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Model-Driven Research in Social Computing

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2010 June 13 …

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.

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  • 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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