Can Couch Potatoes be Collaborators?

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Keynote at CollaborateCom 2010. Invited talk at OSU-CETI

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Can Couch Potatoes be Collaborators?

  1. 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. 2. Collaboration : about intersections in interactions Community “ Who” Content “ On What” Context “ Why” Computation “ How” Faceted Collaboration .. collaborative computing and collaborative media are just facets
  3. 3. Collaborative Computing = Orchestrated interactions Community “ Who” Content “ On What” Context “ Why” Computation “ How” Drive Efficiency Machines assist People to increase Productivity
  4. 4. Collaborative Media = Orchestrated interactions Community “ Who” Content “ On What” Context “ Why” Computation “ How” Facilitate Emergence People assist Machines to increase Knowledge
  5. 5. Collaborative Media driven by Participatory users Image used under Creative Commons Licensing, http://www.flickr.com/photos/briansolis/2735401175/ Social Networking = Human communications Social Media = Human (generated) content Social Search = Human computation
  6. 6. X-shifting expands 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. 7. Collaborative Media  Social Web people-driven + consumer + organic Collaborative Computing  Information Web machine-driven + enterprise + orchestrated volume attention expertise
  8. 8. What does a ‘collaborator’ look like Depth of ‘engagement’ Diversity of participation
  9. 9. Rest of this talk Can TV be a collaborative media platform Can couch potatoes be collaborators?
  10. 10. Reducing collaboration to an Activity Format actor verb target context who did what (intent| relevance| engagement) to what under what circumstance
  11. 11. Elements of successful web collaborative media <ul><li>Lots of interesting & accessible social targets </li></ul><ul><li>Enable interesting verbs (annotate | tag | chat) </li></ul><ul><li>Frictionless interaction . Make it dead easy to ‘enact’ these verbs (e.g. tag|like) </li></ul><ul><ul><li>Attention is a finite resource </li></ul></ul><ul><li>Layer . </li></ul><ul><ul><li>Expose higher level .. That arise from verb patterns </li></ul></ul><ul><ul><ul><li>Tag similarity|affinity </li></ul></ul></ul><ul><ul><ul><li>Expertise </li></ul></ul></ul><ul><ul><ul><li>Object popularity </li></ul></ul></ul><ul><ul><li>Create higher level verbs around higher level patterns (e.g. ask) </li></ul></ul><ul><li>Gamify . Make participation addictive (urgency | competition | symmetry | persistence) </li></ul>tag annotate chat ask
  12. 12. COLLABORATIVE MEDIA ON TV : THREE STORY LINES <ul><li>Realizable? (engineering | usability) </li></ul><ul><li>Relevant? (business models | adoption) </li></ul>You’re sharing similar thoughts and you’re sharing them live while they happen .. so you’re 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 I’m alone a lot. There are times when it would be nice to have someone to talk to.”
  13. 13. If collaboration is the answer, what is the question? <ul><li>Converging to IP | web | digital </li></ul><ul><li>Exploding content. Fragmenting user attention </li></ul><ul><li>New Wave Media. Old school user search habits </li></ul><ul><li> TV has a knowledge discovery problem. </li></ul><ul><ul><li>for content </li></ul></ul><ul><ul><li>within content </li></ul></ul><ul><ul><li>during content </li></ul></ul><ul><li>Where collaborative | social is a desirable approach </li></ul>TV STUDIOS NEW AGGREGATORS USER GENERATED Public & Private Networks iTunes
  14. 14. What is TV : Ask the people .. <ul><li>It just works. </li></ul><ul><li>It turns on instantly. </li></ul><ul><li>Others will have watched the same thing I watch. </li></ul><ul><li>It’s episodic; structured. </li></ul><ul><li>It’s not demanding. </li></ul>05/16/11 © 2010 Motorola Mobility, Inc. - Internal Confidential
  15. 15. content discovery conundrums Hot shows Peak Moments Inside Stuff
  16. 16. content discovery conundrums  varying social solutions Hot shows  social as filter Peak Moments  social as sensor Inside Stuff  social as router
  17. 17. Web & TV similarity <ul><li>Content </li></ul><ul><li>Conversations around content (e.g. forums) </li></ul><ul><li>Social Network </li></ul><ul><li>Rights Management </li></ul><ul><li>Monetization (Ads) </li></ul><ul><li>User Interaction technologies (speech | gesture) </li></ul><ul><li>User Attention </li></ul>
  18. 18. Different engineering hurdles .. Feature Web Ecosystem TV Ecosystem Content Reference Webpage URL Program ID + Offset + Duration Interaction Device Resource-rich (PC + browser) Resource-limited (TV + remote) User Identity Individual Login Household vs. Individual Application Experience Lean Forward Lean Back Content Access & Consumption Model Free User-controlled Paid Provider-controlled
