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J. Rich How the Meta Cloud is Changing Development Social Developer Summit

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J. Rich How the Meta Cloud is Changing Development Social Developer Summit

  1. 1. The Collective Stream and the Metadata Cloud A June 2010 Review Jodee Rich CEO PeopleBrowsr
  2. 2. HUMAN SOCIALISATION Swinging through the trees..
  3. 3. HUMAN SOCIALISATION Emerging from the jungle with Language The Collective Stream and Metadata – June 2010
  4. 4. HUMAN SOCIALISATION Thousands of years later we wrote it down
  5. 5. HUMAN SOCIALISATION PCs, the internet, cell phones have come together to enable a vast distributed network of human intelligence
  6. 6. HUMAN SOCIALISATION A Persistent Stream of Consciousness..
  7. 7. HUMAN SOCIALISATION Established Infrastructure, the Stream and the Meta Cloud
  8. 8. HUMAN SOCIALISATION Persistent Open Meta Framework displaces established Infrastructure
  9. 9. HUMAN SOCIALISATION Government Intervention
  10. 10. HUMAN SOCIALISATION Collective Consciousness and Industry Disruption – May 2010 Stream Dries up…
  11. 11. HUMAN SOCIALISATION Or WE adapt..
  12. 12. 1 YEAR OF TWITTER TRAFFIC: View chart and stats on analytic.ly Now at 50 Million Tweets/day
  13. 13. BRAND METADATA - 1 MILLION BRAND MENTIONS PER DAY
  14. 14. 2010 OPENNESS Little Twitter is dragging the others out of the cave and into the open Openness and Diversity is fundamental to a Meta Data System
  15. 15. OPENNESS Because it is open, the Twitter Stream will become the core transport layer for rich MetaData and Cross Network Links
  16. 16. Social Meta Data Examples Links Sentiment Hashtags Likes ReTweets Influence. Eg Klout Extended Profile Brand Pics Lists Personas. Eg Tlists Connections Relatedness Cross Media Rels
  17. 17. CASE STUDIES IN MAY 2010 ABC Hotlist Sony Pictures Comcast Entertainment Airline Sentiment eBay Toyota Recall Super Bowl Ads UK Elections Music Influencers in New York
  18. 18. ABC TWEETERS HOTLIST Merge Corporate Exec profile metadata
  19. 19. Goal: Evaluate impact of Traditional Media on the Social Media sphere Build engaged audience Solution: 180 day Historical Analysis of Posts overlay on TV Ad spend metadata and other channels Performance and Results Identified type of ads that produce the best audience response 50% fluctuation on engagement based on time of message release
  20. 20. Industry : Media Entertainment Goals: Promote a Network TV Premiere Create online Buzz during the Event Performance and Results N umber 1 Twitter Trending Topic during Premiere Over 17,000 mentions of the #Hashtag during the week of the Premiere
  21. 21. Airline Sentiment Metadata merging Mechanical Turk with the Twitter Stream. 95% accuracy Vs 70-80% automation alone US AIRLINE INDUSTRY STUDY JUNE 2009
  22. 22. Seek an effective way to measure brand sentiment accurately. The goal is to find a list of influencers speaking in both positive and negative terms and engage. Call center to respond to negative sentiment metadata everyday Velocity 10,000 Mentions/day filtered to 180 Meaningful comments
  23. 23. <ul><li>Analytics: </li></ul><ul><ul><li>Overlayed Sentiment , Brand and Ad Metadata </li></ul></ul><ul><ul><li>Effect of Traditional Media on Social Media </li></ul></ul><ul><ul><li>Mechanical Turk to measure accurate Sentiment </li></ul></ul><ul><ul><li>Metrics to measure Success: </li></ul></ul><ul><ul><ul><li>Total Mentions </li></ul></ul></ul><ul><ul><ul><li>Positive Mentions </li></ul></ul></ul>By Volume Mullen and Radian6 SUPER BOWL Collective Consciousness and Industry Disruption – May 2010
