USER GENERATED Public & Private Networks iTunes 14. What is TV : Ask the people ..

  • It just works.
  • It turns on instantly.
  • Others will have watched the same thing I watch.
  • Its episodic; structured.
  • Its not demanding.

05/16/11 2010 Motorola Mobility, Inc. - Internal Confidential 15. content discovery conundrums Hot shows Peak Moments Inside Stuff 16. content discovery conundrumsvarying social solutions Hot shows social as filter Peak Moments social as sensor Inside Stuff social as router 17. Web & TV similarity

  • Content
  • Conversations around content (e.g. forums)
  • Social Network
  • Rights Management
  • Monetization (Ads)
  • User Interaction technologies (speech | gesture)
  • User Attention

18. Different engineering hurdles .. Feature Web Ecosystem TV Ecosystem Content Reference Webpage URL Program ID + Offset + Duration InteractionDevice Resource-rich (PC + browser) Resource-limited (TV + remote) User Identity IndividualLogin Household vs. Individual Application Experience Lean Forward Lean Back Content Access & Consumption Model Free User-controlled Paid Provider-controlled 19. TVL.ICIO.US : SOCIAL BOOKMARKING FOR TV

  • Social as Filter

20. Social Bookmarking as a value proposition Widely adoptedfor web content

  • Proven valueto discovery on web
  • Improved quality, click-ability of search results for content discovery
  • Models user interests and social ties or influences for audience discovery

Promising valueto TV-centric needs in engagement, discovery 21. Social Bookmarking for TV The Concept Social Bookmark=CreatorIdentity + ContentReference+ DescriptiveTags TV viewers TV Clips 22. User Benefit : Shared clips as social currency Bob likes a Bill Maher joke. He clips that segment and saves it using his TV bookmarking service using tags likefunnyandBill Maher Alice (Bobs friend) sees the bookmark and is intrigued. She clicks it to request and view the clip directly on her TV. User Identification Reference Generation Tag Annotation Bookmark Retrieval Bookmark Consumption 23. TV vs Web. different medium | different challenge Feature TV Ecosystem Challenges Bookmark Creation TV or video clips (dynamic) Retrospective bookmarking is hard Content Reference Program ID + Offset + Duration Affinity analysisfor discovery InteractionDevice Resource-limited (TV + remote) Cumbersomeinputfor annotation User Identity Household vs. Individual Handlingconcurrency Creation Experience Lean Back Attention vs. Interruption Consumption Model Paid Provider-controlled Utility vs. Legality around distribution 24. Provider Need: understand & acquire customers effectively Producing commercial content iscostly finer granularity of analytics (clip vs. item) can help Recoup costs with targeted advertising clip annotations = viewerintent Reduce costs by de-risking decisions clip activity = viewerinterest 25. TWEET TV : TWITTER BASED HEAT SEEKINGFOR TV

  • Social as Sensor

26. Sports TV : the channel flippers dilemma Keeping up with a number of simultaneous games for knowledge | social capital Ebbs & flows make interest level fluctuate rapidly in real-time Digital Herd Mentality. Being where the action is Media monitoring. Cognitive effort | latency 27. Twitter sensor for thereal-world : fast AND good

  • For location-based queries 43% chance that a Twitter result will be the #1 ranked
  • General queries23% chance that a Twitter result is #1
  • Newest Twitter results ~4 seconds old. The newest Web results are 10x older (41 seconds).
  • A top ranking Twitter result for a location-based query2 minutes old (vs Web which is 22 minutes old)
  • When Twitter results appear at least one of them is in the top ranked position

28. Sports world is at least as chirpy #superbowl 4000 tps #worldcup 3300 tps 29. The trend hasnt gone unnoticed .. 30. social navigation: e*pg

  • Can we detect in-game events quickly & reliably by analyzing public twitter streams?
      • game event detection | user sentiment extraction | Loc | device
  • Can we overlaythis sentiment on traditional EPG grids to createcredible experience
  • Can this be done without miring end-user in unnatural amounts of profile filling

sport team player mojo events 31. social navigation: e*pg

  • Can we detect in-game events quickly & reliably by analyzing public twitter streams?
    • fairly accurate for football and soccer
    • Sensing latency better than web 2.0 sources (30 seconds vs 2 minutes)
  • Calibrating the Twitter sensor ..
    • Sensitivity | latency | response to different game cadences | singularity detection ?

32. TV ANSWERS: Q&A FOR TV

  • Social as Router

33. Web search : from automated to human Credits. Aardvark@slideshare 34. Weak links : stronger than strongCredits. Aardvark@slideshare 35. TV & Social Search

  • Focus onuser-generated queries around viewed content
  • Real-world examples(from Yahoo! Answers TV category)
  • Benefits of explicit search
    • Finer granularity in defining focus user interest (about X)
    • Clearer idea of user intent (what is Xwhere can I buy X )

36. So, wheres the problem?

  • Occur out-of-band today(online, in CQA communities)
    • Requires a second device (PC) for querying while watching TV
    • Imposes cognitive (recall) and descriptive (entry) burde