  19. 19. TVL.ICIO.US : SOCIAL BOOKMARKING FOR TV <ul><li>Social as Filter </li></ul>
  20. 20. Social Bookmarking as a value proposition Widely adopted for web content <ul><li>Proven value to discovery on web </li></ul><ul><li>Improved quality, click-ability of search results for content discovery </li></ul><ul><li>Models user interests and social ties or influences for audience discovery </li></ul>Promising value to TV-centric needs in engagement, discovery
  21. 21. Social Bookmarking for TV – The Concept Social Bookmark = Creator Identity + Content Reference + Descriptive Tags TV viewers TV Clips
  22. 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 like ‘funny’ and ‘Bill Maher’ Alice (Bob’s 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. 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 analysis for discovery Interaction Device Resource-limited (TV + remote) Cumbersome input for annotation User Identity Household vs. Individual Handling concurrency Creation Experience Lean Back Attention vs. Interruption Consumption Model Paid Provider-controlled Utility vs. Legality around distribution
  24. 24. Provider Need: understand & acquire customers effectively Producing commercial content is costly  finer granularity of analytics (clip vs. item) can help Recoup costs with targeted advertising  clip annotations = viewer intent Reduce costs by de-risking decisions  clip activity = viewer interest
  25. 25. TWEET TV : TWITTER BASED ‘HEAT SEEKING’ FOR TV <ul><li>Social as Sensor </li></ul>
  26. 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. 27. Twitter sensor for the real-world : fast AND good <ul><li>For location-based queries 43% chance that a Twitter result will be the #1 ranked </li></ul><ul><li>General queries  23% chance that a Twitter result is #1 </li></ul><ul><li>Newest Twitter results ~4 seconds old. The newest Web results are 10x older (41 seconds). </li></ul><ul><li>A top ranking Twitter result for a location-based query  2 minutes old (vs Web which is 22 minutes old) </li></ul><ul><li>When Twitter results appear at least one of them is in the top ranked position </li></ul>
  28. 28. Sports world is at least as ‘chirpy’ #superbowl 4000 tps #worldcup 3300 tps
  29. 29. The trend hasn’t gone unnoticed ..
  30. 30. social navigation: e*pg <ul><li>Can we detect in-game events quickly & reliably by analyzing public twitter streams? </li></ul><ul><ul><ul><li>game event detection | user sentiment extraction | Loc | device </li></ul></ul></ul><ul><li>Can we overlay this sentiment on traditional EPG grids to create credible experience </li></ul><ul><li>Can this be done without miring end-user in unnatural amounts of profile filling </li></ul>sport team player mojo events
  31. 31. social navigation: e*pg <ul><li>Can we detect in-game events quickly & reliably by analyzing public twitter streams? </li></ul><ul><ul><li>fairly accurate for football and soccer </li></ul></ul><ul><ul><li>Sensing latency better than web 2.0 sources (30 seconds vs 2 minutes) </li></ul></ul><ul><li>Calibrating the Twitter sensor .. </li></ul><ul><ul><li>Sensitivity | latency | response to different game cadences | singularity detection ? </li></ul></ul>
  32. 32. TV ANSWERS: Q&A FOR TV <ul><li>Social as Router </li></ul>
  33. 33. Web search : from automated to human Credits. Aardvark@slideshare
  34. 34. Weak links : stronger than strong Credits. Aardvark@slideshare
  35. 35. TV & Social Search <ul><li>Focus on user-generated queries around viewed content </li></ul><ul><li>Real-world examples (from Yahoo! Answers ‘TV’ category) </li></ul><ul><li>Benefits of explicit search </li></ul><ul><ul><li>Finer granularity in defining focus user interest (about “X”) </li></ul></ul><ul><ul><li>Clearer idea of user intent (what is X  where can I buy X ) </li></ul></ul>
  36. 36. So, where’s the problem? <ul><li>Occur out-of-band today (online, in CQA communities) </li></ul><ul><ul><li>Requires a second device (PC) for querying while watching TV </li></ul></ul><ul><ul><li>Imposes cognitive (recall) and descriptive (entry) burden on user </li></ul></ul><ul><ul><li>TV out-of-the-loop for analytics tracking (dilutes user profiling) </li></ul></ul><ul><li>Tension between ‘lean-back’ and ‘interactive ’ behaviors </li></ul><ul><ul><li>BUT users will search the searchable given opportunity & means </li></ul></ul>http://weblogs.hitwise.com/bill-tancer/2007/01/special_k_another_tv_search_ca.html Increased traffic from a Kellogg ‘Special K” call to action. At the end of the spot the voice over urged users to go to Yahoo! and search on &quot;Special K.&quot; Spot included screen shot of Yahoo! Search
