Model-Driven Research in Social Computing

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/

  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

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

  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

  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

From: Sun et al, ICWSM2009 Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 6

  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

  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

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

*In thousands Monthly Active Editors

Monthly Edits by Editor Class (in thousands)

Monthly Ratio of Reverted Edits

  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 €

  Ecological population growth model –  Also depend on environmental conditions –  K, carrying capacity (due to resource limitation) dN N = rN(1− ) dt K €

  Follows a logistic growth curve New Article

  Carrying Capacity as a function of time.

  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

  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 €

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

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

Concepts Topics Users Documents Noise Tags Decoding Encoding T1…Tn Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 28

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

Semantic Similarity Graph Web Tools Reference Guide Howto Tutorial Tips Help Tip Tutorials Tricks Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 34

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

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

Dellarocas, MIT Sloan Management Review Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 40

(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

Poor heuristic Good heuristic Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 42

Solo Cooperative (“good hints”) Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 43

  Appropriate for the occasion Hypertext 2010 Keynote at MSM 2010-06-13 Workshop 44

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

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

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

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

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

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

  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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Model-Driven Research in Social Computing

by Ed Chi

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