![Shirky on Groups In the past, we unconsciously assumed that people cannot self-assemble easily. Technology and expectations now allow the formation of otherwise “latent groups”. “ The scope of work that can [now] be done by non-institutional groups is a profound challenge to the status quo.”](https://image.slidesharecdn.com/hearstsocialtechnology-090413234216-phpapp01/85/Social-Technology-60-320.jpg)
Social Technology
- 1. Social Technology Marti A. Hearst UC Berkeley NSF, March 18, 2009
- 2. Social Technology: Technology used by groups of people. How society is being changed by technology-mediated interactions.
- 3. Talk Structure Examples of social (uses of) technology. Consequences. What makes social systems work? Implications for research and NSF.
- 4. Social Technology Categories Recruiting Outside Expertise Crowdsourcing Shared Data Shared World / Platform Collaborative Creation Social Networks Idea Markets Implicit Contributions
- 5. Recruiting Outside Expertise: The Goldcorp Challenge
- 6. GoldCorp Challenge Rob McEwen was stuck for ideas about how to decide where to drill in Ontario. Went to a meeting at MIT in 1999, heard about open source programming. Decided to try it with gold prospecting! Ran a contest Predictions both confirmed their geologists’ predictions and produced new sites. fractaltechnologies.com
- 7. Recruiting Outside Expertise: Innocentive
- 11. Recruiting Outside Expertise: Key Points One organization can’t be employing all the necessary expertise to solve hard problems. The connectivity of the Internet makes it possible like never before to find the missing expertise puzzle pieces. The Long Tail of personal expertise.
- 12. Crowdsourcing
- 13. Crowdsourcing: Amazon’s Mechanical Turk A pool of thousands of people Small tasks, small pay Many people do it for entertainment + pay Careful modularization required Already a research tool Relevance judgements for search NLP assessments User Interface assessments
- 14. Crowdsourcing: NASA Clickworkers Early experiment, in 2001 Mars images from Viking Orbiter Citizen Science in action
- 15. Crowdsourcing: More Examples Stork’s OpenMind Initiative (1999) Including a game: shoot zeros vs. O’s HotOrNot (2000) vonAhn’s ESP game (2004) KittenWar! (~2005)
- 17. Crowdsourcing: Key Points Little to no expertise needed. The connectivity of the Internet makes it possible like never before to find enough people with the time and willingness to do these tasks.
- 19. Shared Data: Augmenting Information Objects Comments Reviews Tags Ratings Favorites Bookmarks
- 20. Shared Data: Mashups
- 21. Shared Data: Influencing Outcomes by Voting American Idol Kitchen Democracy
- 26. Shared Data: Key Points Easy to participate, but requires some expertise or specialized access (have bought and used product, read the book, have an opinion about the legislation). Not a project with a coordinated goal; rather people are contributing to specific data items that they choose themselves. Being able to see and search the entire set of user-augmented data creates value for everyone.
- 27. Shared World / Platform Inspiring People to Create
- 28. Shared World / Platform: Participatory Games SIMS Games 100 million unit sales in sept 2008 TheSims2.com community Web siteboasts more than 4.3 million unique visitors every month World of Warcraft
- 29. Shared World / Platform: Third Party iPhone and Facebook Apps
- 30. Shared World / Platform: Key Points Requires people to have some skill or creativity; only a subset contribute. Long tail contributions, not to a coherent whole, not for a goal. The massive exposure and participation levels make it worthwhile to make these creations.
- 31. Large-Scale Collaborations Open source software Top Coder Wikipedia Peer 2 Patent
- 32. Peer 2 Patent Collaborative Patent Review
- 33. Peer 2 Patent
- 34. Peer 2 Patent
- 35. Peer 2 Patent
- 36. Large-Scale Collaborations: Key Points Usually requires some expertise; the kinds of expertise needed are heterogeneous. People are working together towards a shared goal. Can only be done because of the supporting technology. The pieces need to be modularized (sometimes by a central entity).
- 37. Idea Markets Set up a market with an idea as a premise. Public policy questions. How would crime rates change if more citizens could legally carry hidden guns? Make a market based on the crime rate change after a hidden-gun bill was passed. (Hansen 1999) Internal product markets. Manage IT portfolio via a trading market .