  37. 37. What’s the challenge in ‘inline’ TV Search? How to ask the question? ‘ Search’ interfaces on TV are cumbersome, limited in facets Human questions tend to be ambiguous, imprecise Whom to target for responses? Humans excel (over SE) in visual interpretation, intuitive query ‘tuning’, collective wisdom
  38. 38. The “TV Answers” System How to ask the question? ‘ Freeze-Frame’ interface to capture visual context ‘ Templates’ helper to ease query creation Whom to ask? ‘ Edge proxy’ intermediary to route query to relevant user communities for responses
  39. 39. from what to how Evolution of TV as Collaboration Platform
  40. 40. TV : not so long ago .. 05/16/11 Content | device | interactivity are all bundled
  41. 41. TV : here. now & a bit beyond .. 05/16/11 content cuts the device cord video & interactivity bundled content device
  42. 42. Interactivity & Collaboration. Evolutionary thread Operator Backoffice Set-top Branded content Internet Content
  43. 43. Dual-Screen TV : Incursion of couch top devices 05/16/11 device rendering interactivity content behind Moore’s beyond Moore’s separate & synchronize
  44. 44. Is Dual Screen for real? 05/16/11 2.2 M viewers 100 K dual screen users (week 1) 500 K plays tv appification .. on to the 2 nd screen.. with promising content proof pts .. fast go-to-market alternatives  audio fingerprints pervasive media multiplexing Ref. GigaOm. TV Apps: Evolution from Novelty to Mainstream
  45. 45. Benefits of Dual Screen 05/16/11 viewer content creator advertiser application developer personalized. private direct-to-consumer. superior analytics. shorter learning curve faster technology waves. ‘ cooler’ platforms Interactivity. superior targeting
  46. 46. TV ‘check-in’ : a TV apps data point <ul><li>Downloadable ‘TV applications’ on the smartphone marketplace </li></ul><ul><li>Support collaborative operations that don’t require TV data manipulation </li></ul><ul><ul><li>Recommend </li></ul></ul><ul><ul><li>Check-in </li></ul></ul><ul><ul><li>Chat with friends </li></ul></ul><ul><li><1 yr | 15 companies | $75M venture. </li></ul>Company Description Tunerfish ( Comcast ) Led by Plaxo (Comcast acquisition) team. Includes both TV check-in and social rewards GoMiso Funded by Google ventures . Android | iphone apps GetGlue #1 in traffic ( 5M monthly check-ins). Check-in + recommender technology. HotPotato Launched : 11/09. Acquired by Facebook : 8/10 Startling.tv Focus on broadcast partners. MTV alumni founders TV.com relay ( CBS ) CBS broadcasting
  47. 47. TV ‘check-in’ : a TV apps data point <ul><li>Downloadable ‘TV applications’ on the smartphone marketplace </li></ul><ul><li>Support collaborative operations that don’t require TV data manipulation </li></ul><ul><ul><li>Recommend </li></ul></ul><ul><ul><li>Check-in </li></ul></ul><ul><ul><li>Chat with friends </li></ul></ul><ul><li><1 yr | 15 companies | $75M venture. </li></ul>Company Description Tunerfish ( Comcast ) Led by Plaxo (Comcast acquisition) team. Includes both TV check-in and social rewards GoMiso Funded by Google ventures . Android | iphone apps GetGlue #1 in traffic ( 5M monthly check-ins). Check-in + recommender technology. HotPotato Launched : 11/09. Acquired by Facebook : 8/10 Startling.tv Focus on broadcast partners. MTV alumni founders TV.com relay ( CBS ) CBS broadcasting
  48. 48. Does more become less, or better? curation to the rescue? atomization of interest groups quality diversity timeliness 500 x 10 x 8 x 2 .. = biggish studio studio host mso cast player friends
  49. 49. Other ways TV futures depart from the past <ul><li>Removing the input bottleneck </li></ul><ul><ul><li>keyboard | gesture </li></ul></ul><ul><ul><li>and dealing with the identity challenge </li></ul></ul><ul><li>Video symmetry </li></ul><ul><ul><li>webcam as sensor? </li></ul></ul><ul><li>Game mechanics for persuasion </li></ul><ul><ul><li>heuristics </li></ul></ul><ul><ul><li>to science? </li></ul></ul>
  50. 50. Future of TV as collaboration platform looking back at mobiles Application optimized platform (voice | messaging) Collaboration horsepower Step change in platform capabilities | trajectory Time Platform as Application Magnet New sensors (GPS) New modalities (touch) New networks (WiFi) Web Enablement Content Rights Innovation (a la carte) Symmetric rich media
  51. 51. Future of TV as collaboration platform speculation by analogy Platform as Application Magnet New sensors (webcam) New modalities (gesture) New networks (WiFi) Web Enablement Content Rights Innovation (Internet TV) Symmetric rich media Collaboration horsepower ? Time Application optimized platform (broadcast | on-demand video)
  52. 52. In summary .. Can TV be a collaborative platform Already there Can couch potatoes be collaborators? yes
  53. 53. Questions?

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