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.
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Visualising conversation around #c4thepromise
- 1. Visualising conversation around #c4thepromise Steve Winton, Jenni Lloyd & Edd Parris
- 2. Interesting things are afoot in TV Land
- 3. The client That well-known public service broadcaster Innovative, and up for doing this differently
- 4. DIKW Wisdom Knowledge Information Data
- 6. The programme The Promise Highly-sensitive, controversial Extremists on both sides of the issue Potentially even damaging to Channel 4’s brand
- 9. The brief Demonstrate how Channel 4 is fulfilling its role as a public service broadcaster Expose all aspects of the conversation, and not be dominated by the political/religious elements Encourage exploration of, and participation in, the online conversation
- 10. We need a mechanic that will show the variety as well as the volume.
- 11. Designs and Prototype Early designs Early prototype
- 12. Inspiration title Could we create a ‘conversation constellation’?
- 13. Early designs
- 14. Early prototype
- 15. How to catch a tweet Gnip PuSH (Superfeedr) Streaming API / Datasift Polling
- 16. Gnip Very good, but very expensive
- 17. PuSH (Superfeedr) Inexpensive But, in our tests, failed to deliver
- 18. Streaming API / Datasift We ♥ streaming But, not suitable for this project
- 19. Polling Old-fashioned, but reliable Picks up tweets retrospectively :) Pre-moderated, so near real-time is good enough
- 22. Process overview A tweet arrives
- 23. Process overview We remove all the cruft
- 24. Process overview Adjectives Nouns Verbs Adverbs Hashtags URLs Users We use an NLP algorithm to extract phrases and tag the ‘parts of speech’
- 25. Process overview We build up a mahoosive database of interconnected phrases
- 26. Process overview We create a “phrases graph” that represents the conversation
- 27. Process overview And a distances matrix (phrases that commonly occur together are close to each other)
- 28. Process overview And then, like looking at towns on a map, we can identify communities of phrases, and group them into clusters.
- 29. Cheats A B start
- 30. The app in its natural habitat http://j.mp/twitter-tracker
- 31. The app in its natural habitat http://j.mp/twitter-tracker
- 32. 99 Problems : The Boss Bad ideas Testing
- 33. The Boss
- 34. Bad ideas Initial clustering algorithm took an hour to run : Catching the long tail Refreshing the app at 9pm
- 35. Test data Hard to predict and design for what people will actually talk about And so hard to test with meaningful, realistic data
- 36. Learnings Realtime and pre-moderation, not the best of friends Realtime + NLP + graph analysis is an interesting problem to tackle at scale
- 37. Learnings Understanding the nature of the conversation (attention patterns, responses over time, conversations on the side)
- 38. Learnings anticipation immersion preparation review
- 39. Learnings Promoting the hashtag on-air works!
- 40. Learnings Lots of activity during live web chats
- 41. What next? For broadcasters Social TV Making the most of the ‘second screen’ TV checkins Social experience + time-shifted viewing
- 42. What next? For Datacopter iOS Real real-time Better NLP Revisit the UX Contributors, influencers, values Fitter, healthier and more productive
- 43. Thanks! :) Any questions?
- 44. 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
Editor's Notes
- 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
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- Also, pre-moderated!!!\n
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- Also, pre-moderated!!!\n
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- 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
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- 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
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