Ed	
  H.	
  Chi	
  
                Principal	
  Scientist	
  and	
  Area	
  Manager	
  

                Augmented	
  Soc...
    Early	
  fundamental	
  contributions	
  from:	
  
      –  Computer	
  scientists	
  interested	
  in	
  changing	
 ...
2010-09-13   Mensch und Computer 2010 Keynote   3
      Problem:	
  	
  
                          –  Intellectual	
  over-­‐specialization	
  
                          ...
Graphical User Interface
chartered	
  to	
  create	
  the	
  architecture	
  of	
                         Laser Printing
i...
    Fitts’	
  Law	
  
    Models	
  of	
  Human	
  Memory	
  
    Models	
  of	
  Human	
  Attention	
  
    Interrupt...
    We	
  know	
  motion	
  in	
  the	
  periphery	
  is	
  more	
  noticeable	
  
     than	
  in	
  the	
  foveal	
  re...
    We	
  know	
  that	
  people	
  
     can	
  Block	
  out	
  the	
  
     irrelevant	
  content	
  
     quite	
  eas...
Characteriza*on	
                Models	
  




                         Evalua*ons	
                 Prototypes	
  



 ...
Start with Capturing User Traces




2010-09-13         Mensch und Computer 2010 Keynote   10
    Scan	
  
    Skim	
  
    Decide	
  
    Action	
  




2010-09-13        Mensch und Computer 2010 Keynote   11
Characteriza*on	
                Models	
  




                         Evalua*ons	
                 Prototypes	
  



 ...
    human-­‐information	
  interaction	
  is	
  adaptive	
  to	
  the	
  extent:	
  

          MAXIMIZE
                ...
Scent Values:
Start users at    Probabilities of
  page with         Transition                        Examine user patter...
Characteriza*on	
                Models	
  




                         Evalua*ons	
                 Prototypes	
  



 ...
       A	
  store	
  that	
  knows	
  your	
  goal.	
  
            Over	
  50%	
  reduction	
  in	
  task	
  time.	
  
...
       Identify	
  tasty	
  pages	
  
       Waft	
  scent	
  backward	
  along	
  links	
  
        –      Loses	
  int...
Partial information goal:                                62 copies/min.
 “remote diagnostic
  technology”



  Remainder o...
Associated Entries
                                                underlined in red



2010-09-13   Mensch und Computer 2...
Conceptually highlight any relevant
User first type search keywords:                       passages and keywords	

     “an...
Characteriza*on	
                Models	
  




                         Evalua*ons	
                 Prototypes	
  



 ...
(times capped at five minutes)


                       10/12 subjects preferred ScentTrails



2010-09-13                ...
2005-10-21 UMN talk
2005-10-21 UMN talk
    Descriptive:	
  clarify	
  terms,	
  key	
  concepts	
  
    Explanatory:	
  reveal	
  relationships	
  and	
  proce...
Bongwon	
  Suh,	
  Gregorio	
  Convertino,	
  Ed	
  H.	
  Chi,	
  Peter	
  
Pirolli.	
  The	
  Singularity	
  is	
  Not	
 ...
Number of Articles (Log Scale)




             http://en.wikipedia.org/wiki/Wikipedia:Modelling_Wikipedia’s_growth

2010-...
Monthly Edits




2010-09-13     Mensch und Computer 2010 Keynote   28
Monthly Edits




2010-09-13     Mensch und Computer 2010 Keynote   29
*In thousands       Monthly Active Editors




       2010-09-13        Mensch und Computer 2010 Keynote   30
*In thousands       Monthly Active Editors




       2010-09-13        Mensch und Computer 2010 Keynote   31
2010-09-13   Mensch und Computer 2010 Keynote   32
Monthly Ratio of Reverted Edits




2010-09-13               Mensch und Computer 2010 Keynote   33
2010-09-13   Mensch und Computer 2010 Keynote   34
     Preferential	
  Attachment:	
  Edits	
  beget	
  edits	
  
