Presentation of the crowdsourced geotagging service HP gloe from a research perspective

1. Crowdsourcing and Markets in the
Social Media Age
Thomas Sandholm—Research Scientist, HP Labs, Palo Alto
December 17, 2009
1 © Copyright 2009 Hewlett-Packard Development Company, L.P.

2. Information Abundance and
Attention Scarcity
– Information overflow
on mobile devices
•Small visual real-estate
•Limited bandwidth
•Limited input capabilities
– How can crowdsourcing,
markets, and context
improve the mobile Web
experience?
2 © Copyright 2009 Hewlett-Packard Development Company, L.P.

3. Agenda
– Crowdsourcing
– Markets
– Social Media
– Examples
– SCL Projects
– HP Gloe
3 © Copyright 2009 Hewlett-Packard Development Company, L.P.

4. CROWDSOURCING
4 © Copyright 2009 Hewlett-Packard Development Company, L.P.

5. Wisdom of Crowds
– Surowiecki: The Wisdom of Crowds (2004)
•Skepticism about dumb mobs
•Crowds can be smarter than smartest individual if:
1.Diverse (otherwise no new ideas)
2.Independent (otherwise groupthink)
3.Decentralized (otherwise specialization is hard)
4.Aggregation (otherwise local knowledge is lost)
5 © Copyright 2009 Hewlett-Packard Development Company, L.P.

6. Crowdsourcing or Peer Production:
Specialization and reputation drive long tail content
production
– Business model
•Open call
•Parallel problem solving
•Vetting of solutions
– Phenomenon
•Historically: few publishers and millions of readers
•Now: millions of publishers with few readers
– Examples
•Wikipedia, YouTube, Linux, Digg
6 © Copyright 2009 Hewlett-Packard Development Company, L.P.

7. Dynamics of Peer Production
Why do people contribute to social media?
– Lack of attention reason to stop contributing
– Feedback loop of attention leads to power laws
– Negligible part of content produced by majority of
users
– Negligible number of users produce majority of
content
– Hubs of well-connected users vital to dissemination
– Persistence paradoxically inversely correlated to
success
7 © Copyright 2009 Hewlett-Packard Development Company, L.P.

8. MARKETS
8 © Copyright 2009 Hewlett-Packard Development Company, L.P.

9. Market Properties
Surowiecki’s fourth condition
– Aggregation of decentralized local knowledge
– Determine value of goods
•Price varies until supply and demand are in equillibrium
•Self-adjusting and decentralized
– Walras’ Law in General Equilibrium Theory
•Sum of excess demand across all markets equals zero
– Market Mechanism:
•Incentive compatible, individually rational, strategy proof
•Incentive to participate truthfully, non-gameable
•Selfish optimization leads to welfare
•Effective high-quality, truthful information signals
9 © Copyright 2009 Hewlett-Packard Development Company, L.P.

10. Prediction or Information Markets
Focus on information aggregation and prediction
– Voting with costs and payoffs
– 1988 Forsythe, Nelson, Neumann: Iowa Electronic Markets
•Predict outcome of political races
•Polling less accurate due to phrasing of question
•Real money forces truthful behavior
– 2003 Hanson: DoD PAM ―Terrorism Futures‖
•Public outcry – unethical to bet on terrorism attacks
•Publicity – Crowdcast, Inkling, HubDub, Bet2Give
•Same games, some real for-profit markets
– Yahoo Tech Buzz, Google internal information flow markets
•Cube neighbors influence betting behavior
10 © Copyright 2009 Hewlett-Packard Development Company, L.P.

11. SOCIAL MEDIA
11 © Copyright 2009 Hewlett-Packard Development Company, L.P.

12. Web 2.0 Social Media Apps
Drove content production paradigm shift
– Long tail content
•Unpopular content gets published
– Grass root reputation
•meritocracy
•open APIs for mashups
– Collaborative filtering
•Amazon book recommendations
– The network effect
•More users more quality opposite to Web 1.0
– Social networks are leveraged to disseminate information
•Viral marketing
•Friends determine what you see
– Flickr, MySpace, YouTube, Facebook, Twitter
•Not technology leaders but grabbed critical social mass
•Innovators in how people interact
12 © Copyright 2009 Hewlett-Packard Development Company, L.P.

13. Context-aware applications
Location, Social, Time, Weather Conditions
– Mobile devices more personal
•Already used to interact with friends
– Sensors give more information to applications
•No explicit user involvement
– iPhone paved the way for the mobile Web
•Full-featured Web browsers on smartphones
– Affordable unlimited 3G data plans
– Privacy opt-in
•Intrusive apps more accepted if they have a social value
13 © Copyright 2009 Hewlett-Packard Development Company, L.P.

