Ed	
  H.	
  Chi,	
  Principal	
  Scientist	
  and	
  Area	
  Manager	
  

                Augmented	
  Social	
  Cognition...
    Cognition:	
  the	
  ability	
  to	
  remember,	
  think,	
  and	
  reason;	
  the	
  faculty	
  of	
  
     knowing....
Characteriza*on	
                Models	
  




                         Evalua*ons	
                 Prototypes	
  



 ...
    All	
  models	
  are	
  wrong!	
  
      –  Some	
  are	
  more	
  wrong	
  than	
  others!	
  
    So	
  what	
  ar...
    For	
  example,	
  for	
  information	
  diffusion,	
  it’s	
  theory	
  of	
  
     influentials	
  [Gladwell,	
  etc....
From: Sun et al, ICWSM2009




             Hypertext 2010 Keynote at MSM
2010-06-13              Workshop                ...
    Descriptive:	
  clarify	
  terms,	
  key	
  concepts	
  
    Explanatory:	
  reveal	
  relationships	
  and	
  proce...
    A	
  tough	
  task	
  to	
  identify	
  models	
  from	
  the	
  
     literature,	
  since	
  it	
  is	
  so	
  spre...
Hypertext 2010 Keynote at MSM
2010-06-13              Workshop             9
Number of Articles (Log Scale)




http://en.wikipedia.org/wiki/Wikipedia:Modelling_Wikipedia’s_growth
Monthly Edits
Monthly Edits
*In thousands   Monthly Active Editors
Monthly Edits by Editor Class (in thousands)
Monthly Ratio of Reverted Edits
Hypertext 2010 Keynote at MSM
2010-06-13              Workshop             18
     Preferential	
  Attachment:	
  Edits	
  beget	
  edits	
  
           –  more	
  number	
  of	
  previous	
  edits,	...
    Ecological	
  population	
  growth	
  model	
  
          –  Also	
  depend	
  on	
  environmental	
  conditions	
  
...
    Follows	
  a	
  logistic	
  growth	
  curve	
  


                                               New Article
    Carrying	
  Capacity	
  as	
  a	
  function	
  of	
  time.	
  
    Biological	
  system	
  
      –  Competition	
  increases	
  as	
  
         population	
  hit	
  the	
  limits	
  o...
    r-­‐Strategist	
  
      –  Growth	
  or	
  exploitation	
  
                                                        ...
Hypertext 2010 Keynote at MSM
2010-06-13              Workshop             25
Social Tagging Creates Noise



                                              •  Synonyms
                                ...
Encoding	
                                             Retrieval
                                                         ...
Concepts	
                                                            Topics	
  




Users	
                              ...
Hypertext 2010 Keynote at MSM
2010-06-13              Workshop             29
Hypertext 2010 Keynote at MSM
2010-06-13              Workshop             30
Source: Hypertext 2008 study on del.icio.us (Chi & Mytkowicz)
                       Hypertext 2010 Keynote at MSM
2010-06...
Hypertext 2010 Keynote at MSM
2010-06-13              Workshop             32
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)



                                   P(Tag|...
Database                                        Lucene
• Delicious                                      • P(URL|Tag)      ...
Hypertext 2010 Keynote at MSM
2010-06-13              Workshop             37
Hypertext 2010 Keynote at MSM
2010-06-13              Workshop             38
Hypertext 2010 Keynote at MSM
2010-06-13              Workshop             39
Dellarocas, MIT Sloan Management Review




                    Hypertext 2010 Keynote at MSM
2010-06-13                  ...
(1)	
  Generate	
  new	
  tools	
  and	
  systems,	
  new	
  techniques	
  
(2)	
  Generate	
  data	
  that	
  looks	
  li...
Poor heuristic




                            Good heuristic




              Hypertext 2010 Keynote at MSM
2010-06-13  ...
Solo




                 Cooperative (“good hints”)




             Hypertext 2010 Keynote at MSM
2010-06-13            ...
    Appropriate	
  for	
  
     the	
  occasion	
  




                              Hypertext 2010 Keynote at MSM
2010-...
externally-motivated       self-motivated          framing
                                                               ...
framing



Before Search
                   externally-motivated       self-motivated
                   searchers        ...
externally-motivated       self-motivated         framing
                                                                ...
externally-motivated       self-motivated         framing
                                                                ...
externally-motivated       self-motivated           framing
                                                              ...
externally-motivated     self-motivated        framing
                                                                  t...
Hypertext 2010 Keynote at MSM
2010-06-13              Workshop             51
     Research	
  Vision:	
  Understand	
  how	
  social	
  computing	
  
                       systems	
  can	
  enhance...
Model-Driven Research in Social Computing
Model-Driven Research in Social Computing
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Model-Driven Research in Social Computing

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2010 June 13
Keynote talk given at the
Workshop for Modeling Social Media
ACM Hypertext 2010 Conference

Presenter: Ed H. Chi

Talk Title:
Model-driven Research for Augmenting Social Cognition

Short Abstract:
Model-driven research seeks to predict and to explain the phenomena in systems. The drive to do this for social computing research should further our understanding of how these systems evolve and develop. I will illustrate how we have modeled the dynamics in the popular social bookmarking system, Delicious, using Information Theory. I will also show how using equations from Evolutionary Dynamics we were better able to explain what might be happening to Wikipedia's contribution patterns.