           –  more	
  number	
  of	
  previous	
  edits,	...
    Biological	
  system	
  
      –  Competition	
  increases	
  as	
  
         population	
  hit	
  the	
  limits	
  o...
     r-­‐Strategist	
  
       –  Growth	
  or	
  exploitation	
  
                                                      ...
     Ecological	
  population	
  growth	
  model	
  
           –  Also	
  depend	
  on	
  environmental	
  conditions	
 ...
    Follows	
  a	
  logistic	
  growth	
  curve	
  


                                                New Article




201...
    Carrying	
  Capacity	
  as	
  a	
  function	
  of	
  time.	
  




2010-09-13                       Mensch und Comput...
2010-09-13   Mensch und Computer 2010 Keynote   41
Concepts	
                                                               Topics	
  




Users	
                           ...
2010-09-13   Mensch und Computer 2010 Keynote   43
2010-09-13   Mensch und Computer 2010 Keynote   44
Source: Hypertext 2008 study on del.icio.us (Chi & Mytkowicz)

2010-09-13             Mensch und Computer 2010 Keynote    ...
2010-09-13   Mensch und Computer 2010 Keynote   46
Joint	
  work	
  with	
  	
  
Rowan	
  Nairn,	
  Lawrence	
  Lee	
  

Kammerer,	
  Y.,	
  Nairn,	
  R.,	
  Pirolli,	
  P.,...
Semantic Similarity Graph
                  Web
   Tools
                            Reference

                  Guide
 H...
Tags                       URLs


                                       P(URL|Tag)



                                   ...
2010-09-13   Mensch und Computer 2010 Keynote   50
2010-09-13   Mensch und Computer 2010 Keynote   51
2010-09-13   Mensch und Computer 2010 Keynote   52
Dellarocas,	
  MIT	
  Sloan	
  Management	
  Review	
  


2010-09-13   Mensch und Computer 2010 Keynote                   ...
(1)	
  Generate	
  new	
  tools	
  and	
  systems,	
  new	
  techniques	
  
(2)	
  Generate	
  data	
  that	
  looks	
  li...
externally-motivated       self-motivated          framing
                                                               ...
externally-motivated       self-motivated           framing
                                                              ...
externally-motivated     self-motivated        framing
                                                                  t...
    All	
  models	
  are	
  wrong!	
  
      –  Some	
  are	
  more	
  wrong	
  than	
  others!	
  
    So	
  what	
  ar...
2010-09-13   Mensch und Computer 2010 Keynote   59
Word connectivity
     Human Movement Study: Fitts’ law

     MT = a + b Log2(Dsi/Wi + 1)




    18000



               ...
Between	
  just	
  getting	
  things	
  done	
  	
  
vs.	
  finding	
  out	
  the	
  science	
  




2010-09-13            ...
A                                 B
Bucket Testing or A/B Testing [Kohavi et al]
Characteriza*on	
                        Models	
  


     Evalua*ons
              	
                      Prototypes
   ...
2010-09-13   Mensch und Computer 2010 Keynote   64
    Research	
  Vision:	
  Understand	
  how	
  social	
  computing	
  
     systems	
  can	
  enhance	
  the	
  ability	...
2010-09-13   Mensch und Computer 2010 Keynote   66
    Appropriate	
  for	
  
     the	
  occasion	
  




2010-09-13                    Mensch und Computer 2010 Keynote   ...
Poor heuristic




                              Good heuristic




2010-09-13    Mensch und Computer 2010 Keynote   68
Solo




                 Cooperative (“good hints”)




2010-09-13   Mensch und Computer 2010 Keynote   69
Social Tagging Creates Noise



                                                 •  Synonyms
                             ...
Database                                         Lucene
• Delicious                                     • P(URL|Tag)      ...
framing