- 38. Idea Markets: Key Points Ferrets out hidden expertise or hidden information. People don’t have to expose what they know directly. People don’t have to know all pieces of the puzzle; it (hopefully) arises out of the mix. The connectivity of the Internet makes it possible like never before to find enough people with the right pieces of information to do this.
- 39. Social Networks Undirected social networks Facebook, MySpace, etc. Directed Social Networks Connected within an organization, or for a purpose. IBM’s Dogear Intranet system GovLoop Slideshare
- 40. Directed social network: GovLoop
- 41. GovLoop
- 42. GovLoop
- 43. GovLoop
- 44. Social Networks: Key Points Usually no expertise required. Identity is central, relationships are key. People make contributions, or just hang out. Value rises out of connectedness, sometimes leading to virality.
- 45. Implicit Contributions Clicks on Search results Recommended items Ads Search queries Anchor text (in hyperlinks)
- 46. Putting it all Together
- 47. Many People, Don’t See Others’ Activities / Data Innocentive / Goldcorp Mechanical Turk ESP Game KittenWar expertise
- 48. Many People, Shared Data, but Not Working Together SIMS tools, iPhone apps Idea Markets Crime fighting Reviews, comments, tags … Social networks, voting Expertise or Special knowledge
- 49. Many People, Shared Data, and Working Together Open source software Peer2Patent Wikipedia Top Coder Scientific Research
- 50. Consequences
- 51. Participation has Consequences influence the perception of which sentences should go in a summary Hu, Sun, Lim, CIKM’07 effect purchasing choices. Chevalier, J. A., and Mayzlin, D. 2006 Chen, Dhanasobhon, & Smith 2007 businesses no longer control the conversation about their products and brand. Bickart & Schindler 2001 Comments Reviews
- 52. Latent Groups Reified Have Consequences Stranded airplane passengers, 1999 vs 2006. HSBC bank, student accounts. Purple tunnel of doom ticket holders. Every camera-bearing person attending the Inauguration.
- 54. Consequences: Expectations Have Changed Building new CACM website: must have comments! Government must make data available! Companies must respond to customers!
- 55. Downsides Socially destructive uses: “ Ana girls” Autism/mercury controversy eBay fraud Also happens without social technology: “ Swiftboat veterans for truth” Bernie Madoff
- 56. Theoretical Underpinnings Well, some thoughts anyhow
- 57. Some Pontificating What has changed about groups? Clay Shirky, author of “Here comes everybody” What makes for a successful social site? Rashmi Sinha, CEO of slideshare Tim O’Reilly, O’Reilly media Clay Shirky, author of “Here comes everybody”
- 58. Shirky on Groups We have lots and lots of words for them: corporation, congregation, clique, cabal, club, crowd
- 59. Shirky on Groups Much of the structure of social institutions is a response to the difficulty of managing groups. (The organizational tools we use are those that are the least bad.) In changing the way groups form, communication is key. We now have tools that are flexible enough to match our in-built social capabilities.
- 60. Shirky on Groups In the past, we unconsciously assumed that people cannot self-assemble easily. Technology and expectations now allow the formation of otherwise “latent groups”. “ The scope of work that can [now] be done by non-institutional groups is a profound challenge to the status quo.”
- 61. What leads to successful social?
- 62. Defining Web 2.0 Defining by Example, Brainstorming Starting Point, Tim O’Reilly & others FooCamp, Aug 2005
- 63. Web 2.0 Principles O’Reilly on The Web 2.0 lesson: leverage customer-self service and algorithmic data management Web as Platform Long tail (fringes) vs. Head (Akamai vs bittorrent (p2p), doubleclick vs adsense, and more recently hulu vs youtube) Participation vs. publishing (scripting) Reality tv vs. situation comedies American Idol vs. record studios
- 64. Web 2.0 Collaboratively Generated Definition of Web 2.0 FooCamp, Aug 2005
- 65. Long Tail = Lots of “Failure” The power law nature of the information use means: Most flickr photos are not commented on Most blogs are read by only a few people Most sourceforge projects have few downloads Only a few meetup groups get really popular Making it cheap to fail allows for exploring a wide range of options. But companies can’t hire people this way.
