ICWSM Keynote: Duncan Watts – Using the Web to do Social Science

Duncan Watts knows about Social Networks.  Looks at the web as a tool for doing social science research.  Observing individual behavior, and interactions recorded in real time, for large populations.  Can the web lead to a social science revolution?

This talk will describe four studies, each motivated by a substansive science question, and illustrate use of new technology that would not have been practical a few years ago.

Dynamic Network Analysis

We typically look at static networks.  If we look at how networks change over time, we can ask different questions

  • What factors affect tie formation/termination?
  • How do network properties change?
  • How constrained are individual choices

Used an Email Data network in a university community: 14.5M emails over 9 months, 43K members

Found that Structure Drives Evolution — network proximity overwhelming determines new ties.  This type of data can be used to build empirical models of network formations.  Can also look at network stability of various properties over time.  However, found that individual rankings change.

Web-Based “Macro-Sociological” Experiment on Social Influence

Why are cultural “hits” (eg, Harry Potter, Titanic, Michael Jackson) so much more successful than average, yet so difficult to predict?  It may have to do with “Social Influence.”  If everyone is influencing each other, what effects does it have on the market overall?

You couldn’t really do this type of experiment (comparing individual and marketwide) studies in a lab before the web (physical constraints, can’t go back in time)

Created a web based “Cultural Market” Music Lab.  Subjects where shown a grid of 48 songs by unknown bands,. You can listen to a song, and decide to download it.  Subjects randomly assigned to 2 conditions: see download counts (social influence), or no download counts.  Also broke the study into 8 different ‘worlds’.

Found social influence at individual level: people are more likely to click on songs in the top 10.

Calculated properties at Collective Level.  Inequality of Success (Gini Coefficient), Unpredictability of Success (average difference in market share of songs across R realizations of the world).  Found that when people know what other people think, unpredictability goes up, inequality of success higher than control.  Also found that ‘best’ songs never do poorly, ‘worst’ songs never do great.

Broader Impacts: Relevant whenever people base decisions on observations of others.  Market

s do not simply reveal stable underlying preferences. Institutions based on “Representative Individual” models may need to be revised.

Network Survey on Facebook

Some evidence that Americans increasingly group themselves with like minded individuals, some contrary evidence.  One hypothesis is that people are not as similar to their friends as they think.  This type of study is really hard to put together, execute.  Facebook made it easier.

Created “Friend Sence” Application.  Application asks you what does your friend think about various questions.  Got 2500 respondents, 12,160 complete dyads.  This is a known biased population, BUT A traditional study would have cost 200-300K and 2 years.  On Facebook, it took 2-3k, a couple months.

Results: Friends are more similar than strangers, but not as similar as they think.  It turns out people are unaware of much of what their friends think.

How Do Financial Incentives Affect Performance

Assumption that performance-based pay should result in better work than fixed pay.  This study asks whether an employer can elicit better performance from a given wooker pool by paying them more.

This study looked at web-based peer production (wikipedia, Y! Answers, Digg).  Used Amazon Mechanical Turk for crowd sourcing labor.  Subjects accept a job, receive an up front fee.  They are sent to another site, where they are assigned a task, and will receive a bonus for doing well.  Randomly assigned to 3 pay levels (low, medium, high).

Results: Subjects do more work for more pay.  Also, do less work for harder tasks.  However, found that increasing pay did NOT improve accuracy.

Also, found that people always thought they were being underpaid (in post questions, people said they should be paid more).

Tentative suggestion: Payment levels should be dictated by recruitment and retention, not direct impact on quality. (lots of caveats on the results of this study).

Concludes that social network platforms offer social scientists new tools to study social interactions, collective dynamics.

Lots of exciting progress in “network science”

  • physics, computer sicence, sociology, economics
  • massive scale
  • network experiments
  • Large, observational studies

Fundamental advances will require new approaches.  Need to study lage populations of individuals, plus interactions and behavior over extended time.

Social Science 2.0: Should address macro-level phenomena from a bottom up understanding of micro-level social interactions.

  • scale of data, experimentation, platforms still ufeasible for lone researchers
  • shared data storage, experimental platforms, subject pools needed
  • human subject research on the web = privacy and consent issues

I really liked this discussion because it relates some of the technical discussions at this conference (network analysis, data mining, etc) with larger social issues.

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