Before Search
                   externally-motivated       self-motivated
                   searchers        ...
externally-motivated       self-motivated         framing
                                                                ...
externally-motivated       self-motivated         framing
                                                                ...
    For	
  example,	
  for	
  information	
  diffusion,	
  it’s	
  theory	
  of	
  
     influentials	
  [Gladwell,	
  etc....
From: Sun et al, ICWSM2009




2010-09-13   Mensch und Computer 2010 Keynote                     76
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Model-based Research in Human-Computer Interaction (HCI): Keynote at Mensch und Computer 2010

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Model-based Research in Human-Computer Interaction (HCI): Keynote at Mensch und Computer 2010

  1. 1. Ed  H.  Chi   Principal  Scientist  and  Area  Manager   Augmented  Social  Cognition  Area   Palo  Alto  Research  Center   @edchi   echi@parc.com   2010-09-13 Mensch und Computer 2010 Keynote 1 Image from: http://www.flickr.com/photos/ourcommon/480538715/
  2. 2.   Early  fundamental  contributions  from:   –  Computer  scientists  interested  in  changing   how  we  interact  with  information   –  Psychologists  interested  in  the  implications   of  these  changes     The  need  to  establish  HCI  as  a  science   –  Adopt  methods  from  psychology   –  Dual  purpose:  understand  nature  of  human   behavior  and  build  up  a  science  of  HCI   techniques.   9/13/10 HCIC "Living Lab" 2
  3. 3. 2010-09-13 Mensch und Computer 2010 Keynote 3
  4. 4.   Problem:     –  Intellectual  over-­‐specialization     The  Memex     Extend  the  powers  of  the  human  mind   with  technology   –  Individuals  could  attend  to  greater  spans   –  Facile  command  of  all  recorded  knowledge   –  Sharing  of  knowledge  gained   2010-09-13 Mensch und Computer 2010 Keynote 4
  5. 5. Graphical User Interface chartered  to  create  the  architecture  of   Laser Printing information  &  the  office  of  the  future   Ethernet  invented  distributed  personal  computing   -­‐  Bit-mapped Displays  established  Xerox’s  laser  printing  business     -­‐  Distributed File Systems Page Description Languages  created  the  foundation  for  the  digital  revolution   -­‐  First Commercial Mouse Object-oriented Programming WYSIWYG Editing Distributed Computing VLSI Design Methodologies Optical Storage Client/Server Architecture Device Independent Imaging Cedar Programming Language 2010-09-13 Mensch und Computer 2010 Keynote 5
  6. 6.   Fitts’  Law     Models  of  Human  Memory     Models  of  Human  Attention     Interruptability     Cognitive  and  Behavorial  Modeling     Perception  and  Navigation     …   2010-09-13 Mensch und Computer 2010 Keynote 6
  7. 7.   We  know  motion  in  the  periphery  is  more  noticeable   than  in  the  foveal  region  [DaVinci].       Now  think  about  research  and  products  that  involve   animations  or  flashing  icons.   2010-09-13 Mensch und Computer 2010 Keynote 7
  8. 8.   We  know  that  people   can  Block  out  the   irrelevant  content   quite  easily     Until  it’s  semantically   meaningful  or   important  to  you   Hey, Jurgen! UIST 2004 8
  9. 9. Characteriza*on   Models   Evalua*ons   Prototypes     Characterize  activity  with  experiments,  ethnography,  log  analysis     Model  interaction  dynamics  and  interface  variations     Prototype  tools  to  increase  benefits  or  reduce  cost     Evaluate  prototypes  with  users   2010-09-13 Mensch und Computer 2010 Keynote 9