- 66. Sinha: “ Fast, cheap, & somewhat in control” Leave the beta underdeveloped (perpetual beta) Less polished look allows users to feel ownership Put it out there. Respond. Refine. Communicate with the first 10,000 users (Caterina Fake of flickr says the same thing) They email you every day, want to visit your offices Customer service as user research Design for crowds: Users drive navigation (tags, popularity) Social networks, people interacting, comments, ratings.
- 67. Relinquish Control Example: Plentyoffish.com Markus Frind, NYTimes 1/13/08 1.2 billion page hits in Dec ’07 Only one employee, 10 hours/week Site users do the photo screening Example: Obama campaign Give volunteers real responsibility Incentivize with goals Don’t try to control outsiders’ messages
- 68. Relinquish Control Wikipedia, Linux, had visible leaders, but the rules were developed by the participants. Edinburgh Fringe Festival / Foo camp: big public events; the organization arises via controlled chaos.
- 69. Shirky: Successful Social Sites Three rules, but hard to combine them Plausible Promise Appropriate Tools Acceptable Bargain
- 70. Plausible Promise The paradox of groups: I won’t join unless I know others will too. Must attend personally to those who join early to help with this in many cases.
- 71. Appropriate Tools Delicious tagging allows the individual to benefit immediately, the group benefits as a side-effect. Livejournal benefited from having groups of teens joining together.
- 72. Acceptable Bargain Wikipedia had to make it clear they wouldn’t go commercial in order to continue to expand. Some flickr groups have commenting rules. eBay didn’t initially allow for a reputation system, but had to add it in. AOL guide writers felt they had been wronged after the site sold for lots of money. Digg had to allow posting of a DRM key
- 73. The Goverati
- 74. The Push for Open Data www.maplight.org Gathering open data sets, doing mashups, esp. congressional voting records GovTrack.us Gathering data on federal legislation stimuluswatch.org Asking people to enter their own data on how Recovery funds are being spent. www.ombwatch.org Internally gathered data (apparently) “ Government Data and the Invisible Hand” Paper by Felton et al.
- 75. Government Opening Data USAspending.gov “ google for govt” bill Fedspending.org Narrower subset of USAspending Recovery.gov
- 76. Citizen Participation personaldemocracy.org “ a hub for the conversation already underway between political practitioners and technologists” kitchendemocracy.org Discuss and vote on local issues
- 77. The Role of NSF / CISE
- 78. Main Questions Should this effect the research questions that proposers address? If so, what should they be? How do we study this phenomenon? Should this fundamentally change how research / science is conducted? Should this fundamentally change how research is funded?
- 79. Research Questions How to support constructive online debate? How to make citizen input scale? How to divide up tasks to allow for large-scale user participation? Which incentives work for which kinds of groups? What to do about destructive uses? What about those who are too shy to participate? Can any contributions be anonymous? Slander / correcting false information.
- 80. Research Example: What Makes Hit Songs? Answer: It’s strongly affected by what other people think! NSF grant: “ Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market”, Salganik, Dodds, Watts
- 81. Research on Collaborative X Collaborative Search Collaborative Visualization
- 82. Collaborative Visualization Sense.us (Heer): collaborative analysis around viz Jeff Heer
- 83. Social Visualization Manyeyes (Wattenberg & Viegas) shared data, shared viz, but individual work
- 84. NSF HSD Program: A Good Start Human and Social Dynamics Agents of Change (AOC) Dynamics of Human Behavior (DHB) Decision Making, Risk, and Uncertainty (DRU ) About 14 IIS awards Collaborative Research: IT-Enhanced Market Design and Experiments Transformed Social Interaction in Virtual Environments Scalable Computational Analysis of the Diffusion of Technological Concepts Investigating the Dynamics of Free/Libre Open Source Software Development Teams
- 85. How to Study This? Make your own social network: Garcia-Molina’s CourseRank
- 87. Thank you! Marti Hearst ischool.berkeley.edu/~hearst