Your SlideShare is downloading. ×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Collective stream and metadata june 2010 by PeopleBrowsr

831

Published on

PeopleBrowsr presents Collective Stream & Metadata June 2010 …

PeopleBrowsr presents Collective Stream & Metadata June 2010

The Collective Stream and Meta Data Cloud is profoundly changing the way we write code, analyze events.
Vast dynamic data stores power collective consciousness

Published in: Technology
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
831
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
8
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • AU election - Nic
  • Transcript

    • 1. The Collective Stream and the Metadata Cloud A June 2010 Review Jodee Rich CEO PeopleBrowsr
    • 2. HUMAN SOCIALISATION Swinging through the trees..
    • 3. HUMAN SOCIALISATION Emerging from the jungle with Language The Collective Stream and Metadata – June 2010
    • 4. HUMAN SOCIALISATION Thousands of years later we wrote it down
    • 5. HUMAN SOCIALISATION PCs, the internet, cell phones have come together to enable a vast distributed network of human intelligence
    • 6. HUMAN SOCIALISATION A Persistent Stream of Consciousness..
    • 7. HUMAN SOCIALISATION Established Infrastructure, the Stream and the Meta Cloud
    • 8. HUMAN SOCIALISATION Persistent Open Meta Framework displaces established Infrastructure
    • 9. HUMAN SOCIALISATION Government Intervention
    • 10. HUMAN SOCIALISATION Collective Consciousness and Industry Disruption – May 2010 Stream Dries up…
    • 11. HUMAN SOCIALISATION Or WE adapt..
    • 12. 1 YEAR OF TWITTER TRAFFIC: View chart and stats on analytic.ly Now at 50 Million Tweets/day
    • 13. BRAND METADATA - 1 MILLION BRAND MENTIONS PER DAY
    • 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. OPENNESS Because it is open, the Twitter Stream will become the core transport layer for rich MetaData and Cross Network Links
    • 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. 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. ABC TWEETERS HOTLIST Merge Corporate Exec profile metadata
    • 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. 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. Airline Sentiment Metadata merging Mechanical Turk with the Twitter Stream. 95% accuracy Vs 70-80% automation alone US AIRLINE INDUSTRY STUDY JUNE 2009
    • 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.
      • Analytics:
        • Overlayed Sentiment , Brand and Ad Metadata
        • Effect of Traditional Media on Social Media
        • Mechanical Turk to measure accurate Sentiment
        • Metrics to measure Success:
          • Total Mentions
          • Positive Mentions
      By Volume Mullen and Radian6 SUPER BOWL Collective Consciousness and Industry Disruption – May 2010
    • 24. SUPER BOWL
      • Results:
        • 103,158 Total Mentions
        • Sampled 1000 Tweets from Every Brand and used Mechanical Turk Human Sentiment to analyze
        • Polarized:
          • 50% Positive
          • 28% Negative
          • 18% Neutral
      Collective Consciousness and Industry Disruption – May 2010
    • 25. SUPER BOWL Collective Consciousness and Industry Disruption – May 2010
    • 26. SUPER BOWL Correlation of Tweets and Ads
    • 27. UK ELECTION DASHBOARD
    • 28.
      • Top Bands:
      • Mgmt
      • Vampire Weekend
      • Passion Pit
      • Anamanaguchi
      • Animal Collective
      • The Strokes
      • Researched 900 bands in NY Extracted mentions of each in the last 6 months Selected most mentioned
      NEW YORK MUSIC INDUSTRY
      • Top Music Influencers:
      • @Jimmyfallon
      • @Nytimes
      • @TheOnion
      • @johnlegend
      • @maddow
      • @InStyle Influencers – extracted biggest music labels/accounts followers + everyone in NY on Twitter directory + NY users under music/venues Twitter lists
      Persona Metadata
    • 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. Mentions, RTs, … Comments, Sharing,… Profiles, Comments, … Status Updates, Comments, … Pictures, Comments, … Connections, Comments Blogs Mentions, … Fan Pages SMS CROSS PLATFORM INTEGRATION
    • 31. THE NEXT TWO YEARS The Conversation Stream becomes the Conversation Cloud A real time historical record Meta Data Hyperlinks become People Hyperlinks
    • 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. EXPERIMENTAL APPS What can we build? T2 Contextual Search and Post Artificial Intelligence - AI Cloud powered Q and A
    • 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. 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. EXPERIMENTAL APPS T2 What can we build? T2 Contextual Search and Post Inline Content T2 HyperLocal Linked to other netwoks
    • 37. EXPERIMENTAL APPS T2
    • 38. EXPERIMENTAL APPS T2
    • 39. VAST DYNAMIC DATA STORES POWER COLLECTIVE CONSCIOUSNESS
    • 40. REFERENCES This Deck http://bit.ly/ 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/

    ×