  10. 10. Start with Capturing User Traces 2010-09-13 Mensch und Computer 2010 Keynote 10
  11. 11.   Scan     Skim     Decide     Action   2010-09-13 Mensch und Computer 2010 Keynote 11
  12. 12. Characteriza*on   Models   Evalua*ons   Prototypes     Characterize  activity  with  experiments,  ethnography,  log  analysis     Model  interaction  dynamics  and  interface  variations     Prototype  tools  to  increase  benefits  or  reduce  cost     Evaluate  prototypes  with  users   2010-09-13 Mensch und Computer 2010 Keynote 12
  13. 13.   human-­‐information  interaction  is  adaptive  to  the  extent:   MAXIMIZE [ Net Knowledge Gained Costs of Interaction ] 2010-09-13 Mensch und Computer 2010 Keynote 13
  14. 14. Scent Values: Start users at Probabilities of page with Transition Examine user patterns some goal Flow users through the network 2010-09-13 Mensch und Computer 2010 Keynote 14
  15. 15. Characteriza*on   Models   Evalua*ons   Prototypes     Characterize  activity  with  experiments,  ethnography,  log  analysis     Model  interaction  dynamics  and  interface  variations     Prototype  tools  to  increase  benefits  or  reduce  cost     Evaluate  prototypes  with  users   2010-09-13 Mensch und Computer 2010 Keynote 15
  16. 16.   A  store  that  knows  your  goal.     Over  50%  reduction  in  task  time.   2010-09-13 Mensch und Computer 2010 Keynote 16
  17. 17.   Identify  tasty  pages     Waft  scent  backward  along  links   –  Loses  intensity  as  it  travels   XC4411 copier Features: XC4411 features digital copiers XC5001 remote diagnostics color copiers copiers ... back fax machines other maintenance remote diagnostics ... 2010-09-13 Mensch und Computer 2010 Keynote 17
  18. 18. Partial information goal: 62 copies/min. “remote diagnostic technology” Remainder of information goal: 92 copies/min. “speed >= 75” 2010-09-13 Mensch und Computer 2010 Keynote 18
  19. 19. Associated Entries underlined in red 2010-09-13 Mensch und Computer 2010 Keynote 19
  20. 20. Conceptually highlight any relevant User first type search keywords: passages and keywords “anthrax symptoms” Draw user attention 2010-09-13 Mensch und Computer 2010 Keynote 20
  21. 21. Characteriza*on   Models   Evalua*ons   Prototypes     Characterize  activity  with  experiments,  ethnography,  log  analysis     Model  interaction  dynamics  and  interface  variations     Prototype  tools  to  increase  benefits  or  reduce  cost     Evaluate  prototypes  with  users   2010-09-13 Mensch und Computer 2010 Keynote 21
  22. 22. (times capped at five minutes) 10/12 subjects preferred ScentTrails 2010-09-13 Mensch und Computer 2010 Keynote 22
  23. 23. 2005-10-21 UMN talk
  24. 24. 2005-10-21 UMN talk
  25. 25.   Descriptive:  clarify  terms,  key  concepts     Explanatory:  reveal  relationships  and  processes     Predictive:  about  performance  and  situations     Prescriptive:  convey  guidance  for  decision   making  in  design  by  recording  best  practice     Generative:  enable  practitioners  to  create,   invent  or  discover  something  new   2010-09-13 Mensch und Computer 2010 Keynote 25
  26. 26. Bongwon  Suh,  Gregorio  Convertino,  Ed  H.  Chi,  Peter   Pirolli.  The  Singularity  is  Not  Near:  Slowing  Growth  of   Wikipedia.  In  Proc.  of  WikiSym  2009.  Oct,  2009.  Florida,   USA   2010-09-13 Mensch und Computer 2010 Keynote 26
  27. 27. Number of Articles (Log Scale) http://en.wikipedia.org/wiki/Wikipedia:Modelling_Wikipedia’s_growth 2010-09-13 Mensch und Computer 2010 Keynote 27
  28. 28. Monthly Edits 2010-09-13 Mensch und Computer 2010 Keynote 28