Published in: Technology, Education

Model-Driven Research in Social Computing

  1. Ed  H.  Chi,  Principal  Scientist  and  Area  Manager   Augmented  Social  Cognition  Area   Palo  Alto  Research  Center   Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 1 Image from: http://www.flickr.com/photos/ourcommon/480538715/
  2.   Cognition:  the  ability  to  remember,  think,  and  reason;  the  faculty  of   knowing.     Social  Cognition:  the  ability  of  a  group  to  remember,  think,  and   reason;  the  construction  of  knowledge  structures  by  a  group.   –  (not  quite  the  same  as  in  the  branch  of  psychology  that  studies  the   cognitive  processes  involved  in  social  interaction,  though  included)     Augmented  Social  Cognition:  Supported  by  systems,  the   enhancement    of  the  ability  of  a  group  to  remember,  think,  and   reason;  the  system-­‐supported  construction  of  knowledge   structures  by  a  group.     Citation:  Chi,  IEEE  Computer,  Sept  2008   Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 2
  3. Characteriza*on   Models   Evalua*ons   Prototypes     Characterize  activity  on  social  systems  with  analytics     Model  interaction  social  and  community  dynamics  and  variables     Prototype  tools  to  increase  benefits  or  reduce  cost     Evaluate  prototypes  via  Living  Laboratories  with  real  users   Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 3
  4.   All  models  are  wrong!   –  Some  are  more  wrong  than  others!     So  what  are  theories  and  models  good  for?     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   Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 4
  5.   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 Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 5
  6. From: Sun et al, ICWSM2009 Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 6
  7.   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   7
  8.   A  tough  task  to  identify  models  from  the   literature,  since  it  is  so  spread  out  in  various   publications     Just  a  few  examples  from  our  group.   UIST 2004 8
  9. Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 9
  10. Number of Articles (Log Scale) http://en.wikipedia.org/wiki/Wikipedia:Modelling_Wikipedia’s_growth
  11. Monthly Edits
  12. Monthly Edits
  13. *In thousands Monthly Active Editors
  14. Monthly Edits by Editor Class (in thousands)
  15. Monthly Ratio of Reverted Edits
  16. Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 18
  17.   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 €
  18.   Ecological  population  growth  model   –  Also  depend  on  environmental  conditions   –  K,  carrying  capacity  (due  to  resource  limitation)   dN N = rN(1− ) dt K €
  19.   Follows  a  logistic  growth  curve   New Article
  20.   Carrying  Capacity  as  a  function  of  time.  
  21.   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    
  22.   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   €
  23. Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 25
  24. Social Tagging Creates Noise •  Synonyms •  Misspellings •  Morphologies People use different tag words to express similar concepts. Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 26
  25. Encoding   Retrieval   “video    people    talks  technology”     h:p://www.ted.com/index.php/speakers   h:p://edge.org   “science    research  cogni*on”   Hypertext 2010 Keynote at MSM 27   2010-06-13 Workshop 27
  26. Concepts   Topics   Users   Documents   Noise   Tags   Decoding   Encoding   T1…Tn   Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 28
  27. Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 29
  28. Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 30
  29. Source: Hypertext 2008 study on del.icio.us (Chi & Mytkowicz) Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 31
  30. Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 32
  31. 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.     Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 33
  32. Semantic Similarity Graph Web Tools Reference Guide Howto Tutorial Tips Help Tip Tutorials Tricks Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 34
  33. Tags URLs P(URL|Tag) P(Tag|URL)   Spreading  Activation  in  a  bi-­‐graph     Computation  over  a  very  large  data  set   –  150  Million+  bookmarks   Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 35
  34. 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     Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 36
  35. Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 37
  36. Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 38
  37. Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 39
  38. Dellarocas, MIT Sloan Management Review Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 40
  39. (1)  Generate  new  tools  and  systems,  new  techniques   (2)  Generate  data  that  looks  like  real  behavioral  data   Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 41
  40. Poor heuristic Good heuristic Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 42
  41. Solo Cooperative (“good hints”) Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 43
  42.   Appropriate  for   the  occasion   Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 44
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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 search step A 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
  48. 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
  49. Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 51
  50.   Research  Vision:  Understand  how  social  computing   systems  can  enhance  the  ability  of  a  group  of   people  to  remember,  think,  and  reason.     Living  Laboratory:  Create  applications  that  harness   collective  intelligence  to  improve  knowledge   capture,  transfer,  and  discovery.   http://asc-­‐parc.blogspot.com   http://www.edchi.net   echi@parc.com   Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 52 Image from: http://www.flickr.com/photos/ourcommon/480538715/

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