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Cool Coolhunts
Peter A. Gloor MIT Center for Collective Intelligence
1
Organization Intranet/E Predict project success
World Web/Blog/ Social Networking sites
Predict movie box office success Predict political electionsPredict consumer sentimentPredict stocks
Organization Intranet/E
Unit of Interaction Research GoalsObservation Archives
World Web/Blog/ Social Predict movie box office success Networking sites Predict political elections
Predict consumer sentimentPredict stocks
Organization Intranet/E- Predict project success Mail/phone log Predict startup success
Individual Web/Blog/E- Predict personality characteristics Mail/phone log/ sociometric badges
Predict team success
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Web and Blogs correspond to Real World
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Predicting US Presidential Elections, Nov. 4, 2008
On Election Night Blog Buzz metric: 47% McCain 53% Obama
Corresponds to popular vote!
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Predicting Oscars and Box Offices Success
5 5
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Investment Trends
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F/X Trading
EUR/USD returns Baseline Model R2 adjusted
“USD”/”EURO” 0.13*
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S&P Trading
S&P returns Baseline Model R2 adjusted
VIX/VIX 1st diff 0.064**
“United States”/”EURO” 0.042**
VIX/VIX 1st diff, “United States”/”EURO
0.169**
VIX: Chicago Board Options Exchange Volatility Index
(blend of options for S&P stock)= magnitude of expected changes in S&P 500 index over the next 30-day
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Twitter Stockprediction
hope, fear, worry
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Individual Web/Blog/E Predict personality characteristics
Organization Intranet/E-Mail/phone log
Predict project success Predict startup success
Unit of Interaction Observation Archives
Research Goals
World Web/Blog/ Social Networking sites
Organization Intranet/E-Mail/phone log
Individual Web/Blog/E-Mail/phone log/ sociometric badges
Predict movie box office success Predict political electionsPredict consumer sentimentPredict stocks
Predict project success Predict startup success
Predict personality characteristics Predict team success
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All founders of 12 Universities
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The Digital Divide
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Individual Web/Blog/E-Mail/phone log/ sociometric badges
Predict personality characteristics Predict team success
Unit of Interaction Research GoalsObservation Archives
World Web/Blog/ Social Predict movie box office success Networking sites Predict political elections
Predict consumer sentimentPredict stocks
Organization Intranet/E- Predict project success Mail/phone log
Individual Web/Blog/E-
Predict startup success
Predict personality characteristics Mail/phone log/ sociometric badges
13
Predict team success
Creativity and Performance of Eclipse Developers
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Individual Signals
Blue – drummer
Red – singer/leader
Green - audience
© 2011 Peter A. Gloor 15
Social Network of Nurses in PACU
The leadership communicates with each other RN 274 is by far the most between! The leadership communicates with each other
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Tom A
Thank Yllen Fillia Makedon
ou Robin Athey Tom Malone
Pascal Marmier Melina Becker Chris Miller Hans Brechbuhl Stefan Nann Gloria Busche Keiichi Nemoto Scott Cooper Tuomas Niinimäki Marius Cramer Daniel Olguin Olguin Marco DeMaggio Daniel Oster Pierre Dorsaz Maria Paasivaara Lyric Doshi Sandy Pentland Scott Dynes John Quimby Marc Egger Johannes Putzke Eric Esser Ornit Raz Kai Fischbach Renaud Richardet Hauke Führes Ken Riopelle Julia Gluesing Michael Schober Francesca Grippa Detlef Schoder George Herman Thomas Schmalberger Takashi Iba Shosta Sulonen Bill Ives Masamichi Takahashi Eric Johnson David Verrill Adriaan Jooste Christoph Von Arb Jermain Kaminsiki Ben Waber Min-Hyung Kang Andrew Westerdale Yared Kidane JoAnn Yates Reto Kleeb Wayne Yuhasz Jonas Krauss Xue Zhang http://www.ickn.org Dustin Larimer Antonio Zilli http://www.swarmcreativity.net Casper Lassenius Yan Zhao Rob Laubacher www.galaxyadvisors.com
Charles Leiserson
17© 2011 Peter A. Gloor
MIT OpenCourseWarehttp://ocw.mit.edu
15.599 Workshop in IT: Collaborative Innovation NetworksFall 2011 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.