Visualisingconversation around#c4thepromiseSteve Winton, Jenni Lloyd & Edd Parris
Interesting things are afoot in TV Land
The clientThat well-known publicservice broadcasterInnovative, and up fordoing this differently
DIKW        Wisdom       Knowledge       Information          Data
Datacopter!www.datacopter.com
The programmeThe PromiseHighly-sensitive, controversialExtremists on both sides of the issuePotentially even damaging to C...
TrailerSpoiler alert!
The briefDemonstrate how Channel 4 is fulfilling its role as apublic service broadcasterExpose all aspects of the conversa...
We need a mechanic that will show the varietyas well as the volume.
Designs and PrototypeEarly designsEarly prototype
InspirationtitleCould we create a ‘conversation constellation’?
Early designs
Early prototype
How to catch a tweetGnipPuSH (Superfeedr)Streaming API / DatasiftPolling
Gnip       Very good, but very expensive
PuSH (Superfeedr)        Inexpensive        But, in our tests, failed to deliver
Streaming API / Datasift         We ♥ streaming         But, not suitable for this project
Polling          Old-fashioned, but reliable          Picks up tweets retrospectively :)          Pre-moderated, so near r...
PlatformSystem architecture
System architecture
Process overview                   A tweet arrives
Process overview               We remove all the cruft
Process overview    Adjectives   Nouns   Verbs   Adverbs    Hashtags     URLs    Users                    We use an NLP al...
Process overview               We build up a mahoosive               database of               interconnected phrases
Process overview               We create a “phrases               graph” that represents               the conversation
Process overview               And a distances matrix               (phrases that commonly               occur together ar...
Process overviewAnd then, like looking at towns on a map,we can identify communities of phrases,and group them into cluste...
Cheats         A B start
The app in its natural habitat                        http://j.mp/twitter-tracker
The app in its natural habitat                        http://j.mp/twitter-tracker
99 Problems :The BossBad ideasTesting
The Boss
Bad ideas        Initial clustering algorithm took an hour to        run :        Catching the long tail        Refreshing...
Test data            Hard to predict and design for what people            will actually talk about            And so hard...
Learnings       Realtime and pre-moderation, not the best of       friends       Realtime + NLP + graph analysis is an    ...
LearningsUnderstanding the nature of the conversation (attentionpatterns, responses over time, conversations on the side)
Learningsanticipation   immersionpreparation    review
Learnings       Promoting the hashtag on-air works!
Learnings       Lots of activity during live web chats
What next? For broadcastersSocial TVMaking the most of the ‘second screen’TV checkinsSocial experience + time-shifted view...
What next? For DatacopteriOSReal real-timeBetter NLPRevisit the UXContributors, influencers, valuesFitter, healthier and m...
Thanks! :)                Any             questions?
Thanks! :)You’ve been wonderful x        Stay in touch?        Jenni Lloyd / @jennilloyd / jenni.lloyd@nixonmcinnes.co.uk ...
Creditshttp://www.flickr.com/photos/38063599@N00/2632323465/http://www.flickr.com/photos/michibertolino/2326851802/http://...
Visualising conversation around #c4thepromise
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Visualising conversation around #c4thepromise

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How we built Channel 4's Twitter Tracker for The Promise.

Presented at the Twitter DevNest, March 2011.

Published in: Technology, Business
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  • We work for NixonMcInnes, we are a bunch of coders, strategists, designers and thinkers...\n
  • TV - ripe for innovation\nTV sparks conversation, and always has done\nBUT now there’s an interesting platform to capture that\nNew behaviours emerging\nPeople using Twitter and their social networks to engage around TV programmes\n
  • Channels\n2-screen\nTime-shifted viewing\n\nA very interesting client to work with, always up for doing new and interesting things, that haven’t been done before\nBrave\nA certain amount of their programming should serve the public interest, educational\n
  • Applies to broadcaster, and viewer\n
  • Connecting with the audience at home\nExtending the studio experience to the Twitter audience\nThe audience becomes a part of the show\nA lot to learn from this (for C4) and a lot of interest within C4\n
  • The Promise\n
  • http://www.flickr.com/photos/michibertolino/2326851802/\n
  • \n
  • Also, pre-moderated!!!\n
  • \n
  • Also, pre-moderated!!!\n
  • \n
  • \n
  • \n
  • Edd next!!!\n
  • \n
  • Edd\n
  • Edd\n
  • Edd\n
  • Edd\n
  • Edd\n
  • Edd\n
  • Edd\n\nSteve coming up!\n
  • Steve\n
  • Steve\n
  • Steve\n
  • Steve\n
  • Steve\n
  • Steve\n
  • Steve\n
  • ????\nKnown phrases\nTweaking as we went, lots of config options in the db, cuz of the unknown nature of what we would actually get!\n\nSteve\n
  • Jenni\n
  • Jenni\n
  • Edd\n
  • Edd\n
  • Edd\n
  • Edd\n\nJenni coming up!\n\nTesting. Lack of realistic data, volume and content\n
  • Gaining insight into different behaviour, how people tweet, the language they use, depending on the kind of programming, the time of day (around TX), the types of conversation before during and after TX\n\n\nAnticipation, preparation, reaction, considerations \nMomentum\nHashtag promotion\nPost-TX Web chat\n
  • Jenni\n
  • \n
  • Stemming\nClustering\nGraphs\nRealtime data + NLP is tricky and unpredictable\nRealtime + moderation not so good\nWorks well as an artifact that lives beyond the drama series\nGood response when broadcaster mentions the hashtag at the beginning of the show! Promoting the hashtag works!\n
  • Stemming\nClustering\nGraphs\nRealtime data + NLP is tricky and unpredictable\nRealtime + moderation not so good\nWorks well as an artifact that lives beyond the drama series\nGood response when broadcaster mentions the hashtag at the beginning of the show! Promoting the hashtag works!\n
  • Jenni\n
  • Steve\n
  • \n
  • \n
  • \n
  • Visualising conversation around #c4thepromise