  29. 29. Monthly Edits 2010-09-13 Mensch und Computer 2010 Keynote 29
  30. 30. *In thousands Monthly Active Editors 2010-09-13 Mensch und Computer 2010 Keynote 30
  31. 31. *In thousands Monthly Active Editors 2010-09-13 Mensch und Computer 2010 Keynote 31
  32. 32. 2010-09-13 Mensch und Computer 2010 Keynote 32
  33. 33. Monthly Ratio of Reverted Edits 2010-09-13 Mensch und Computer 2010 Keynote 33
  34. 34. 2010-09-13 Mensch und Computer 2010 Keynote 34
  35. 35.   Preferential  Attachment:  Edits  beget  edits   –  more  number  of  previous  edits,  more  number  of  new  edits   Growth rate depends on: N = current population r = growth rate of the population N(t) = N 0 ⋅ e rt dN = r⋅ N dt Growth rate Current of population € population € 2010-09-13 Mensch und Computer 2010 Keynote 35
  36. 36.   Biological  system   –  Competition  increases  as   population  hit  the  limits  of  the   ecology   –  Advantage  go  to  members  of  the   population  that  have  competitive   dominance  over  others     Analogy   –  Limited  opportunities  to  make   novel  contributions   –  Increased  patterns  of  conflict  and   dominance     2010-09-13 Mensch und Computer 2010 Keynote 36
  37. 37.   r-­‐Strategist   –  Growth  or  exploitation   dN N –  Less-­‐crowded  niches  /  produce  many   = rN(1− ) offspring   dt K   K-­‐Strategist   –  Conservation   [Gunderson & Holling 2001] –  Strong  competitors  in  crowded  niches  /   invest  more  heavily  in  fewer  offspring   € 2010-09-13 Mensch und Computer 2010 Keynote 37
  38. 38.   Ecological  population  growth  model   –  Also  depend  on  environmental  conditions   –  K,  carrying  capacity  (due  to  resource  limitation)   dN N = rN(1− ) dt K € 2010-09-13 Mensch und Computer 2010 Keynote 38
  39. 39.   Follows  a  logistic  growth  curve   New Article 2010-09-13 Mensch und Computer 2010 Keynote 39
  40. 40.   Carrying  Capacity  as  a  function  of  time.   2010-09-13 Mensch und Computer 2010 Keynote 40
  41. 41. 2010-09-13 Mensch und Computer 2010 Keynote 41
  42. 42. Concepts   Topics   Users   Documents   Noise   Tags   Decoding   Encoding   T1…Tn   2010-09-13 Mensch und Computer 2010 Keynote 42
  43. 43. 2010-09-13 Mensch und Computer 2010 Keynote 43
  44. 44. 2010-09-13 Mensch und Computer 2010 Keynote 44
  45. 45. Source: Hypertext 2008 study on del.icio.us (Chi & Mytkowicz) 2010-09-13 Mensch und Computer 2010 Keynote 45
  46. 46. 2010-09-13 Mensch und Computer 2010 Keynote 46
  47. 47. Joint  work  with     Rowan  Nairn,  Lawrence  Lee   Kammerer,  Y.,  Nairn,  R.,  Pirolli,  P.,  and  Chi,  E.  H.  2009.  Signpost  from  the   masses:  learning  effects  in  an  exploratory  social  tag  search  browser.  In   Proceedings  of  the  27th  international  Conference  on  Human  Factors  in   Computing  Systems  (Boston,  MA,  USA,  April  04  -­‐  09,  2009).  CHI  '09.  ACM,  New   York,  NY,  625-­‐634.     2010-09-13 Mensch und Computer 2010 Keynote 47
  48. 48. Semantic Similarity Graph Web Tools Reference Guide Howto Tutorial Tips Help Tip Tutorials Tricks 2010-09-13 Mensch und Computer 2010 Keynote 48
  49. 49. Tags URLs P(URL|Tag) P(Tag|URL)   Spreading  Activation  in  a  bi-­‐graph     Computation  over  a  very  large  data  set   –  150  Million+  bookmarks   2010-09-13 Mensch und Computer 2010 Keynote 49
  50. 50. 2010-09-13 Mensch und Computer 2010 Keynote 50
  51. 51. 2010-09-13 Mensch und Computer 2010 Keynote 51
  52. 52. 2010-09-13 Mensch und Computer 2010 Keynote 52
  53. 53. Dellarocas,  MIT  Sloan  Management  Review   2010-09-13 Mensch und Computer 2010 Keynote 53
