model-driven research in social computing

Image from: http://www.flickr.com/photos/ourcommon/480538715/

Ed H. Chi, Principal Scientist and Area Manager

Augmented Social Cognition Area Palo Alto Research Center

1 2010-06-13

Hypertext 2010 Keynote at MSM Workshop

  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

2 2010-06-13 Hypertext 2010 Keynote at MSM

Workshop

  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

3 Hypertext 2010 Keynote at MSM

Workshop 2010-06-13 3

Characteriza*on Models

Prototypes Evalua*ons

  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

2010-06-13 Hypertext 2010 Keynote at MSM

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


Workshop 5

Figure From: Kleinberg, ICWSM2009


Workshop 6

From: Sun et al, ICWSM2009

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

UIST 2004 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.


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


Workshop 18

  Preferential Attachment: Edits beget edits –  more number of previous edits, more number of new edits

€

N(t) = N0 ⋅ ert

€

dNdt

= r ⋅ N

Growth rate of population

Current population

Growth rate depends on: N = current population r = growth rate of the population

  Ecological population growth model –  Also depend on environmental conditions –  K, carrying capacity (due to resource limitation)

€

dNdt

= rN(1− NK)

  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 –  Less-‐crowded niches / produce many

offspring

  K-‐Strategist –  Conservation –  Strong competitors in crowded niches /

invest more heavily in fewer offspring €

dNdt

= rN(1− NK)

[Gunderson & Holling 2001]


Workshop 25

•  Synonyms •  Misspellings •  Morphologies

People use different tag words to express similar concepts.

Social Tagging Creates Noise

2010-06-13 26 Hypertext 2010 Keynote at MSM

Workshop

Encoding Retrieval

27

h:p://edge.org

“science research cogni*on”

h:p://www.ted.com/index.php/speakers

“video people talks technology”


Workshop

Topics Concepts

Users Documents

Tags

T1…Tn Encoding Decoding

Noise


Workshop


Workshop


Workshop 31

Source: Hypertext 2008 study on del.icio.us (Chi & Mytkowicz)


Workshop

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.


Workshop

2010-06-13 34

Guide

Web

Howto

Tips Help

Tools

Tip

Tricks

Tutorial

Tutorials

Reference

Semantic Similarity Graph

Hypertext 2010 Keynote at MSM Workshop

  Spreading Activation in a bi-‐graph   Computation over a very large data set

–  150 Million+ bookmarks

Tags URLs

P(URL|Tag)

P(Tag|URL)


Workshop

Web Server

Search Results

UI Frontend

• Delicious • Ma.gnolia • Other social cues

Crawling

• Tuples of bookmarks

• [User, URL, Tags, Time]

Database • P(URL|Tag) • P(Tag|URL) • Bayesian Network Inference

MapReduce

• Pre-computed patterns in a fast index

Lucene • Serve up search results

• Well defined APIs

Web Server

•  MapReduce: months of computa*on to a single day

•  Development of novel scoring func*on


Workshop


Workshop


Workshop 38


Workshop 39


Workshop 40

Dellarocas, MIT Sloan Management Review

(1) Generate new tools and systems, new techniques (2) Generate data that looks like real behavioral data


Workshop 41

Poor heuristic

Good heuristic


Workshop

Solo

Cooperative (“good hints”)


Workshop

  Appropriate for the occasion


Workshop 44

“evidence file”

SENSEMAKING

process

search FORAGING

Bef

ore

Sear

ch externally-motivated

searchers

31%

framing the context

refining the requirements

FORMULATE REPRESENTATION

GATHER REQUIREMENTS

69%

13% 59% 28%

transactional

self-motivated searchers

navigational informational

Dur

ing

Sear

ch

Aft

er S

earc

h

step A

step B

TRANSACTION

step A

step B

DO NOTHING

search product /end product

ORGANIZE DISTRIBUTE

TAKE ACTION

28% 72%

Social Interactions

to public others

to proximate others

to self 15% 87% 2%

“evidence file”

SENSEMAKING

process

search FORAGING

externally-motivated searchers

31%

framing the context



GATHER REQUIREMENTS

69%

13% 59% 28%

transactional



Dur

ing

Sear

ch

Aft

er S

earc

h

step A

step B

TRANSACTION

step A

step B

DO NOTHING


ORGANIZE DISTRIBUTE

TAKE ACTION

28% 72%

Social Interactions

to public others

to proximate others

to self 15% 87% 2%

Bef

ore

Sear

ch

“evidence file”

SENSEMAKING

process

search FORAGING

Bef

ore

Sear

ch externally-motivated

searchers

31%

framing the context



GATHER REQUIREMENTS

69%

13% 59% 28%

transactional



Dur

ing

Sear

ch

Aft

er S

earc

h

step A

step B

TRANSACTION

step A

step B

DO NOTHING


ORGANIZE DISTRIBUTE

TAKE ACTION

28% 72%

Social Interactions

to public others

to proximate others

to self 15% 87% 2%

“evidence file”

SENSEMAKING

process

search FORAGING


31%

framing the context



GATHER REQUIREMENTS

69%

13% 59% 28%

transactional



step A

step B

TRANSACTION

step A

step B

DO NOTHING


ORGANIZE DISTRIBUTE

TAKE ACTION

28% 72%

Social Interactions

to public others

to proximate others

to self 15% 87% 2%

43% users engaged in pre-search social interactions.

150 reports of unique search experiences mapped to a canonical model of social search.

59% users engaged in post-search sharing.

Bef

ore

Sear

ch

Dur

ing

Sear

ch

Aft

er S

earc

h

3 types of search: informational search provides a compelling case for social search support.

reasons for interacting: thought others might be interested, to get feedback, out of obligation

reasons for interacting: to get advice, guidelines, feedback, or search tips

“evidence file”

SENSEMAKING

process

search FORAGING


31%

framing the context



GATHER REQUIREMENTS

69%

13% 59% 28%

transactional



step A

step B

TRANSACTION

step A

step B

DO NOTHING


ORGANIZE DISTRIBUTE

TAKE ACTION

28% 72%

Social Interactions

to public others

to proximate others

to self 15% 87% 2%

•  instant messaging (IM) to personal social connections near the search box

Bef

ore

Sear

ch

Dur

ing

Sear

ch

Aft

er S

earc

h

•  tag clouds from domain experts •  other users’ search trails (for feedback) •  related search terms (for feedback)

Similar to: Glance; Smyth"

•  sharing tools built-in to (search) site •  collective tag clouds (for feedback)

Spartag.us"

Mr. Taggy"


Workshop 51

Image from: http://www.flickr.com/photos/ourcommon/480538715/

  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 [email protected]


Workshop

model-driven research in social computing

Technology