14. Location Based Services
Pull or push content based on sensed location
– Geography-aware service on mobile device
– GIS for mobile phones
– GPS + Internet on smartphones
– Find what’s around you
– Map based, AR based or Distance/Popularity Listings
– Google Navigation/Maps, Wikitude.Me, Layar,
Sherpa, Where, GeoVector, Loopt
14 © Copyright 2009 Hewlett-Packard Development Company, L.P.

15. EXAMPLES
15 © Copyright 2009 Hewlett-Packard Development Company, L.P.

16. Digg – a crowdsourced newspaper
– Crowd-rendered front page
– Novelty and popularity tradeoff
– Update frequency unmatched
16 © Copyright 2009 Hewlett-Packard Development Company, L.P.

17. Delicious – social bookmarking
– Share bookmarks with friends
– Find popular bookmarks
– Search by tags and tag clouds
17 © Copyright 2009 Hewlett-Packard Development Company, L.P.

18. Google AdWords – bidding for page
prominence
– Market mechanisms used to leverage attention economy
– Key word auctions
– Non-intrusive, relevant presentation
– Virtually all of Google’s revenue
18 © Copyright 2009 Hewlett-Packard Development Company, L.P.

19. SOCIAL COMPUTING
LAB PROJECTS
19 © Copyright 2009 Hewlett-Packard Development Company, L.P.

20. BRAIN
– Prediction of events in small groups
– Forecasting in teams biased towards ―loudest‖ member
– When members put a bet on their forecast they predict more
carefully and accurately
– Risk attitudes of members matter, when normalized away the
weighted prediction becomes more accurate
20 © Copyright 2009 Hewlett-Packard Development Company, L.P.

21. Tycoon
– Market based computational
resource allocation
– Resources capacity allocated
proportional to individual bids and
inversely proportional to aggregate
bids
– q = b/(b+y)
– Variable pricing, self adjusting to
demand
– Similar properties to second bid
auction but converges quicker
and has no discrete clearing times
21 © Copyright 2009 Hewlett-Packard Development Company, L.P.

22. HP Gloe
22 © Copyright 2009 Hewlett-Packard Development Company, L.P.

23. Idea
Vote on web page to location mappings
– Popularity search by radius, aggregation by URL
– Social filtering
– Folksonomy of hierarchical channels
– Cyberspace notes
– Votes governed by market economy
23 © Copyright 2009 Hewlett-Packard Development Company, L.P.

24. Popularity Ranking and Social Filtering
24 © Copyright 2009 Hewlett-Packard Development Company, L.P.

25. Folksonomy of hierarchical channels
Restaurants
Pizza Restaurants
Pizza
Restaurants
Burgers Restaurants
Diners
25 © Copyright 2009 Hewlett-Packard Development Company, L.P.

26. Cyberspace notes
26 © Copyright 2009 Hewlett-Packard Development Company, L.P.

27. Votes governed by market economy
– Limited recommendation budget
•American Idol type voting
– Contributions controlled by currency
•Earn, pay, penalties, income streams
– Optimal budget allocation
•Across regions in marketing campaigns
•Across hierarchical key words
– Voting minimal effort but truth-telling enforced
•Restaurant services forcing you to leave comments to vote
– Maximize information obtained from crowd
•Value distribution beyond 5 star ratings
27 © Copyright 2009 Hewlett-Packard Development Company, L.P.

28. Demo
– Launched on Android market Dec 1st
– ~6 million recommendations, ~8000 channels, ~150
users
– Data from Wikipedia, TripAdvisor, Panoramio,
WikiTravel, POI Plaza, IMDb, DBPedia, Geonames,
Google, Twitter, Bing
28 © Copyright 2009 Hewlett-Packard Development Company, L.P.

29. 29 © Copyright 2009 Hewlett-Packard Development Company, L.P.

30. 30 © Copyright 2009 Hewlett-Packard Development Company, L.P.

31. Research Problems
– Design a market that gives incentive to users to
contribute valuable mappings
– Visualization, control and optimization techniques to
trade off popularity (clicks and recs), novelty, and
distance
– Dynamic content provisioning, recommendation decay
– Optimized channel and location-based bidding
strategies
– Data analytics, e.g. behavioral patterns in Web page
recommendations
31 © Copyright 2009 Hewlett-Packard Development Company, L.P.

32. Summary
– Crowdsourcing can be a very effective
technique to offer long tail content if certain
conditions are met
– Markets can be used to control the quality of
contributions and to aggregate local
information concisely
– Your social network provides a way to
effectively filter and discover new information
32 © Copyright 2009 Hewlett-Packard Development Company, L.P.

33. Q&A
33 © Copyright 2009 Hewlett-Packard Development Company, L.P.