  54. 54. (1)  Generate  new  tools  and  systems,  new  techniques   (2)  Generate  data  that  looks  like  real  behavioral  data   2010-09-13 Mensch und Computer 2010 Keynote 54
  55. 55. externally-motivated self-motivated framing the context Before Search searchers searchers 31% 69% Social Interactions GATHER REQUIREMENTS refining the requirements FORMULATE REPRESENTATION 28% 13% 59% During Search navigational transactional informational FORAGING step A step A search process step B step B “evidence file” TRANSACTION SENSEMAKING search product /end product After Search 28% 72% DO NOTHING TAKE ACTION ORGANIZE DISTRIBUTE to self 15% to proximate 87% to public 2% others others
  56. 56. externally-motivated self-motivated framing the context Before Search searchers searchers 31% 69% 43% users engaged in pre-search social Social Interactions interactions. GATHER REQUIREMENTS refining the reasons for interacting: to get advice, guidelines, feedback, FORMULATE REPRESENTATION requirements or search tips 28% 13% 59% During Search navigational transactional informational FORAGING step A step A search 3 types of search: informational search provides a 150 reports of unique search experiences compelling caseBfor social search support. mapped to a canonical model of social search. step B step process “evidence file” TRANSACTION SENSEMAKING search product /end product After Search 28% 72% DO NOTHING TAKE ACTION 59% users engaged in post-search sharing. ORGANIZE DISTRIBUTE reasons for interacting: thought others might be interested, to get feedback, out of obligation to self 15% to proximate 87% to public 2% others others
  57. 57. externally-motivated self-motivated framing the context Before Search searchers searchers •  instant 31% messaging69% to personal social (IM) Social Interactions connections near the search box refining GATHER REQUIREMENTS the requirements FORMULATE REPRESENTATION 28% 13% 59% During Search navigational transactional informational •  step A clouds from domain FORAGING tag step A experts search •  step B users’ search trails process feedback) other (for step B •  related search terms (for feedback) Similar to: Glance; Smyth" “evidence file” TRANSACTION SENSEMAKING search product /end product After Search 28% 72% DO NOTHING TAKE ACTION •  sharing tools built-in to (search) site Spartag.us" •  collective tag clouds (for feedback) ORGANIZE DISTRIBUTE Mr. Taggy" to self 15% to proximate 87% to public 2% others others
  58. 58.   All  models  are  wrong!   –  Some  are  more  wrong  than  others!     So  what  are  theories  and  models  good  for?     They’re  a  summary  of  what  we  think  is  happening   –  Ways  to  describe  and  explain  what  we  have  learned   –  Predicts  user  and  group  behavior   –  Helps  generate  new  novel  tools  and  systems   2010-09-13 Mensch und Computer 2010 Keynote 58
  59. 59. 2010-09-13 Mensch und Computer 2010 Keynote 59
  60. 60. Word connectivity Human Movement Study: Fitts’ law MT = a + b Log2(Dsi/Wi + 1) 18000 English Letter Corpus 16000 14000 12000 10000 (News, chat etc) 8000 6000 4000 [Zhai et al., 2000, 2002] 2000 0 sp E T A H O N S R I D L U W M C G Y F B P K V J X Q Z Slide adopted from Mary Czerwinski Keynote UIST 2004 “Fitts-digraph energy” 27 27 Pij ⎡ ⎛ Dij ⎞ ⎤ t = ∑ ∑ ⎢ Log2 ⎜ +1⎟ ⎥ W ( A →B) = e −ΔE kT if ΔE >0 i=1 j =1 IP ⎣ ⎝ Wi ⎠ ⎦ =1 if ΔE ≤ 0 Metropolis “random walk” optimization Alphabetical tuning UIST 2004 60 € €
  61. 61. Between  just  getting  things  done     vs.  finding  out  the  science   2010-09-13 Mensch und Computer 2010 Keynote 61
  62. 62. A B Bucket Testing or A/B Testing [Kohavi et al]