    1. 1. Visualisingconversation around#c4thepromiseSteve Winton, Jenni Lloyd & Edd Parris
    2. 2. Interesting things are afoot in TV Land
    3. 3. The clientThat well-known publicservice broadcasterInnovative, and up fordoing this differently
    4. 4. DIKW Wisdom Knowledge Information Data
    5. 5. Datacopter!www.datacopter.com
    6. 6. The programmeThe PromiseHighly-sensitive, controversialExtremists on both sides of the issuePotentially even damaging to Channel 4’s brand
    7. 7. TrailerSpoiler alert!
    8. 8. The briefDemonstrate how Channel 4 is fulfilling its role as apublic service broadcasterExpose all aspects of the conversation, and not bedominated by the political/religious elementsEncourage exploration of, and participation in, the onlineconversation
    9. 9. We need a mechanic that will show the varietyas well as the volume.
    10. 10. Designs and PrototypeEarly designsEarly prototype
    11. 11. InspirationtitleCould we create a ‘conversation constellation’?
    12. 12. Early designs
    13. 13. Early prototype
    14. 14. How to catch a tweetGnipPuSH (Superfeedr)Streaming API / DatasiftPolling
    15. 15. Gnip Very good, but very expensive
    16. 16. PuSH (Superfeedr) Inexpensive But, in our tests, failed to deliver
    17. 17. Streaming API / Datasift We ♥ streaming But, not suitable for this project
    18. 18. Polling Old-fashioned, but reliable Picks up tweets retrospectively :) Pre-moderated, so near real-time is good enough
    19. 19. PlatformSystem architecture
    20. 20. System architecture
    21. 21. Process overview A tweet arrives
    22. 22. Process overview We remove all the cruft
    23. 23. Process overview Adjectives Nouns Verbs Adverbs Hashtags URLs Users We use an NLP algorithm to extract phrases and tag the ‘parts of speech’
    24. 24. Process overview We build up a mahoosive database of interconnected phrases
    25. 25. Process overview We create a “phrases graph” that represents the conversation
    26. 26. Process overview And a distances matrix (phrases that commonly occur together are close to each other)
    27. 27. Process overviewAnd then, like looking at towns on a map,we can identify communities of phrases,and group them into clusters.
    28. 28. Cheats A B start
    29. 29. The app in its natural habitat http://j.mp/twitter-tracker
    30. 30. The app in its natural habitat http://j.mp/twitter-tracker
    31. 31. 99 Problems :The BossBad ideasTesting
    32. 32. The Boss
    33. 33. Bad ideas Initial clustering algorithm took an hour to run : Catching the long tail Refreshing the app at 9pm
    34. 34. Test data Hard to predict and design for what people will actually talk about And so hard to test with meaningful, realistic data
    35. 35. Learnings Realtime and pre-moderation, not the best of friends Realtime + NLP + graph analysis is an interesting problem to tackle at scale
    36. 36. LearningsUnderstanding the nature of the conversation (attentionpatterns, responses over time, conversations on the side)
    37. 37. Learningsanticipation immersionpreparation review
    38. 38. Learnings Promoting the hashtag on-air works!
    39. 39. Learnings Lots of activity during live web chats
    40. 40. What next? For broadcastersSocial TVMaking the most of the ‘second screen’TV checkinsSocial experience + time-shifted viewing
    41. 41. What next? For DatacopteriOSReal real-timeBetter NLPRevisit the UXContributors, influencers, valuesFitter, healthier and more productive
    42. 42. Thanks! :) Any questions?
    43. 43. Thanks! :)You’ve been wonderful x Stay in touch? Jenni Lloyd / @jennilloyd / jenni.lloyd@nixonmcinnes.co.uk Steve Winton / @steveWINton / steve.winton@nixonmcinnes.co.uk Edd Parris / @empika / edward.parris@nixonmcinnes.co.uk
    44. 44. Creditshttp://www.flickr.com/photos/38063599@N00/2632323465/http://www.flickr.com/photos/michibertolino/2326851802/http://www.flickr.com/photos/85791047@N00/5352474332/http://www.flickr.com/photos/snakphotography/4365066875/http://www.flickr.com/photos/jeffanddayna/4610127963/http://www.flickr.com/photos/tmartin/71654890/http://www.theplace2.ru/archive/river_phoenix/img/kinopoisk_ru_River_P-1.jpghttp://www.flickr.com/photos/marilynjane/482679465/http://www.flickr.com/photos/sheilaellen/111377949/http://barrygruff.wordpress.com/2010/11/16/jay-z-99-problems-the-prodigy-remix/http://www.flickr.com/photos/pasukaru76/4892378102/http://muppet.wikia.com/http://www.flickr.com/photos/whatcouldgowrong/4608963722/http://www.flickr.com/photos/paullikespics/3279094697/http://www.flickr.com/photos/scissorhands33/3430164569/
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