  24. 24. SUPER BOWL <ul><li>Results: </li></ul><ul><ul><li>103,158 Total Mentions </li></ul></ul><ul><ul><li>Sampled 1000 Tweets from Every Brand and used Mechanical Turk Human Sentiment to analyze </li></ul></ul><ul><ul><li>Polarized: </li></ul></ul><ul><ul><ul><li>50% Positive </li></ul></ul></ul><ul><ul><ul><li>28% Negative </li></ul></ul></ul><ul><ul><ul><li>18% Neutral </li></ul></ul></ul>Collective Consciousness and Industry Disruption – May 2010
  25. 25. SUPER BOWL Collective Consciousness and Industry Disruption – May 2010
  26. 26. SUPER BOWL Correlation of Tweets and Ads
  27. 27. UK ELECTION DASHBOARD
  28. 28. <ul><li>Top Bands: </li></ul><ul><li>Mgmt </li></ul><ul><li>Vampire Weekend </li></ul><ul><li>Passion Pit </li></ul><ul><li>Anamanaguchi </li></ul><ul><li>Animal Collective </li></ul><ul><li>The Strokes </li></ul><ul><li>Researched 900 bands in NY Extracted mentions of each in the last 6 months Selected most mentioned </li></ul>NEW YORK MUSIC INDUSTRY <ul><li>Top Music Influencers: </li></ul><ul><li>@Jimmyfallon </li></ul><ul><li>@Nytimes </li></ul><ul><li>@TheOnion </li></ul><ul><li>@johnlegend </li></ul><ul><li>@maddow </li></ul><ul><li>@InStyle Influencers – extracted biggest music labels/accounts followers + everyone in NY on Twitter directory + NY users under music/venues Twitter lists </li></ul>Persona Metadata
  29. 29. SMS is the benchmark Twitter, Facebook and the other networks are still small, 150 Million posts/day combined SMS is over 7 Billion/day SCALE..ITS EARLY DAYS
  30. 30. Mentions, RTs, … Comments, Sharing,… Profiles, Comments, … Status Updates, Comments, … Pictures, Comments, … Connections, Comments Blogs Mentions, … Fan Pages SMS CROSS PLATFORM INTEGRATION
  31. 31. THE NEXT TWO YEARS The Conversation Stream becomes the Conversation Cloud A real time historical record Meta Data Hyperlinks become People Hyperlinks
  32. 32. THE NEXT TWO YEARS The Conversation Cloud becomes the Rich Meta Data Cloud Social Meta Data Cloud will become the core backbone for people data
  33. 33. EXPERIMENTAL APPS What can we build? T2 Contextual Search and Post Artificial Intelligence - AI Cloud powered Q and A
  34. 34. EXPERIMENTAL APPS AI What can we build? In the past the quest for AI has been driven by machine learning projects. They have been Training and CPU intensive
  35. 35. EXPERIMENTAL APPS AI What can we build? Artificial Intelligence AI is now Build a database of Questions and Answers from the Twitterverse Crowdsource Questions without Answers – Crowdflower Devote CPU cycles to contextual analysis and NLP Artificial Intelligence AI was about machine learning or CPU cycles For the first time we have a vast open database of Questions and Answers Lets turn the problem upside down..
  36. 36. EXPERIMENTAL APPS T2 What can we build? T2 Contextual Search and Post Inline Content T2 HyperLocal Linked to other netwoks
  37. 37. EXPERIMENTAL APPS T2
  38. 38. EXPERIMENTAL APPS T2
  39. 39. VAST DYNAMIC DATA STORES POWER COLLECTIVE CONSCIOUSNESS
  40. 40. REFERENCES This Deck http://bit.ly/MetaCloud www.Analytic.ly Socialnomics09 PeopleBrowsr Super Bowl Study PeopleBrowsr Top 20 Brands Study http://www. s lides hare .net/peoplebrowsr/the-twitter-metadata-revolution-and-collective-consciousness http://www. nytimes .com/external/readwriteweb/2010/05/17/17readwriteweb-twitter-forefather-leaves-aims-to-disrupt-b-89770.html http://blogs.hbr.org/research/2010/05/why-gallup-when-you-can-tweet.html http://www. briansolis .com/2010/05/report-top-20-brands-on-twitter-april-2010/

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  • AU election - Nic

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