  63. 63. Characteriza*on   Models   Evalua*ons   Prototypes   Evalua*ons   Prototypes     Design,  Prototype,  Learn;       If  you  can,  you  should  codify  your   findings  so  that  others  can     Then  Re-­‐design,  Prototype,  Learn   replicate  it,  learn  from  it,  predict     Sometimes  that’s  all  you  can  do.   behavior  from  it.     The  basis  of  a  true  scientific  field   2010-09-13 Mensch und Computer 2010 Keynote 63
  64. 64. 2010-09-13 Mensch und Computer 2010 Keynote 64
  65. 65.   Research  Vision:  Understand  how  social  computing   systems  can  enhance  the  ability  of  a  group  of   people  to  remember,  think,  and  reason.   http://asc-­‐parc.blogspot.com   http://www.edchi.net   echi@parc.com   WikiDashboard.com   MrTaggy.com   Zerozero88.com  
  66. 66. 2010-09-13 Mensch und Computer 2010 Keynote 66
  67. 67.   Appropriate  for   the  occasion   2010-09-13 Mensch und Computer 2010 Keynote 67
  68. 68. Poor heuristic Good heuristic 2010-09-13 Mensch und Computer 2010 Keynote 68
  69. 69. Solo Cooperative (“good hints”) 2010-09-13 Mensch und Computer 2010 Keynote 69
  70. 70. Social Tagging Creates Noise •  Synonyms •  Misspellings •  Morphologies People use different tag words to express similar concepts. 2010-09-13 Mensch und Computer 2010 Keynote 70
  71. 71. Database Lucene • Delicious • P(URL|Tag) • Serve up search • Ma.gnolia • P(Tag|URL) results • Tuples of • Pre-computed • Other social cues bookmarks • Bayesian Network patterns in a fast • Well defined APIs • [User, URL, Tags, Inference index Time] Crawling MapReduce Web Server Web Server UI Search Frontend Results •  MapReduce:  months  of  computa*on  to  a  single  day   •  Development  of  novel  scoring  func*on     2010-09-13 Mensch und Computer 2010 Keynote 71
  72. 72. framing Before Search externally-motivated self-motivated searchers searchers the context 31% 69% Social Interactions GATHER REQUIREMENTS refining the requirements FORMULATE REPRESENTATION 28% 13% 59% During Search navigational transactional informational FORAGING step A step A search process step B step B “evidence file” TRANSACTION SENSEMAKING search product /end product After Search 28% 72% DO NOTHING TAKE ACTION ORGANIZE DISTRIBUTE to self 15% to proximate 87% to public 2% others others
  73. 73. externally-motivated self-motivated framing the context Before Search searchers searchers 31% 69% Social Interactions GATHER REQUIREMENTS refining the requirements FORMULATE REPRESENTATION 28% 13% 59% During Search navigational transactional informational FORAGING step A step A search process step B step B “evidence file” TRANSACTION SENSEMAKING search product /end product After Search 28% 72% DO NOTHING TAKE ACTION ORGANIZE DISTRIBUTE to self 15% to proximate 87% to public 2% others others
  74. 74. externally-motivated self-motivated framing the context Before Search searchers searchers 31% 69% Social Interactions GATHER REQUIREMENTS refining the requirements FORMULATE REPRESENTATION 28% 13% 59% During Search navigational transactional informational FORAGING step A step A search process step B step B “evidence file” TRANSACTION SENSEMAKING search product /end product 28% 72% After Search DO NOTHING TAKE ACTION ORGANIZE DISTRIBUTE to self 15% to proximate 87% to public 2% others others
  75. 75.   For  example,  for  information  diffusion,  it’s  theory  of   influentials  [Gladwell,  etc.]   –  reach  a  small  group  of  influential  people,  and  you’ll  reach   everyone  else   Figure From: Kleinberg, ICWSM2009 2010-09-13 Mensch und Computer 2010 Keynote 75
  76. 76. From: Sun et al, ICWSM2009 2010-09-13 Mensch und Computer 2010 Keynote 76

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